# Research articles for the 2020-04-27

A constraint-based notion of illiquidity
Thomas Krabichler,Josef Teichmann
arXiv

This article introduces a new mathematical concept of illiquidity that goes hand in hand with credit risk. The concept is not volume- but constraint-based, i.e., certain assets cannot be shorted and are ineligible as num\'eraire. If those assets are still chosen as num\'eraire, we arrive at a two-price economy. We utilise Jarrow & Turnbull's foreign exchange analogy that interprets defaultable zero-coupon bonds as a conversion of non-defaultable foreign counterparts. In the language of structured derivatives, the impact of credit risk is disabled through quanto-ing. In a similar fashion, we look at bond prices as if perfect liquidity was given. This corresponds to asset pricing with respect to an ineligible num\'eraire and necessitates F\"ollmer measures.

A dynamic conditional approach to portfolio weights forecasting
Fabrizio Cipollini,Giampiero M. Gallo,Alessandro Palandri
arXiv

We build the time series of optimal realized portfolio weights from high-frequency data and we suggest a novel Dynamic Conditional Weights (DCW) model for their dynamics. DCW is benchmarked against popular model-based and model-free specifications in terms of weights forecasts and portfolio allocations. Next to portfolio variance, certainty equivalent and turnover, we introduce the break-even transaction costs as an additional measure that identifies the range of transaction costs for which one allocation is preferred to another. By comparing minimum-variance portfolios built on the components of the Dow Jones 30 Index, the proposed DCW overall attains the best allocations with respect to the measures considered, for any degree of risk-aversion, transaction costs and exposure.

Anomalies in Commodity Futures Markets: Risk or Mispricing?
Hollstein, Fabian,Prokopczuk, Marcel,Tharann, BjÃ¶rn
SSRN
In recent years, commodity markets have become increasingly popular among financial investors. In contrast to traditional markets such as equities or bonds for which many studies have identified various profitable investment strategies, less is known for commodity markets. In this paper, we therefore examine prominent (anomaly) variables in commodity futures markets. We identify sizable premia for jump risk, momentum, and volatility-of-volatility. Other prominent variables, such as downside beta, idiosyncratic volatility, and MAX, are not priced in commodity futures markets. Based on the specific features of commodity futures we draw implications as to whether return premia are driven by behavioral distortions.

Arbitrage-free modeling under Knightian Uncertainty
Matteo Burzoni,Marco Maggis
arXiv

We study the Fundamental Theorem of Asset Pricing for a general financial market under Knightian Uncertainty. We adopt a functional analytic approach which require neither specific assumptions on the class of priors $\mathcal{P}$ nor on the structure of the state space. Several aspects of modeling under Knightian Uncertainty are considered and analyzed. We show the need for a suitable adaptation of the notion of No Free Lunch with Vanishing Risk and discuss its relation to the choice of an appropriate filtration. In an abstract setup, we show that absence of arbitrage is equivalent to the existence of \emph{approximate} martingale measures sharing the same polar set of $\mathcal{P}$. We then specialize the results to a discrete-time framework in order to obtain true martingale measures.

Bank financial stability, bank valuation and international oil prices: Evidence from listed Russian public banks
Claudiu Albulescu
arXiv

Using data on 17 listed public banks from Russia over the period 2008 to 2016, we analyze whether international oil prices affect the bank stability in an oil-dependent country. We posit that a decrease in international oil prices has a negative long-run macroeconomic impact for an oil-exporting country, which further deteriorates the bank financial stability. More specifically, a decrease in international oil prices leads for an oil-exporting country as Russia to a currency depreciation and to a deterioration of the fiscal stance. In addition, given the positive correlation of oil and stock prices documented by numerous previous studies, a decrease in international oil prices represents a negative signal for the stock markets investors, negatively affecting banks' share prices and thus, their capacity to generate sustainable earnings. In this context, the bank financial stability can be menaced. With a focus on public listed banks and using a Pool Mean Group (PMG) estimator, we show that an increase in international oil prices and in the price to book value ratio has a long-run positive effect on Russian public banks stability, and conversely. While positive oil-price shocks contribute to bank stability in the long run, an opposite effect is recorded for negative shocks. However, no significant impact is documented in the short run. Our findings are robust to different bank stability specifications, different samples and control variables.

