Research articles for the 2020-10-26
SSRN
Penn Central Transportation Company (Penn Central), the resulting railroad company of the late 1960s merger of Pennsylvania Railroad and New York Central Railroad, filed for bankruptcy on June 21, 1970. The bankruptcy came in the middle of the 1969â"70 recession and sparked a sharp downturn in the commercial paper market. The Federal Reserve did not intervene directly in the commercial paper market but rather increased funding options available to banks via the discount window and an amendment to Regulation Q. The banks then provided funds to corporations unable to acquire them from the commercial paper market. The liquidity crisis abated within a few weeks.
arXiv
In this paper, finite element method is applied to Leland's model for numerical simulation of option pricing with transaction costs. Spatial finite element models based on P1 and/or P2 elements are formulated in combination with a Crank-Nicolson-type temporal scheme. The temporal scheme is implemented using the Rannacher approach. Examples with several sets of parameter values are presented and compared with finite difference results in the literature. Spatial-temporal mesh-size ratios are observed for controlling the stability of our method. Our results compare favorably with the finite difference results in the literature for the model.
SSRN
The high demands placed on the automotive industry makes it essential for companies to ensure consistent success in developing new products by understanding customer needs and developing products desired by the customers. It is, however, complex to work with customer needs and requirements largely depending on the fact that customers' preferences change and that customers do not know what they want in the future. To address these obstacles it is important to work effectively with requirements management and create a standardized process to ensure that customer needs are prioritized during the development process. With the increase of complexity of complex mechatronic products, it is necessary to involve multidisciplinary design teams, thus, the traditional customer requirements modeling for a single discipline team becomes difficult to be applied in a multidisciplinary team and project since team members with various disciplinary backgrounds may have different interpretations of the customers' requirements. A new synthesized multidisciplinary customer requirements (MCRs) modeling method is provided for obtaining and describing the common understanding of customer needs and more importantly transferring them into a detailed and accurate product design specifications (PDS) to interact with different team members effectively. Model-Based Systems Engineering (MBSE) is the practice of developing a set of related system models that help define, design, and document a system under development. This proposed research offers the instruction to realize the customer-driven personalized customization of the complex multidisciplinary product.
arXiv
Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.
arXiv
This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put traded option prices of the main U.S. banks contains valuable information for predicting macroeconomic conditions and economic uncertainty, and it can serve as a tool for forward-looking systemic risk monitoring.
SSRN
Following the adoption of a joint framework by euro-area countries in response to the intensifying financial crisis in October 2008, Austria enacted a package of measures including the Interbank Market Support Act (Interbankmarktstärkungsgesetz, "IBSG"). In addition to calling for the establishment of a new clearing bank to facilitate interbank lending, IBSG permitted the Austrian government to guarantee debt securities issued by other eligible institutions. Securities issued by eligible institutions with a maturity of three years or less (five years in exceptional circumstances) were eligible for guarantee. According to IBSG, the amount outstanding for all measures taken under the act could not exceed â¬75 billion. Of this, â¬4 billion was specifically allocated for guarantees of the clearing bank, Oesterreichische Clearingbank AG (OeCAG). This was subsequently reduced to â¬50 billion. The guarantee scheme established by IBSG was used by six institutions in addition to OeCAG, for a total of approximately â¬25 billion (â¬24.05 billion, CHF 325 million, and Â¥20 billion). After being extended by one year the issuance window for guarantees closed on December 31, 2010. None of the participating institutions defaulted on the guaranteed debt. The amount of guaranteed debt outstanding declined steadily, and all had matured by June 2014.
SSRN
In October 2008, euro-area countries adopted a joint framework to guide national policies combatting the effects of the global financial crisis. In Austria, this led to the enactment of a number of measures and amendments, including the Interbank Market Support Act (Interbankmarktstärkungsgesetz, or IBSG). IBSG called for the establishment of a new clearing bank to facilitate interbank lending. It also permitted the Minister of Finance to guarantee up to â¬5 billion of short-term securities issued by the clearing bank and to absorb losses of the clearing bank up to â¬4 billion. The clearing bank, Oesterreichische Clearingbank AG (OeCAG), was owned and capitalized by Austrian banks and was open to participation from all credit institutions and insurance companies. Through regular auctions, OeCAG matched available funds to demands for credit for fixed terms. Over the lifespan of the bank, 310 auctions were conducted in euros and dollars, allotting â¬22.5 billion and $1.5 billion respectively. After being extended by one year, the guarantee scheme authorized by IBSG expired on December 31, 2010, and OeCAG closed shortly thereafter. None of the government guarantees were triggered.
SSRN
One of the hallmarks of the global financial crisis of 2007-09 was the rapid evaporation of the non-deposit, wholesale funding many financial institutions had become increasingly reliant upon in the years leading up to the crisis. In the aftermath of the Lehman Brothers bankruptcy, governments became increasingly concerned about even fundamentally sound institutionsâ ability to access necessary funding. In response, beginning in October 2008, authorities across the globe began introducing guarantee programs enabling institutions to issue debt that would be backed by a guarantee from the government in exchange for a guarantee fee. While the specific details of these programs varied (sometimes widely in ways that allow for interesting comparisons), some version of this basic idea was implemented by over twenty countries. The programs saw significant use in the aggregate but were not uniformly utilized. They are generally seen as having achieved their objectives but may also in certain circumstances have had unintended consequences such as market distortions based on flawed fee structures and the crowding out of non-guaranteed debt.
arXiv
In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number of European options. We formulate the problem as a convex optimisation problem, for which we provide a PDE formulation along with its dual counterpart. Then we solve numerically the dual problem, which involves a fully non-linear Hamilton-Jacobi-Bellman equation. The method is tested by calibrating a Heston-like LSV model with simulated data and foreign exchange market data.
arXiv
This research is to assess cryptocurrencies with the conditional beta, compared with prior studies based on unconditional beta or fixed beta. It is a new approach to building a pricing model for cryptocurrencies. Therefore, we expect that the use of conditional beta will increase the explanatory ability of factors in previous pricing models. Besides, this research is also a pioneer in placing the uncertainty factor in the cryptocurrency pricing model. Earlier studies on cryptocurrency pricing have ignored this factor. However, it is a significant factor in the valuation of cryptocurrencies because uncertainty leads to investor sentiment and affects prices.
arXiv
We present a neural network (NN) approach to fit and predict implied volatility surfaces (IVSs). Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to value other financial instruments. In other words, low training losses are as important as generalization capabilities. Importantly, IVS models need to generate realistic arbitrage-free option prices, meaning that no portfolio can lead to risk-free profits. We propose an approach guaranteeing the absence of arbitrage opportunities by penalizing the loss using soft constraints. Furthermore, our method can be combined with standard IVS models in quantitative finance, thus providing a NN-based correction when such models fail at replicating observed market prices. This lets practitioners use our approach as a plug-in on top of classical methods. Empirical results show that this approach is particularly useful when only sparse or erroneous data are available. We also quantify the uncertainty of the model predictions in regions with few or no observations. We further explore how deeper NNs improve over shallower ones, as well as other properties of the network architecture. We benchmark our method against standard IVS models. By evaluating our method on both training sets, and testing sets, namely, we highlight both their capacity to reproduce observed prices and predict new ones.
arXiv
Discrete-choice life cycle models can be used to, e.g., estimate how social security reforms change employment rate. Optimal employment choices during the life course of an individual can be solved in the framework of life cycle models. This enables estimating how a social security reform influences employment rate. Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model. Solving such life cycle models requires the use of approximate methods, such as reinforced learning algorithms. We compare how well a deep reinforced learning algorithm ACKTR and dynamic programming solve a relatively simple life cycle model. We find that the average utility is almost the same in both algorithms, however, the details of the best policies found with different algorithms differ to a degree. In the baseline model representing the current Finnish social security scheme, we find that reinforced learning yields essentially as good results as dynamics programming. We then analyze a straight-forward social security reform and find that the employment changes due to the reform are almost the same. Our results suggest that reinforced learning algorithms are of significant value in analyzing complex life cycle models.