Capital Adequacy and Systemic Risk of Banks in India
Dash, Mihir
SSRN

Collateral Eligibility of Corporate Debt in the Eurosystem
Pelizzon, Loriana,Riedel, Max,Simon, Zorka,Subrahmanyam, Marti G.
SSRN
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is unaffected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.

Corporate Investment and Financing over the Industry Business Cycle
Kumar, Praveen,Yerramilli, Vijay
SSRN
How do firms vary their capital investment and financing policies in response to business cycle fluctuations within their industry? To address this question, we use the regime-switching approach to compute the quarterly time-series of the probability of a future industry downturn for industry groups within the U.S. manufacturing sector. After controlling for the aggregate business cycle, we find that firm-level capital investment and debt issuance are procyclical, whereas net equity issuance and cash accumulation are countercyclical over the industry business cycle. However, there is substantial heterogeneity across firms based on size and credit rating. The effects of the industry business cycle are often larger than that of the aggregate business cycle, and including the former in the analysis changes the relationships between firm-level financing policies and aggregate business cycle documented in previous studies. We show that extending the standard dynamic trade-off model to incorporate heterogeneous effects of industry and aggregate business cycles can help explain the empirical findings.

Current Scenario of Financial Soundness of Textile, Pharmaceutical and Chemical Industry of Bangladesh: An Emphasize on Altmanâ€™s Z-Score for Publicly Traded Manufacturing Companies
Hossain, Dewan Azmal,Roy, Saykat Chandra,Mim, MD. Sajadul Huque
SSRN
This study analyzes the financial soundness of the textile, pharmaceutical and chemical industry of Bangladesh using Altmanâ€™s Z score. Textile, pharmaceutical and chemical industries are contributing to the overall economy of Bangladesh in a gargantuan way. Thatâ€™s why it is important to analyze the financial health of these sectors to understand the current scenario so that organizations that are facing difficulties in terms of solvency or profitability may get proper attention from the authority. The key findings of this study include 66 % of the organizations under the Pharmaceutical and Chemical industry are in the â€œSafe zoneâ€, whereas 45% of the organizations under the Textile industry are in the â€œSafe Zoneâ€. 28% of the organizations under Pharmaceutical and Chemical industry are in the â€œDistress zoneâ€ whereas 30% of the organizations under the Textile industry are in the â€œDistress zoneâ€. 25% of the organizations under the Textile industry are in the â€œGrey zoneâ€ whereas only 9% of the organizations under the Pharmaceutical and Chemical industry are in the â€œGrey zoneâ€. This study also finds that overall, textile and pharmaceuticals and chemical industries are in safe zone where their scores are 4.43 and 4.50 respectively.

Empirical Study of Market Impact Conditional on Order-Flow Imbalance
Anastasia Bugaenko
arXiv

In this research, we have empirically investigated the key drivers affecting liquidity in equity markets. We illustrated how theoretical models, such as Kyle's model, of agents' interplay in the financial markets, are aligned with the phenomena observed in publicly available trades and quotes data. Specifically, we confirmed that for small signed order-flows, the price impact grows linearly with increase in the order-flow imbalance. We have, further, implemented a machine learning algorithm to forecast market impact given a signed order-flow. Our findings suggest that machine learning models can be used in estimation of financial variables; and predictive accuracy of such learning algorithms can surpass the performance of traditional statistical approaches.

Understanding the determinants of price impact is crucial for several reasons. From a theoretical stance, modelling the impact provides a statistical measure of liquidity. Practitioners adopt impact models as a pre-trade tool to estimate expected transaction costs and optimize the execution of their strategies. This further serves as a post-trade valuation benchmark as suboptimal execution can significantly deteriorate a portfolio performance.