SSRN
The loan bills temporary credit facility was first implemented in May 2008, before the Global Financial Crisis had truly hit Denmark. It continued to be utilized as part of a broader effort to increase interbank lending after the collapse of Lehman Brothers in September 2008. The objective of the loan bills scheme was to facilitate lending among financial institutions. Each week, loan bills could be pledged as collateral for a seven-day loan from Denmarkâs central bank, Danmarks Nationalbank. One banking institution could borrow from another institution by issuing a loan bill, and the institution buying the bill could raise liquidity by using it as collateral for a loan from Danmarks Nationalbank. The buying institution could also count the loan bill toward its statutory liquidity, as required by section 152 of the Danish Financial Business Act. While reports show that the program may have helped improve money market liquidity for some financial institutions, loan bills were not widely used. On February 26, 2011, the credit facility for loan bills expired.
SSRN
The international financial system had been experiencing challenges for almost a year before the crisis truly manifested in Denmark during the Summer of 2008 with the sudden demise of Roskilde Bank, Denmarkâs eighth largest bank. As more Danish banks became distressed in the fall of 2008 after the collapse of Lehman Brothers, the government determined that it was necessary to intervene in the banking sector through actions such as taking over and winding up distressed banks, giving guarantees to back up the sector, and providing capital injections and liquidity support. This paper focuses on the two different types of guarantee schemes which were both implemented at the outset of the Global Financial Crisis by the Danish government in the fall of 2008 and in early 2009. The main difference between the two guarantee schemes was their breadth. While the original guarantee scheme (known in Denmark as the âGeneral State Guaranteeâ) was a blanket guaranteeâ"covering deposits in essentially all Danish banks and all unsecured debt regardless of maturity, complexity, or any other terms or conditions of the instrumentâ"the new guarantee scheme (known in Denmark as the âIndividual State Guaranteeâ) required applications by individual credit institutions and covered specific debt issuances. Both programs were heavily utilized. Under the General State Guarantee, almost all of the Danish banking industry in terms of market share was covered, with only 14 small banks out of almost 140 opting not to be covered. Similarly, by the time the issuance window of the Individual State Guarantee initially expired in December of 2010, it had guaranteed debt issuances of about 50 institutions that totaled approximately DKK 194 billion (approximately â¬26 billion).
SSRN
The loan bills temporary credit facility was first implemented in May 2008, before the Global Financial Crisis had truly hit Denmark. It continued to be utilized as part of a broader effort to increase interbank lending after the collapse of Lehman Brothers in September 2008. The objective of the loan bills scheme was to facilitate lending among financial institutions. Each week, loan bills could be pledged as collateral for a seven-day loan from Denmarkâs central bank, Danmarks Nationalbank. One banking institution could borrow from another institution by issuing a loan bill, and the institution buying the bill could raise liquidity by using it as collateral for a loan from Danmarks Nationalbank. The buying institution could also count the loan bill toward its statutory liquidity, as required by section 152 of the Danish Financial Business Act. While reports show that the program may have helped improve money market liquidity for some financial institutions, loan bills were not widely used. On February 26, 2011, the credit facility for loan bills expired.
arXiv
The general method is proposed for constructing a family of martingale measures for a wide class of evolution of risky assets. The sufficient conditions are formulated for the evolution of risky assets under which the family of equivalent martingale measures to the original measure is a non-empty set. The set of martingale measures is constructed from a set of strictly nonneg ative random variables, satisfying certain conditions. The inequalities are obtained for the non-negative random variables satisfying certain conditions. Using these inequalities, a new simple proof of optional decomposition theorem for the nonnegative super-martingale is proposed. The family of spot measures is introduced and the representation is found for them. The conditions are found under which each martingale measure is an integral over the set of spot measures. On the basis of nonlinear processes such as ARCH and GARCH, the parametric family of random processes is introduced for which the interval of non-arbitrage prices are found. The formula is obtained for the fair price of the contract with option of European type for the considered parametric processes. The parameters of the introduced random processes are estimated and the estimate is found at which the fair price of contract with option is the least.
SSRN
Sustainability in business and ESG in finance has entered the mainstream and has generated thousands of research articles that analyze its correlation with financial performance. We surveyed 1,141 primary peer-reviewed papers and 27 meta-reviews (based on ~1,400 underlying studies) published between 2015 and 2020. We reviewed three types of studies: corporate, investor, and thematic (e.g., climate change). An ordinal choice regression model showed that the type of study explains the inconsistent conclusions in prior reviews regarding whether it âpays to be good.â Our data demonstrates that higher ESG is associated with better financial performance in corporate-focused studies (58% ± 7 of studies were positive). Investor-focused studies find that ESG investing is comparable or preferable to conventional investing in 86% ± 6 of studies (with one in three studies indicating superior performance). Thematic studies were rarely represented in top finance journals, but capture aspects missed by other sustainability research. We found evidence that positive results dominate for recent climate change studies (N=59): 87% and 94% of investor- and corporate-focused studies, respectively, showed neutral/mixed to positive interpretations regarding financial performance. We also employed a Bayesian random effects model to summarize 15 recent, quantitative meta-analyses (covering studies between 1976 and 2018), which estimated a partial correlation coefficient between ESG and financial performance of 0.05 to 0.13. We conclude with six propositions. Some are practical: ESG integration as a strategy appears to perform better than screening or divestment. Others are timely: ESG investing can provide benefits during a social or economic crisis.
SSRN
Dynastic-controlled firms are led by founding family CEOs while the family owns an insignificant share of equity (defined as less than five percent). They represent 7.4% of listed firms in post-war Japan, include well-known firms such as Casio, Suzuki and Toyota, and are often grouped with widely-held firms in the literature. These firms differ in key performance measures from both traditional family firms and non-family firms, and evolve from the former as equity-financed growth dilutes the founding familyâs ownership over time. In turn, the transition from dynastic control to non-family status is driven by a diminution of strategic family resources.
SSRN
In this study, we examine the trade-offs between earnings management (both accruals and real) and covenant violations by examining how they are associated with future accounting and stock market performance. We analyze a matched-pair sample of covenant violation firms with non-violation firms that have a similar risk of a covenant violation. We have three main findings. First, our evidence indicates that covenant violations are costly events for shareholders as lenders appear to use their control rights in ways that increase the likelihood of loan repayment but impose costs for shareholders. Second, there is limited evidence indicating covenant-related accrual-earnings management activities impose significant costs on shareholders, but we find shareholders are worse off following unsuccessful real earnings management. Third, our evidence indicates that, on average, shareholders at high violation risk firms are better off when their firms successfully engage in accruals earnings management to avoid a violation compared to shareholders at firms that violate a covenant but do not manage earnings. Thus, covenant-related earnings management may be in the best interests of shareholders and is not necessarily evidence of shareholder-manager agency conflicts.
arXiv
A significant number of the non-financial firms listed at Nairobi Securities Exchange (NSE) have been experiencing declining financial performance which deter investors from investing in such firms. The lenders are also not willing to lend to such firms. As such, the firms struggle to raise funds for their operations. Prudent financing decisions can lead to financial growth of the firm. The purpose of this study is to assess the effect of Long-term debt on the financial growth of Non-financial firms listed at Nairobi Securities Exchange. Financial firms were excluded because of their specific sector characteristics and stringent regulatory framework. The study is guided by Trade-Off Theory and Theory of Growth of the Firm. Explanatory research design was adopted. The population of the study comprised of 45 non-financial firms listed at the NSE for a period of ten years from 2008 to 2017. The study conducted both descriptive statistics analysis and panel data analysis. The result indicates that Long term debt explains 21.6% and 5.16% of variation in financial growth as measured by growth in earnings per share and growth in market capitalization respectively. Long term debt positively and significantly influences financial growth measured using both growth in earnings per share and growth in market capitalization. The study recommends that, the management of non-financial firms listed at Nairobi Securities Exchange to employ financing means that can improve the earnings per share, market capitalization and enhance the value of the firm for the benefit of its stakeholders.
arXiv
Factor modeling of asset returns has been a dominant practice in investment science since the introduction of the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). The factors, which account for the systematic risk, are either specified or interpreted to be exogenous. They explain a significant portion of the risk in large portfolios. We propose a framework that asks how much of the risk, that we see in equity markets, may be explained by the asset returns themselves. To answer this question, we decompose the asset returns into an endogenous component and the remainder, and analyze the properties of the resulting risk decomposition. Statistical methods to estimate this decomposition from data are provided along with empirical tests. Our results point to the possibility that most of the risk in equity markets may be explained by a sparse network of interacting assets (or their issuing firms). This sparse network can give the appearance of a set exogenous factors where, in fact, there may be none. We illustrate our results with several case studies.