More broadly, the price impact reflects the balance of liquidity across markets. This is of central importance to regulators as it provides an all-encompassing explanation of the correlation between market design and systemic risk, enabling regulators to design more stable and efficient markets.

Fundamental Sources of the Time Variation in Equity Risk Levels
Sinagl, Petra
SSRN
What drives the time variation in equity risk? I find that industries with high fundamental cash-flow risk have a higher degree of time variation in excess returns, systematic risk premia and risk-adjusted returns. Firms in industries with high fundamental cash-flow risk also have relatively high CAPM betas, smaller firm size and higher book-to-market ratios. I propose an asset pricing model with agents endowed with state-dependent preferences that matches these empirical observations. In this model, assets with high fundamental cash-flow risk exhibit not only higher levels of systematic risk but are also exposed to large time variation (or state dependence) of conditional equity risk. My work suggests that, unlike a habit-formation model, a consumption-based model with preference shocks is useful in determining how cash-flow risk drives the crossectional variation in the distribution of risk that is empirically observed across US industries.

Generalization of Affine Feedback Stock Trading Results to Include Stop-Loss Orders
Chung-Han Hsieh
arXiv

The takeoff point of this paper is to generalize the existing stock trading results for a class of affine feedback controller to include consideration of a stop-loss order. Using the geometric Brownian motion as the underlying stock price model, our main result is to provide a closed-form expression for the cumulative distribution function for the trading profit or loss. In addition, we show that the affine feedback controller with stop-loss order indeed generalizes the result without stop order in the sense of distribution function. Some simulations and illustrative examples are also provided as supporting evidence of the theory. Moreover, we provide some technical results aimed at addressing the issues about survivability, cash-financing considerations, long-only property, and lower bound of the expected gain or loss.

Good Days, Bad Days: Stock Market Fluctuation and Taxi Tipping Decisions
Tan, Weiqiang,Zhang, Jian
SSRN
Using taxicab tipping records in New York City (NYC), we develop a novel measure of real-time utility and quantitatively assess the impact of wealth change on the well-being of individuals based on the core tenet of prospect theory. The baseline estimate suggests that a one standard deviation increase in the stock market index is associated with a 0.3% increase in the daily average tipping ratio, which translates to an elasticity estimate of 0.3. The impact is short-lived and in line with the wealth effect interpretation. Consistent with loss aversion, we find that the impact is primarily driven by wealth loss rather than gain. We exploit GPS and timestamp information and design two difference-in-difference tests to establish causal inference. Exploitation of the characteristics of individual stocks suggests that the effect of wealth change on real-time utility is more pronounced in the stocks of firms with large market capitalization. Finally, our aggregate estimate suggests that annual tip revenue in the NYC taxi industry is associated with stock market fluctuation, ranging from -$17.5 million to$12.9 million.

Growing green: the role of path dependency and structural jumps in the green economy expansion
arXiv

Existing research argues that countries increase their production basket by adding products which require similar capabilities to those they already produce, a process referred to as path dependency. Green economic growth is a global movement that seeks to achieve economic expansion while at the same time mitigating environmental risks. We postulate that countries engaging in green economic growth are motivated to invest strategically to develop new capabilities that will help them transition to a green economy. As a result, they could potentially increase their production baskets not only by a path dependent process but also by the non path dependent process we term, high investment structural jumps. The main objective of this research is to determine whether countries increase their green production basket mainly by a process of path dependency, or alternatively, by a process of structural jumps. We analyze data from 65 countries and over a period from years 2007 to 2017. We focus on China as our main case study. The results of this research show that countries not only increase their green production baskets based on their available capabilities, following path dependency, but also expand to products that path dependency does not predict by investing in innovating and developing new environmental related technologies.