SSRN
We show that much of the market premium for the year occurs on a handful of days, identifiable well in advance, on which several of the marketâs most famous, high-media-attention firms simultaneously announce earnings after the market close. Puzzlingly, the market surges occur during the 24 hours prior to the earnings announcements, from close to close. Since there is no overlap between the price increase period and the information revelation, the high returns do not appear to represent a risk premium, and our tests seem to rule out information-leakage explanations. Deepening the puzzle, the market delivers high returns only prior to post-close earnings-announcement clusters, not in advance of clusters that occur in the pre-open period. In addition to being economically large and easily trade-able, the effect is statistically significant, and the results hold in all sub-periods in our sample. We argue that the best explanation for our findings is that of Miller (1977) as extended by Hong and Stein (2007): when over a short âattentionâ period difference of opinion combines with short-sale constraints, prices will rise as optimists buy while pessimists cannot sell.
SSRN
This paper examines the role of speculative motives in the deter- mination of commodity prices and specifically food related commodity prices. The motivation for this study is the considerable flow of funds into commodities, the widespread view that the process of financial- ization has led to greater levels of speculation and that speculation is the primary cause of regular spikes in food prices since the turn of the century. We consider two forms of speculation, a biasing influence (manipulation) and a correcting influence (speculators), relative to the fundamental price. While both forms of speculation are relevant, they are small in terms of their influence on overall prices. We do however find some evidence of an increased role being player by manipulators during the period most associated with financialization.
arXiv
We provide a survey of the Kolkata index of social inequality, focusing in particular on income inequality. Based on the observation that inequality functions (such as the Lorenz function), giving the measures of income or wealth against that of the population, to be generally nonlinear, we show that the fixed point (like Kolkata index k) of such a nonlinear function (or related, like the complementary Lorenz function) offer better measure of inequality than the average quantities (like Gini index). Indeed the Kolkata index can be viewed as a generalized Hirsch index for a normalized inequality function and gives the fraction k of the total wealth possessed by the rich (1-k) fraction of the population. We analyze the structures of the inequality indices for both continuous and discrete income distributions. We also compare the Kolkata index to some other measures like the Gini coefficient and the Pietra index. Lastly, we provide some empirical studies which illustrate the differences between the Kolkata index and the Gini coefficient.
SSRN
While studies have sought to explain the benefits of cross-listing, little attention has been paid to the role of communication between managers and investors during this process. In this paper, I investigate whether managers change communication policies around U.S. cross-listings. I document significant increases in communication when firms cross-list. I then test whether these investor communication practices around cross-listing are associated with capital market benefits. I find that cross-listed firms that communicate more with investors experience greater and longer lasting cross-listing benefits. Lastly, I explore two potential reasons that may lead managers to choose higher levels of communication: to support an increase in investor recognition and to facilitate monitoring. I find results consistent with communication increasing visibility and scrutiny, suggesting that communication functions as a supporting tool to achieve managersâ cross-listing goals.
SSRN
Following the collapse of Lehman Brothers in September 2008, the global commercial paper (CP) market began to tighten as interest rates rose and investors sought more-liquid money market securities. The Bank of Japan (BOJ) introduced several operations in late 2008 to promote liquidity in the CP market. In January 2009, the BOJ began to purchase CP and asset-backed CP outright from banks and other financial institutions. The BOJ could purchase up to ¥3 trillion of CP with a residual maturity of up to three months, among other short-term securities, via 10 purchases of up to ¥300 billion each. The BOJ limited its purchases to CP with a credit rating of a-1 or guaranteed by a company rated a-1. The BOJ would conduct purchases until March 31, 2009; it later extended the measure until December 31, 2009. As the CP market normalized, usage of the outright purchase measure decreased, with the last bid occurring in September 2009. The measure is seen as relatively successful, as interest rates on CP decreased during its first few months and the program provided needed liquidity to financial institutions during a period of market stress.
SSRN
Following the collapse of Lehman Brothers in September 2008, the global commercial paper (CP) market began to tighten as interest rates rose and investors sought more-liquid money market securities. The Bank of Japan (BOJ) introduced several measures in late 2008 to make liquidity available to nonfinancial corporations that were strapped for cash. In December 2008, the BOJ implemented special funds-supplying operations in order to provide unlimited liquidity to banks and other financial institutions so they could continue to fund nonfinancial corporations. The BOJ would provide one- to three-month loans against an equal value of eligible corporate debt at a rate equal to the target uncollateralized overnight call rate, which was consistently 0.1% throughout the operationâs lifetime. The short-term credit market gradually improved over the next year amidst consistent usage of the special operations. Approximately Â¥38 trillion in loans were provided before the special operations ceased in March 2010. The special operations are viewed as relatively successful, as they contributed to shrinking CP spreads during the first few months of implementation and promoted new issuances of CP and other corporate debt.
arXiv
We investigate a machine learning approach to option Greeks approximation based on Gaussian process (GP) surrogates. The method takes in noisily observed option prices, fits a nonparametric input-output map and then analytically differentiates the latter to obtain the various price sensitivities. Our motivation is to compute Greeks in cases where direct computation is expensive, such as in local volatility models, or can only ever be done approximately. We provide a detailed analysis of numerous aspects of GP surrogates, including choice of kernel family, simulation design, choice of trend function and impact of noise.
We further discuss the application to Delta hedging, including a new Lemma that relates quality of the Delta approximation to discrete-time hedging loss. Results are illustrated with two extensive case studies that consider estimation of Delta, Theta and Gamma and benchmark approximation quality and uncertainty quantification using a variety of statistical metrics. Among our key take-aways are the recommendation to use Matern kernels, the benefit of including virtual training points to capture boundary conditions, and the significant loss of fidelity when training on stock-path-based datasets.
arXiv
This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expected to be multi-dimensional. New mathematical challenges arise as the standard dimension reduction cannot be applied, and the convexity of the value function and Isaacs condition may not hold in our ruin probability minimization problem with drift uncertainty. We propose to employ the stochastic Perron's method to characterize the value function as the unique viscosity solution to the associated Hamilton Jacobi Bellman (HJB) equation without resorting to the proof of dynamic programming principle. The required comparison principle is also established in our setting to close the loop of stochastic Perron's method.
SSRN
The literature has used small samples to show that fast trading or low latency trading (LLT) improves efficiency at extremely high frequencies. However, it is not clear whether LLT driven high frequency improvements in efficiency can impact corporate decision making and investor risk sharing or hedging, which are low frequency processes. This paper uses a comprehensive cross-sectional and time-series sample to provide evidence that LLT enhances efficiency around earnings announcements. Low latency traders trade aggressively at the time of the earnings announcements, such that the information in earnings surprises is quickly incorporated into prices and the post-announcement drift is reduced.
arXiv
This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and non-semimartingale market framework with unbounded random coefficients, the optimal portfolio strategy is expressed by means of a Riccati backward stochastic differential equation (BSDE). In the case of affine Volterra models, we derive explicit solutions to this BSDE in terms of multi-dimensional Riccati-Volterra equations. This framework includes multivariate rough Heston models and extends the results of \cite{han2019mean}. In the quadratic case, we obtain new analytic formulae for the the Riccati BSDE and we establish their link with infinite dimensional Riccati equations. This covers rough Stein-Stein and Wishart type covariance models. Numerical results on a two dimensional rough Stein-Stein model illustrate the impact of rough volatilities and stochastic correlations on the optimal Markowitz strategy. In particular for positively correlated assets, we find that the optimal strategy in our model is a `buy rough sell smooth' one.
arXiv
In this paper, we study the mean-variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.
SSRN
The effect of Al2O3 nano-particles on the performance of heat pump to improve its operational efficiency was presented in this paper. In the proposed method the heat pump charged with R600a inclusive with 0.06 % vol. of Al2O3 and used as a nano-refrigerant. Three different nano-particles size 20 nm, 40 nm and 50 nm of Al2O3 have been used for the preparation of nano-lubricant in the proposed mechanism. The mechanism for improvement includes simulations modeling the heat pump components such as compressor, evaporator, condenser and an expansion valve by computer of the heat pump system by using commercial MATLAB. The results showed that the addition of nano-particles to the refrigerant will improve its characteristics of refrigeration system heat transfer and thermal properties. Also, it showed that the using nano-refrigerant in refrigeration system will work normally at all conditions. The mechanism for improvement results found that the heat pump coefficient of performance increased by 19.1%, the power consumption reduced by 21.8 % when using a mineral oil with 20 nm nano-particles size of Al2O3 instead of the conventional mineral oil. Finally, the refrigeration effect increased and work of compressor decreased by using a small nano-particles size of Al2O3.
SSRN
This paper examines banksâ option to adopt the capital transitional arrangement (CTA) set out by the Basel Committee on Banking Supervision in response to the introduction of International Financial Reporting Standards 9 (IFRS 9), which requires the use of an expected credit loss model instead of an incurred loss model to estimate the impairment of financial assets. Using a sample of European publicly listed banks from 2016 to 2019, we find that bank CTA adoption choice is associated with neutral factors captured by bank-specific fundamental factors and potential opportunistic factors related to regulatory constraints implied by the application of IFRS 9. We further examine the association between the CTA adoption choice and bank risk taking. Our results show that banks that adopted the CTA (CTA adopters) decreased their exposure to systematic risk following the CTA adoption compared to the control group of CTA non-adopters. We find that such a relationship varies with the power of the banking authority, being more significant when the banking authority holds more power. Our study is the first academic work to address banksâ voluntary choice to adopt the CTA policy under the mandatory application of IFRS 9.
arXiv
Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN are concatenated with historical records and then fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge, as well as learn data-driven relationship.
arXiv
This paper studies the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. This problem arises naturally when considering many stress tests and appears in the calculation of the interest rate module of the standard formula for the SCR. We obtain theoretical convergence results that complements the recent work of Giles and Goda and gives some additional tractability through a parameter that somehow describes regularity properties around the maximum. We then apply the MLMC estimator to the calculation of the SCR at future dates with the standard formula for an ALM savings business on life insurance. We compare it with estimators obtained with Least Square Monte-Carlo or Neural Networks. We find that the MLMC estimator is computationally more efficient and has the main advantage to avoid regression issues, which is particularly significant in the context of projection of a balance sheet by an insurer due to the path dependency. Last, we discuss the potentiality of this numerical method and analyze in particular the effect of the portfolio allocation on the SCR at future~dates.
arXiv
We propose a robust risk measurement approach that minimizes the expectation of overestimation plus underestimation costs. We consider uncertainty by taking the supremum over a collection of probability measures, relating our approach to dual sets in the representation of coherent risk measures. We provide results that guarantee the existence of a solution and explore the properties of minimizer and minimum as risk and deviation measures, respectively. An empirical illustration is carried out to demonstrate the use of our approach in capital determination.
SSRN
The risk of financial crisis fuelled by 'inflated ratings' is recognised but underestimated. Firstly, the manufacturing of inflated ratings is not fully within the control of credit rating agencies and is linked to arbitrage, which operates unless all avenues are sealed off. Secondly, the arbitrage is poorly understood for inside certain well-known but fake arbitrages there is a `hidden' real one. Ratings arbitrage (investing in credits with `inflated' ratings) is not an economic arbitrage but with Basel II-type capital requirements linked to credit ratings it became one for e.g. banks, because it creates higher financial leverage, a shareholder wealth arbitrage due to the government put, provided market inconsistent ratings exist, which is bound to be the case and of which inflated ratings is a subset. Thus, a Trojan horse was drafted into solvency regulation. Arbitrage emanating from this regulatory design-flaw is prone to spiral into financial crises and was plausibly the ultimate cause of the 2008 Financial Crisis.
arXiv
We consider a semilinear equation linked to the finite horizon consumption - investment problem under the stochastic factor framework and we prove it admits a classical solution and provide all obligatory estimates to successfully apply a verification reasoning. The paper covers the standard time additive utility, as well as the recursive utility framework. We extend existing results by considering more general factor dynamics including a non-trivial diffusion part and a stochastic correlation between assets and factors. In addition, this is the first paper which compromises many other optimization problems in finance, for example those related to the indifference pricing or the quadratic hedging problem. The extension of the result to the stochastic differential utility and robust portfolio optimization is provided as well. The essence of our paper lays in using improved stochastic methods to prove gradient estimates for suitable HJB equations with restricted control space.
arXiv
Mining blocks on a blockchain equipped with a proof of work consensus protocol is well-known to be resource-consuming. A miner bears the operational cost, mainly electricity consumption and IT gear, of mining, and is compensated by a capital gain when a block is discovered. This paper aims at quantifying the profitability of mining when the possible event of ruin is also considered. This is done by formulating a tractable stochastic model and using tools from applied probability and analysis, including the explicit solution of a certain type of advanced functional differential equation. The expected profit at a future time point is determined for the situation when the miner follows the protocol as well as when he/she withholds blocks. The obtained explicit expressions allow to analyze the sensitivity with respect to the different model ingredients and to identify conditions under which selfish mining is a strategic advantage.
arXiv
\noindent We address the issue of market making on electronic markets when taking into account the clustering and long memory properties of market order flows. We consider a market model with one market maker and order flows driven by general Hawkes processes. We formulate the market maker's objective as a stochastic control problem. We characterize an optimal control by proving existence and uniqueness of a viscosity solution to the associated Hamilton-Jacobi-Bellman equation. Finally we propose a fully consistent numerical method allowing to implement this optimal strategy in practice.
arXiv
The BS equations with fractional order two asset price models give a better prediction of options pricing in the monetary market. In this paper, the changed form of BS-condition with two asset price models dependent on the Liovelle-Caputo derivative for good predictions of options prices are utilized. The analytical solution is demonstrated in form of convergent infinite series and obtained by the properties of Samudu Transform.
arXiv
Carpooling is a system in which drivers accept to add some limited detours to their habitual journeys to pick-up and drop-off other riders. Most research and operating platforms present carpooling as an alternative to fixed schedule transit and only very little work has attempted to integrate it with fixed-schedule mass transit. The aim of this paper is to showcase the benefits of such integration, under the philosophy of Mobility as a Service (MaaS), in a daily commuting scenario. We present an integrated mass transit plus carpooling system that, by design, constructs multimodal trips, including transit and carpooling legs. To this aim, the system generates vehicle detours in order to serve transit stations. We evaluate the performance of this system via simulation. We compare the ``Current'' System, where carpooling is an alternative to transit, to our ``Integrated'' System, where carpooling and transit are integrated in a single system. We show that, by doing this, the transportation accessibility greatly increases: about 40\% less users remain without feasible travel options and the overall travel time decreases by about 10\%. We achieve this by requiring relatively small driver detours, thanks to a better utilization vehicle routes, with drivers' vehicles driving on average with more riders on board. The simulation code is available open source.
arXiv
The field of portfolio selection is an active research topic, which combines elements and methodologies from various fields, such as optimization, decision analysis, risk management, data science, forecasting, etc. The modeling and treatment of deep uncertainties for future asset returns is a major issue for the success of analytical portfolio selection models. Recently, robust optimization (RO) models have attracted a lot of interest in this area. RO provides a computationally tractable framework for portfolio optimization based on relatively general assumptions on the probability distributions of the uncertain risk parameters. Thus, RO extends the framework of traditional linear and non-linear models (e.g., the well-known mean-variance model), incorporating uncertainty through a formal and analytical approach into the modeling process. Robust counterparts of existing models can be considered as worst-case re-formulations as far as deviations of the uncertain parameters from their nominal values are concerned. Although several RO models have been proposed in the literature focusing on various risk measures and different types of uncertainty sets about asset returns, analytical empirical assessments of their performance have not been performed in a comprehensive manner. The objective of this study is to fill in this gap in the literature. More specifically, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005-2016.