How Valuable is Financial Flexibility When Revenue Stops? Evidence from the Covid-19 Crisis
Fahlenbrach, RÃ¼diger,Rageth, Kevin,Stulz, RenÃ© M.
SSRN
The COVID-19 shock creates a sudden temporary sharp shortfall in revenue for firms. We expect firms with greater financial flexibility to be better able to fund themselves in the presence of a revenue shortfall and to benefit less from the news concerning policy responses to the crisis on March 24. We find strong evidence that firms with less financial flexibility, i.e., firms with lower cash holdings, higher short-term debt, and higher long-term debt, experience worse stock returns until March 23 and benefit more from the news on March 24. Comparing firms at the 75th percentile of the financial flexibility proxies to those at the 25th percentile, we find that the firms with high financial flexibility experience a stock drop lower by 26% or 9.7 percentage points than those with low financial flexibility. Similar results hold for CDS spreads. Existing measures of financial constraints are not helpful in explaining the reaction of firms to the shock. There is no evidence that, on average, the shock has a larger effect on firms that have larger payouts through dividends or repurchases before the crisis.

Investment Factor Timing: Harvesting The Low-Risk Anomaly Using Artiï¬cial Neural Networks
Dirkx, Philipp,Heil, Thomas
SSRN
We perform investment factor timing based on risk forecasts in reference to the low-risk anomaly. Among various risk measures, we ï¬nd downside deviation most suited for this task. We apply Long Short Term Memory Artiï¬cial Neural Networks (LSTM ANNs) to model the relationship between macro-economic as well as ï¬nancial market data and the downside deviation of factors. The LSTM ANNs allow for complex, non-linear long-term dependencies. We use LSTM-based forecasts to select high- and low-risk factors to set up an investment strategy. The strategy succeeds in diï¬€erentiating positive from negative yielding factors and an accordingly constructed investment strategy outperforms every factor individually as well as a GARCH benchmark model.

Machine Learning for Asset Managers (Chapter 1)
Lopez de Prado, Marcos
SSRN
Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory, rather than on back-testing potential trading rules. The purpose of this monograph is to introduce Machine Learning (ML) tools that can help asset managers discover economic and financial theories.ML is not a black-box, and it does not necessarily over-fit. ML tools complement rather than replace the classical statistical methods. Some of MLâ€™s strengths include: (i) Focus on out-of-sample predictability over variance adjudication; (ii) usage of computational methods to avoid relying on (potentially unrealistic) assumptions; (iii) ability to â€œlearnâ€ complex specifications, including non-linear, hierarchical and non-continuous interaction effects in a high-dimensional space; and (iv) ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Multiple yield curve modelling with CBI processes
Claudio Fontana,Alessandro Gnoatto,Guillaume Szulda
arXiv

We develop a modelling framework for multiple yield curves driven by continuous-state branching processes with immigration (CBI processes). Exploiting the self-exciting behavior of CBI jump processes, this approach can reproduce the relevant empirical features of spreads between different interbank rates. In particular, we introduce multi-curve models driven by a flow of CBI processes. Such models are especially parsimonious and tractable, and can generate contagion effects among different spreads. We provide a complete analytical framework, including a detailed study of discounted exponential moments of CBI processes. The proposed approach allows for explicit valuation formulae for all linear interest rate derivatives and semi-closed formulae for non-linear derivatives via Fourier techniques and quantization. We show that a simple specification of the model can be successfully calibrated to market data.

Necessary and Sufficient Conditions for Frequency-Based Kelly Optimal Portfolio
Chung-Han Hsieh
arXiv

In this paper, we consider a discrete-time portfolio with $m \geq 2$ assets optimization problem which includes the rebalancing~frequency as an additional parameter in the maximization. The so-called Kelly Criterion is used as the performance metric; i.e., maximizing the expected logarithmic growth of a trader's account, and the portfolio obtained is called the frequency-based Kelly optimal portfolio. The focal point of this paper is to extend upon the results of our previous work to obtain various optimality characterizations on the portfolio. To be more specific, using Kelly's criterion in our frequency-based formulation, we first prove necessary and sufficient conditions for the frequency-based Kelly optimal portfolio. With the aid of these conditions, we then show several new optimality characterizations such as expected ratio optimality and asymptotic relative optimality, and a result which we call the Extended Dominant Asset Theorem. That is, we prove that the $i$th asset is dominant in the portfolio if and only if the Kelly optimal portfolio consists of that asset only. The word "extended" on the theorem comes from the fact that it was only a sufficiency result that was proved in our previous work. Hence, in this paper, we improve it to involve a proof of the necessity part. In addition, the trader's survivability issue (no bankruptcy consideration) is also studied in detail in our frequency-based trading framework. Finally, to bridge the theory and practice, we propose a simple trading algorithm using the notion called dominant asset condition to decide when should one triggers a trade. The corresponding trading performance using historical price data is reported as supporting evidence.