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In this essay, which formed the basis for the luncheon keynote speech at the Rethinking Stewardship online conference presented by the Ira M. Millstein Center for Global Markets and Corporate Ownership at Columbia Law School and ECGI, the European Corporate Governance Institute, the essential, but not sufficient, role of regulation to promote more effective stewardship by institutional investors is discussed. To frame specific policy recommendations that align the responsibilities of institutional investors with the best interests of their human investors in sustainable wealth creation, environmental responsibility, the respectful treatment of stakeholders, and, in particular, the fair pay and treatment of workers, the essay: 1) explains how the corporate governance system we now have is fundamentally different than the system we had when the regulatory structures governing institutional investors were put in place; 2)identifies the suboptimal results that have ensued by increasing the power of institutional investors, and thus the stock market, over public companies, while diminishing the protections for other stakeholders and society generally; 3) discusses why leaving needed change to the industry itself is not an adequate answer; and 4) sets forth a series of specific, measured public policy changes for mutual funds, pension funds, and hedge funds. In sum, the essay explains and addresses the reality that companies that make products and deliver services cannot focus more on sustainable profitability, respectful treatment of stakeholders, and social responsibility than the powerful investors that control them permit. Like any powerful economic interest, institutional investors should be expected to be responsible citizens and faithful fiduciaries.
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Financial systems worldwide are increasingly experiencing the mounting pressure of the technology based financial innovations. Some of these developments are generating alternative financial structures, existing parallelly to the âoldâ ones whereas some others are simply replacing the âoldâ ones. Alternative inter-mediating institutions are gaining ground vis a vis encumbents, relying on their technological and market supremacy. The space for traditional financial inter-mediation becomes increasingly under the competition of the new solutions. Some technological solutions provide additionally for the partial or entire disinter-mediation of the financial services and thus removing some existing transaction costs and matching directly economic agents. Digitization and data-fication, coupled with artificial intelligence, are offering new immense operational opportunities and economic benefits. On the other hand they are also the source of new risks to the financial and economic systems, financial stability, national security and consumer well-being, which need to be properly addressed. We review in this paper principal components of the current stream of technology based financial innovations, its main drivers, as well as discuss major strategic issues and impacts that we are facing in this area.
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The 2007â"09 financial crisis reached a critical stage in March 2008. Amid falling house prices and downgrades of mortgage-related securities, financial markets became severely disrupted. The Federal Reserveâ"the US central bankâ"became increasingly concerned about the inability of the 20 primary dealers, including the five largest US investment banks, to fund themselves in short-term funding markets, such as the repurchase agreement market, then estimated at $10 trillion. In response, the Fed created several emergency lending facilities to restore market liquidity that required the Fed to invoke Section 13(3) of the Federal Reserve Act. The Term Securities Lending Facility authorized the Federal Reserve Bank of New York to lend to primary dealers up to $200 billion of highly liquid US Treasuries against collateral that was particularly illiquid at the time. Eligible collateral initially included triple-A private-label mortgage-backed securities but was later broadened. In July 2008, an additional $50 billion was allocated for a TSLF Options Program. The TSLF operated between March 27, 2008, and February 1, 2010. Usage peaked at $236 billion in October 2008. Overall, 18 of the 20 primary dealers participated and the Fed collected $781 million in fees.
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In mid-September 2008, following the bankruptcy of Lehman Brothers, money market mutual funds (MMMFs) began to experience run-like redemption requests after a large fund âbroke the buck,â owing to a large position in Lehman commercial paper (CP). Funds, which as a group were the largest investors in CP, retreated from CP, including asset-backed commercial paper (ABCP). Funds also sought to raise cash to meet redemptions by selling assets but were reluctant to sell ABCP into a depressed market. As the CP and ABCP markets seized up, it became difficult for issuers to place new paper, and concern grew about possible contagion of the broader financial markets and economy. As a result, on September 19, 2008, the Federal Reserve (the Fed) announced the Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility (AMLF), pursuant to which the Fed made discount window loans to depository institutions and broker dealers to purchase high-quality ABCP from eligible MMMFs, providing cash for redemptions. Utilization of the AMLF peaked in October 2008, when outstanding loans totaled $152 billion, or the equivalent of 21% of all outstanding ABCP. As markets improved, utilization of the AMLF waned, and the program expired on February 1, 2010, without the government experiencing any losses. The AMLF is credited with having helped to stabilize MMMF redemptions, to restore liquidity to the ABCP market, and to have fostered liquidity in the money markets in general, but its impact must be considered in light of other coexistent programs.
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The Australian Guarantee Scheme for Large Deposits and Wholesale Funding was developed in 2008 shortly after the failure of Lehman Brothers. It was designed to foster financial-system stability and confidence and to help depository institutions continue to access funding during a period of volatility. In addition to a guarantee for large deposits, the scheme allowed institutions to apply for a government guarantee for newly issued wholesale liabilities with maturities of up to five years; in return, the institutions paid the government a monthly fee based on their credit rating and the value of the debt guaranteed. The entire Guarantee Scheme became operational in November 2008 and closed to new issuance in March 2010, by which time 16 institutions had issued about A$166 billion ($108.7 billion) of guaranteed securities. The Guarantee Schemeâs wholesale funding component formally ended in October 2015, a few months after the final guaranteed instrument matured. It incurred no losses, no claims were made against it, and it earned A$4.5 billion ($2.95 billion) in fees for the support provided.
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Much like other developed economies during the global financial crisis, Belgium faced sub-stantial systemic stress to its large and heavily concentrated financial system. To combat these mounting pressures, the Belgian government launched a wide-ranging, opt-in state debt guarantee program in a concerted effort to instill confidence and stymie the fear of runs in its financial sector. The debt guarantee scheme, pursuant to which eligible institutions could issue government-guaranteed debt, was originally put into place on October 15, 2008, and retroactively covered liabilities entered into from October 9, 2008, to October 31, 2009, with a maximum maturity of three years. It provided significant discretionary authority to the Minister of Finance, such as the ability to add additional conditions and to decline any bank from participating in the scheme. Eligibility was determined on a case by case basis. Fees and issuance thresholds were determined in the same way prior to two April 14, 2009, Royal Decrees, which homogenized fees and expanded the pool of eligible institutions. Bel-gium was also a key member in a number of high profile bank rescues, such as that of Dexia in conjunction with France and Luxembourg. Much of the structure of the guarantee scheme was initially based on the ad hoc scheme that the three nations devised for Dexia earlier in October 2008. The state guarantee scheme expired after no banks had made use of it by Oc-tober 31, 2010, the last day for banks to issue guaranteed debt after amendments to the is-suance window and maturity horizon.
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Following a meeting of Group of Seven leaders in October 2008, the Canadian Minister of Finance announced the creation of a new Canadian Lenders Assurance Facility (CLAF). The facility enabled federally regulated deposit-taking financial institutions to access government insurance of up to three years on newly issued senior unsecured wholesale debt. This mirrored similar programs in other countries to ensure that Canadian financial institutions were not competitively disadvantaged in the wholesale debt market at a time when most developed countries were guaranteeing their banksâ debt. This competitive disadvantage never materialized, and the facility was allowed to expire on December 31, 2009, without ever being used.
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In mid-September 2008, prime money market mutual funds (MMMFs) began experiencing run-like redemption requests sparked by one fund that had âbroken the buckâ because of large exposure to Lehman Brothers commercial paper (CP). As a result, MMMFs, which are significant investors in CP, became reluctant to hold CP. Within a week, outstanding CP had been reduced by roughly $300 billion. The CP market experienced severe shortening of maturities and increased rates, making it difficult for issuers to place new paper. When government efforts to assist the MMMFs did not resolve the stresses in the CP market, the Federal Reserve announced, on October 7, 2008, the Commercial Paper Funding Facility (CPFF), which sought to backstop the CP market and revive term lending.The CPFF (through a special purpose vehicle) purchased highly rated US dollarâ"denominated three-month unsecured and asset-backed commercial paper (ABCP) from eligible US issuers. Purchases were funded with loans from the Federal Reserve Bank of New York (FRBNY). The CPFF was highly utilized in its first weeks, purchasing the overwhelming majority of new term CP; its usage then waned as market conditions improved. At its highest level, in January 2009, the CPFF held $350 billionâ"20% of all outstanding CP. The CPFF expired on February 1, 2010, with all loans paid in full. The program accumulated approximately $5 billion in earnings that was paid to the FRBNY. The program is credited with backstopping the market, providing a rollover option for maturing paper, and providing much needed year-end financing. Its role in helping to revive the term-lending market, however, has been debated, but there is evidence that it did help increase lending between CPFF participants and their relationships with nonfinancial corporate borrowers.
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Following the collapse of Lehman Brothers in September of 2008, banks faced extreme difficulty in issuing new debt and finding affordable sources of funds due to heightened fears over counterparty solvency and liquidity risk. By the end of September, the TED spread had spiked to 464 basis points, and issuance of commercial paper fell 88%. On October 14th, to boost confidence and lower short-term financing costs, the Federal Deposit Insurance Corporation announced the Debt Guarantee Program (DGP) as part of the Temporary Liquidity Guarantee Program (TLGP). Under the DGP, the FDIC guaranteed in full a limited amount of senior unsecured debt newly issued by insured depository institutions and certain bank holding companies that did not opt out of the program. Other affiliates of insured depositories were also able to apply to the FDIC for eligibility on a case-by-case basis. If an institution defaulted on a guaranteed bond, the FDIC would cover all payments on interest and principal. In exchange for receiving the guarantee, institutions paid a fee based on the bondâs maturity. The issuance window was set to expire on June 30, 2009, but was extended to October 31, 2009. An additional Emergency Guarantee facility, created at the time of extension, had an issuance window that expired on April 30, 2010, but was never used. Over the course of the program, the 122 participating institutions raised over $600 billion in guaranteed debt. The FDIC paid out about $153 million due to defaults from six institutions, and collected $10.2 billion in fees.
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In July 2009, the European Central Bank introduced a nonstandard measure to revitalize the European covered bond market, which at the time financed about one-fifth of mortgages in Europe. The market struggled after the collapse of Lehman Brothers as the global financial crisis intensified in 2008. Over the course of the program, which lasted 12 months, European central banks, collectively known as âthe Eurosystem,â conducted direct purchases in both primary and secondary markets to a total of â¬60 billion of covered bonds. The Eurosystem held the purchased covered bonds until maturity and made them eligible for lending to counterparties as of March 2010. Some evaluations consider the first CBPP a success, as the covered bond market began to function normally after the programâs implementation. However, the CBPP operated at the same time as other crisis-combatting programs within both the Eurosystem and individual member countries, and its results occurred during a general improvement in conditions.In November 2011, the European Central Bank introduced the second iteration of its covered bond purchase programs to stimulate funding to credit institutions and facilitate lending at the onset of the sovereign debt crisis. The program ran for one year and fell far short of its targeted â¬40 billion in purchases; at its completion, Eurosystem central banks had purchased â¬16.4 billion of covered bonds. It is difficult to holistically evaluate the program; while CBPP2 generally had a positive impact on the covered bond market, it was much less effective than CBPP1.
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The Eurozone struggled during the escalation of the sovereign debt crisis in 2010. In order to aid malfunctioning securities markets, restore liquidity, and enable proper functioning of the monetary policy transmission mechanism, the European Central Bank (ECB) instituted the Securities Markets Programme (SMP) on May 9, 2010. This program enabled Eurosystem central banks to purchase securities from entities in Greece, Ireland, Portugal, Italy, and Spain. The program ended on September 6, 2012, and evaluations of its effectiveness are mixed.
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The announcement of the three-year Long-Term Refinancing Operations (LTROs) by the European Central Bank (ECB) on December 8, 2011, signaled the beginning of the largest ECB market liquidity programs to date. Continued and increasing liquidity-related pressures in the form of ballooning financial market credit default swap (CDS) spreads, Euro-area volatility, and interbank lending rates prompted a much more forceful ECB response than what had been done previously. The LTROs, using a repurchase (repo) agreement auction mechanism, allowed any Eurozone financial institution to tap essentially unlimited funding at a fixed rate of just 1%. Because the three-year LTROs were so similar to their shorter-maturity counterparts, the types of eligible collateral were almost identical, though the three-year operations were slightly less strict with the types of asset-backed securities (ABS), loans, and debts that could be pledged. The first operation, conducted on December 22, 2011, saw 523 banks draw â¬489.2 billion in funding, and the second operation, finalized on February 29, 2012, saw 800 banks draw â¬529.5 billion. Much of the liquidity, rather than being put into private credit markets, was placed at the ECB deposit facility to supplement the interbank lending market. Banks that were more vulnerable to a credit crunch, often located in peripheral countries such as Spain and Italy, tended to use the facility more and also drove the increase in the supply of private credit. Less at-risk institutions tended to engage in âreach-for-yieldâ strategies with debt from riskier sovereigns. Post-crisis evaluations were mixed, but analysts tend to agree that the facilities helped ease the initial shock in the Euro-area money market and reduce the impact of the credit crunch on the broader economy.
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As mortgage defaults and foreclosures continued to climb, the severe strains that started to plague credit markets in the middle of 2007 worsened further. Losses on housing-related securities and derivative instruments continued to climb, causing substantial damage to the balance sheets of large financial institutions that had levered up on these same securities. As their positions worsened, banks found it increasingly difficult to attract funding that wasnât priced at exorbitantly high rates or for very short terms. Term funding markets, specifically those that centered on agency mortgage-backed securities (MBS), quickly dried up as fears of illiquidity and even insolvency spread. To remedy these concerns, the Federal Reserve announced a program called the Single-Tranche Term Repurchase Agreements, which auctioned off repurchase agreements (repos) to primary Dealers every week. This provided a critical source of funding to these institutions, which, at the time, could not access other avenues of funding, such as the discount window. The repos were short term, priced at market rates, and matured 28 days after the settlement date. Of the 20 institutions categorized as primary dealers at the beginning of 2008, 19 participated in the program, which had auctions running from March 7, 2008, to December 31, 2008. Usage peaked at, but never exceeded, $80 billion per month, though the Fed said in its initial press release that the programâs size could have gone up to $100 billion. While the program was smaller compared to other market liquidity initiatives, ST OMO operated at capacity for most of its duration, and spreads between agency MBS repo and Treasury repo rates fell dramatically toward the end of the issuance window.
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The S&P 500 lost 10% the week ending Friday, October 16, 1987, and lost an additional 20% the following Monday, October 19, 1987. The date would be remembered as Black Monday. The Federal Reserve (the Fed) responded to the crash in four distinct ways: (1) issuing a public statement promising to provide liquidity, as needed, âto support the economic and financial systemâ; (2) providing support to the Treasury securities market by injecting in-high-demand maturities into the market via reverse repurchase agreements; (3) allowing the federal funds rate to fall from 7.5% to 7.0% and below; and (4) intervening directly to allow the rescue of the largest options clearing firm in Chicago.
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As the global financial crisis raged in October 2008, its severe impact on global credit markets impelled governments to enact stabilization measures to calm and protect their domestic economies. The Republic of Finland, though not directly affected, designed preemptive interventions to mitigate disruption to its financial system. Among them was the Guarantee Scheme for Bank Funding in Finland (the Guarantee Scheme), announced on October 22, 2008, and implemented on February 12, 2009, which aimed to support banks and mortgage institutions with their short- and medium-term financing needs. Under the program, the Finnish State Treasury made up to â¬50 billion available to guarantee new debt issued by any Finnish deposit bank or mortgage institution considered to be solvent by authorities. Initially, types of debt covered by the Guarantee Scheme included new certificates of deposit, unsecured bonds, and other non-subordinated instruments with maturities of greater than 90 days but less than three years. Covered bonds with maturities of up to five years were also eligible. Although the Guarantee Scheme was amended and prolonged twice, it was never utilized and concluded with the expiration of the issuance window on June 30, 2010.
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In mid-September 2008, money market mutual funds (MMMFs) began to experience run-like redemption requests after the Reserve Primary Fund âbroke the buck.â As a result, MMMFs became reluctant to roll over or invest in commercial paper (CP) and faced the prospect of selling asset-backed commercial paper (ABCP) they held into a declining market to raise cash. The money markets quickly became negatively impacted, and on October 21, 2008, the Fed announced the Money Market Investor Funding Facility (MMIFF), which would loan funds to a series of special purpose vehicles (SPVs) established by the private sector. The SPVs would use the funds (and proceeds from ABCP that they issued) to purchase eligible US dollarâ"denominated money market instruments (certificates of deposit, bank notes, and CP) from eligible money market investors, a group originally limited to MMMFs. The Fed authorized up to $540 billion for the MMIFF, which would have facilitated the purchase of $600 billion of assets. No fund accessed the facility, however, and it was closed on October 30, 2009. Although not utilized, it cannot conclusively be said whether the availability of the MMIFF had an impact on the market. Any such impact is difficult to isolate given the coexistence of other government programs aimed at addressing similar stresses.
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On March 16, 2008, the Federal Reserve created the Primary Dealer Credit Facility, or PDCF, to provide overnight funding to primary dealers in the tri-party repurchase agreement (repo) market, where lenders had become increasingly risk averse. Loans were fully secured by (initially) investment-grade securities and offered at the primary credit rate by the Federal Reserve Bank of New York. The eligible collateral was significantly expanded in September 2008, after rumors of Lehman Brothers potentially filing for bankruptcy, to include all of the types of instruments that could be pledged at the two major tri-party repo clearing banks. The PDCF was a means for the Federal Reserve to provide lender-of-last-resort funding directly to primary dealers, including the five largest US investment banks, which it could not do before. The program also served to buy time for dealers to find other methods of financing. During its tenure, the facility was actively used, with the highest daily amount of outstanding loans at $130 billion, which occurred in September 2008. Overall, 18 of the 20 primary dealers participated in the program, although, unlike the other major program targeting primary dealers, the Term Securities Lending Facility, most participation was by US firms. The facility was closed on February 1, 2010. All loans extended under this facility were repaid in full, with $593 million in interest and fees collected. It has been credited, with other similar programs, with relieving the severe liquidity stresses on primary dealers during the height of the crisis.
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On March 23, 2009, the U.S. Treasury, in conjunction with the Federal Reserve (Fed) and the Federal Deposit Insurance Corporation (FDIC), announced the Public-Private Investment Program (PPIP). PPIP consisted of two complementary programs designed to foster liquidity in the market for certain mortgage-related assets: The Legacy Loans Program and the Legacy Securities Program. This case study discusses the design and implementation of the Legacy Loans Program. Under this program, the FDIC and Treasury attempted to create public-private investment partnerships thatâ"using a combination of private equity, Treasury equity, and FDIC-guaranteed debtâ"would purchase legacy mortgage loans from U.S. banks by way of FDIC-supervised auctions of them. Despite months of FDIC attempts to develop the program, it was never implemented. The program was criticized by many in the media and academic community for favoring the interests of private investors over those of taxpayers; government officials, however, have contended that these concerns were unfounded.
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On March 23, 2009, the U.S. Treasury, in conjunction with the Federal Reserve (Fed) and the Federal Deposit Insurance Corporation (FDIC), announced the Public-Private Investment Program (PPIP). PPIP consisted of two complementary programs designed to foster liquidity in the market for certain mortgage-related assets: The Legacy Loans Program and the Legacy Securities Program. This case study discusses the design and implementation of the Legacy Securities Program. Under this program, the Treasury formed an investment partnership with nine private sector firms it selected at the conclusion of a months-long application process. Using a combination of private equity and debt and equity from the Treasury, nine public-private investment funds (PPIFs) invested $24.9 billion in non-agency residential and commercial mortgage-backed securities (MBS), netting the government a positive return of $3.9 billion on its investment. While the program received mixed reviews from scholars, the private sector, and former government officials, it is seen as having contributed somewhat to the recovery of the secondary mortgage market.
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The year 2019 was one of the luckiest periods in the history of Russiaâs stock market. On a 10-year time horizon (2010â"2019), the geometric mean return on investment in Russian ruble-denominated stocks amounted to 8.3% per annum, which was below the corresponding indices of only a few markets like the USA, the Scandinavian economies, Japan, India, the Philippines, and Argentina. The average annual return on investment in Russian stocks denominated in US dollars stood at 0.7%, which was significantly below the ruble-denominated return on investment in those same stocks due to the ruble weakening in the post-crisis period.
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In the fall of 2008, the securitization market, which was the major provider of credit for consumers and small businesses, came to a near halt. Investors in this market abandoned not only the residential mortgage-backed securities that triggered the financial crisis but also consumer and business asset-backed securities (ABS), which had a long track record of strong performance, and commercial mortgage-backed securities (CMBS). Also, the unprecedented widening of spreads for these securities rendered new issuance uneconomical, and the shutdown of the securitization market threatened to exacerbate the downturn in the economy.On November 25, 2008, the Federal Reserve (the Fed) thus announced the Term Asset-Backed Securities Loan Facility (TALF). TALF was launched on March 3, 2009, to help stabilize funding markets for issuers in the securitization market. The TALF extended term loans, collateralized by the securities, to buyers of certain high-quality asset-backed securities. By reopening the ABS market, the Fed intended to ultimately support the provision of credit to consumers and small businesses. Instead of directly participating in the securitization market, the Fed encouraged private investors to do so by providing them with liquidity and only took risk in the loss of the value of ABS.In aggregate, the Fed issued 2,152 loans, totaling $71.1 billion. The volume of outstanding loans peaked in March 2010 at $48.2 billion. Loans secured by nonmortgage ABS totaled $59 billion, and loans secured by legacy CMBS totaled $12 billion. The original expiration date for the TALF of December 31, 2009, was extended to March 31, 2010, for loans against ABS and legacy CMBS, and until June 30, 2010, for loans against newly issued CMBS. On October 29, 2014, the final outstanding TALF loan was repaid in full, and in the following month, a total of $745.7 million in accumulated fees and income was paid to the Treasury (90%) and the Federal Reserve Bank of New York (10%).
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On March 5, 2009, in the wake of the fallout from the Global Financial Crisis, the Monetary Policy Committee of the Bank of England announced a new, unconventional policy measure: quantitative easing. The MPC determined that simply cutting the Bank Rate in the face of a recession would not be enough to boost spending and increase inflation to meet the Bankâs goal of a 2% CPI-inflation target in the medium term. Rather, over the course of the next year, the Bank purchased £200 billion of assetsâ"primarily giltsâ"in reverse auctions through a newly created Asset Purchase Program. After just under one year of purchases and a brief hiatus, the Bank revisited the program in 2011 and purchased an additional £175 billion of assets, bringing the total to £375 billion. For the most part, studies hold that these two episodes of purchasingâ"QE1 and QE2â"were successful, as gilt and other asset prices increased and the program had an impact on inflation and GDP. However, it is hard to conclusively assert the impact of QE on the economy, as the unconventional policy was implemented concurrently with other measures in the United Kingdom and around the world.
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The key structures of housing finance in the UK in the years leading up to the global financial crisis of 2007-09 consisted of retail deposits, secondary market funding and wholesale interbank lending. Although retail deposits were the major funder of UK mortgages, secondary market funding, which included covered bonds and residential mortgage-backed securities (RMBS), accounted for 31% of UK mortgage lending in 2006. In 2007, the collapse of the U.S. subprime mortgage market triggered a financial shock, and the shock quickly traveled beyond national borders. Regardless of differences in the UK mortgage market, investorsâ concern over the prospects of the U.S. housing market influenced their perception of UK mortgage-backed assets. And with the UK RMBS market substantially reliant on overseas investors, their concern contributed to a downturn in the UK market.In a November 2008 report on mortgage finance by Sir James Crosby, it was argued that â[w]ithout intervention, the market in mortgage-backed securities won't return any time soon â¦â and that âthe inability to refinance existing mortgage-backed funding and the continuing pressures in wholesale funding markets ⦠[were] really hitting the banksâ capacity to make new loans â¦â. In response to this report, HM Treasury announced a £50 billion guarantee scheme for asset-backed securities (the Scheme) on January 19, 2009 and launched this Scheme on April 22, 2009.The Scheme, in which HM Treasury provided a guarantee for eligible newly issued RMBS, represented an extension of the 2008 Credit Guarantee Scheme for unsecured debt issuance by UK incorporated banks and building societies. The Scheme aimed to support residential mortgage lending in the UK economy. The Scheme closed on December 31, 2009, without having been used.
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In January 2009, following continued increases in commercial paper spreads, Her Majestyâs Treasury authorized the Bank of England to begin purchasing commercial paper under the Asset Purchase Facility (APF) in order to maintain UK-based corporationsâ access to short-term financing. Under the Commercial Paper Facility (CPF), the Bank purchased commercial paper from both primary issuers and secondary holders at a rate that was favorable to issuers during the credit crunch but that would no longer be attractive once the markets recovered. By serving as a backstop, or market maker of last resort (MMLR), the Bank helped to restore liquidity to corporate credit markets. By February 2010, almost all issuers could find more favorable spreads in the market, and in November 2010, the Bank gave 12 monthsâ notice of the facilityâs withdrawal. At peak utilization, the Bank purchased £2.4 billion of commercial paper in late April 2009.
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In late 2008, at the height of the Global Financial Crisis, increased liquidity premia and risk aversion in the secondary market hindered companiesâ ability to issue corporate bonds. In response, in January 2009, Her Majestyâs Treasury authorized the Bank of England to establish a facility to purchase commercial bonds through the Asset Purchase Facility. In March 2009, the Bank of England published details on the Corporate Bond Secondary Market Scheme, in conjunction with its quantitative easing program. Under the scheme, the Bank acted as a market maker of last resort in the secondary bond market, making regular purchases of a wide range of high quality, sterling-denominated corporate bonds. In doing so, the Bank hoped to improve the functioning of the secondary market, thereby removing barriers to credit for UK companies. The purchase program was augmented in 2010, when the Bank began to sell corporate bonds through similar frequent auctions. Purchases through the scheme peaked in the second quarter of 2010, at £1.6 billion. Citing improved liquidity in the secondary market and the related decline in usage of the scheme, the Bank began phasing out the Corporate Bond Secondary Market Scheme in 2013, and it was officially closed in August 2016.
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The September 15, 2008, bankruptcy of Lehman Brothers resulted in a collapse of wholesale funding markets that threatened the ability of UK financial institutions to continue funding themselves. By the end of the month, two leading UK banksâ"HBOS and Bradford & Bingleyâ"had to be rescued, and there was a real risk that the entire financial system could collapse. Faced with the need to stabilize the system, UK regulators on October 8 introduced a package of measures that included a £250 billion Credit Guarantee Scheme (the Guarantee Scheme) aimed at providing banks with access to needed funding. Under the Guarantee Scheme, eligible institutions could pay a risk-based fee and issue debt with terms of up to three years that would be guaranteed by HM Treasury. Debt issuance under the Guarantee Scheme was initially quite significant at approximately £100 billion by the end of 2008. After its issuance window closed on February 28, 2010, the Guarantee Scheme terminated on October 26, 2012, when the final guaranteed debt matured. During the course of its existence, the Guarantee Scheme had guaranteed approximately £134 billion in debt. HM Treasury suffered no losses under the program and earned approximately £4.3 billion in fees.
SSRN
In mid-2009, the Bank of England (Bank) opened the Secured Commercial Paper Facility (SCPF) as part of its larger Asset Purchase Facility (APF). Through the facility, the Bank offered to purchase secured commercial paper (SCP), a form of asset backed commercial paper, issued by approved programs from both dealers acting as principal in the primary market and after issue from secondary market holders. The facility was designed to establish the Bank as a ready buyer of SCP in the primary market and as a backstop purchaser in the secondary market. In extending the APF to include purchases of SCP, the Bank aimed to give smaller companies and companies below investment grade quality access to funding, and to improve access to short term working capital for companies that affected the UK economy. To be eligible to participate, programs had to have underlying assets that provided short term credit to borrowers making a material contribution to the UK economy. Only one program was deemed eligible, and the Bank made regular, small purchases of SCP from that program between November 2010 and November 2011. The SCPF was formally closed on August 4, 2016.
SSRN
The Australian Guarantee Scheme for Large Deposits and Wholesale Funding was developed in 2008 shortly after the failure of Lehman Brothers. It was designed to foster financial system stability and confidence and to help depository institutions continue to access funding during a period of volatility. In addition to a guarantee for large deposits, the scheme allowed institutions to apply for a government guarantee for newly issued wholesale liabilities with maturities of up to five years; in return, the institutions paid the government a monthly fee based on their credit rating and the value of the debt guaranteed. The entire Guarantee Scheme became operational in November 2008 and closed to new issuance in March 2010, by which time 16 institutions had issued about A$166 billion ($108.7 billion) of guaranteed securities. The Guarantee Schemeâs wholesale funding component formally ended in October 2015, a few months after the final guaranteed instrument matured. It incurred no losses, no claims were made against it, and it earned A$4.5 billion ($2.95 billion) in fees for the support provided.
arXiv
A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could affect the price formation of an underlying asset market. However, no leveraged ETF trading method for suppressing the increase in volatility as much as possible has yet been proposed. In this paper, we compare different strategies of trading for a proposed trading model and report the results of our investigation regarding how best to suppress an increase in market volatility. As a result, it was found that as the minimum number of orders in a rebalancing trade increases, the impact on the market price formation decreases.
arXiv
The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis. Recently, after visiting the trading floor of a leading financial institution, we noticed that traders always execute their trade orders while observing images of financial time-series on their screens. In this work, we built upon the success in image recognition and examine the value in transforming the traditional time-series analysis to that of image classification. We create a large sample of financial time-series images encoded as candlestick (Box and Whisker) charts and label the samples following three algebraically-defined binary trade strategies. Using the images, we train over a dozen machine-learning classification models and find that the algorithms are very efficient in recovering the complicated, multiscale label-generating rules when the data is represented visually. We suggest that the transformation of continuous numeric time-series classification problem to a vision problem is useful for recovering signals typical of technical analysis.
arXiv
Companies survey their customers to measure their satisfaction levels with the company and its services. The received responses are crucial as they allow companies to assess their respective performances and find ways to make needed improvements. This study focuses on the non-systematic bias that arises when customers assign numerical values in ordinal surveys. Using real customer satisfaction survey data of a large retail bank, we show that the common practice of segmenting ordinal survey responses into uneven segments limit the value that can be extracted from the data. We then show that it is possible to assess the magnitude of the irreducible error under simple assumptions, even in real surveys, and place the achievable modeling goal in perspective. We finish the study by suggesting that a thoughtful survey design, which uses either a careful binning strategy or proper calibration, can reduce the compounding non-systematic error even in elaborated ordinal surveys. A possible application of the calibration method we propose is efficiently conducting targeted surveys using active learning.
SSRN
Whether green bonds deliver a cheaper cost of capital to issuers than vanilla bonds has been a contentious issue since the start of the green bond market. In the marketâs early days anecdotal statements from green bond issuers that their bonds were being oversubscribed, resulting in a pricing difference against equivalent vanilla bonds, led market participants to argue that green bonds provide a cheaper cost of capital. However, this anecdotal evidence was unverifiable until the market matured to a size sufficiently large enough to provide comparable bonds for analysis. The existence of a âgreenium,â a green bond premium over equivalent vanilla bonds, became a key research point for green bond analysts as the market matured. This analysis spread to sustainable finance research centers and bond trading desks and has now become a mainstay topic of green bond conferences and market events. Within academic circles, numerous papers have recently focused on looking directly at pricing differences in the U.S. green municipal-bond market. However, these discussions have yet to provide conclusive evidence for or against the presence of a substantial greenium. The academic debate remains focused on refining a standard methodological approach by which to detect any greenium. Developments such as the green halo effect, which blurs the added value of green bonds for issuers by blending it with the issuerâs vanilla bonds, also make the academic search for a greenium insubstantial in relation to the green bond marketâs overall dynamics. Drawing from the social sciences of finance, this paper contextualizes green bond pricing research by examining recent greenium discussions and the role of the Climate Bonds Initiative (Climate Bonds) in these discussions. We reflect on the beginning of the first green bond pricing research at Climate Bonds and analyze how these early conversations have evolved among both academics and market participants. Drawing from literature review, quantitative pricing data, and qualitative data from semi structured interviews with market participants, we argue that the iterative nature of pricing discussions is a result of both pricing methodologies and market growth dynamics.