New Revenue Recognition Standard and Earnings Informativeness
Chung, Kee H.,Chuwonganant, Chairat
SSRN
The implementation of the new revenue recognition standard (ASC 606) has significantly changed the impact of earnings announcements on various measures of market quality and trading activities. In contrast to the finding of prior research, we show that earning announcements are accompanied by a decrease in the bid-ask spread, the price impact of trades, informed trading, and pricing efficiency, and an increase in the quoted depth after the implementation of ASC 606. These results indicate that ASC 606 has improved the informativeness of earnings and changed the effect of earnings announcements on the firmâ€™s information and trading environments accordingly.

Post-Crisis International Financial Regulatory Reforms: A Primer
Borio, Claudio E. V.,Farag, Marc,Tarashev, Nikola A.
SSRN
This paper reviews post-crisis financial regulatory reforms, examines how they fit together and identifies open issues. Specifically, it takes stock of the salient new features of bank and CCP international standards within a unified analytical framework. The key notion in this framework is "shock-absorbing capacity", which is higher when (i) there is less exposure to the losses that a shock generates and (ii) there are more resources to absorb such losses. How do the reforms strengthen this capacity, individually and as a package? Which areas merit further attention? We argue that, given the political economy pressures and technical obstacles that the reforms have faced, as well as the inherent uncertainty about the reforms' effects, it is important to maintain a conservative regulatory approach. A higher cost of balance sheet space is a healthy side effect of the backstops underpinning such an approach.

Predictability in Commodity Markets: Evidence from More Than a Century
Hollstein, Fabian,Prokopczuk, Marcel,Tharann, BjÃ¶rn,Wese Simen, Chardin
SSRN
Using more than 140 years of data, we comprehensively analyze the predictive power of a broad set of macroeconomic variables for risk and return in commodity spot markets. We find that industrial production growth and inflation are the strongest predictors for future commodity excess returns. Many further variables help predict future commodity volatilities. The introduction of derivatives generally reduces the predictability in the most active commodity markets but increases the predictability in others. Thus, derivatives likely make markets more efficient, but also attract most of the price discovering activity. Commodity spot volatilities generally rise after futures introduction.

Predicting the Equity Premium around the Globe: Comprehensive Evidence from a Large Sample
Hollstein, Fabian,Prokopczuk, Marcel,Tharann, BjÃ¶rn,Wese Simen, Chardin
SSRN
By studying 81 countries over a period of up to 144 years, with different classes of predictor variables and various forecast specifications, we conduct the most comprehensive equity premium predictability analysis to date. We find that excess returns are more predictable in Emerging and Frontier than in Developed Markets. For all groups, forecast combinations perform very well out-of-sample. Analyzing the cross-section of countries, we find that both market inefficiency and the variation in expected returns over time are important drivers of return predictability. Finally, domestic inflation-adjusted returns are substantially more predictable than USD returns.

Primer on Suitability for Consumer Data Use and Product Design
Chugh, Beni,Raghavan, Malavika,Singh, Anubhutie
SSRN
Internationally, suitability or appropriateness in finance is conceptualized as "the degree to which the product or service offered by the intermediary matches the retail client's financial situation, investment objectives, level of risk tolerance, financial need, knowledge and experience" (Basel Committee on Banking Supervision, 2008). A suitability-based approach to financial services provision places obligations on providers to make assessments of whether a product is suitable prior to the sale to a consumer. The effort is to move away from the current approach that places the burden solely on consumers to make complex financial choices. In India, financial sector regulators have supported the need to emphasize on suitability to varying extents. The Reserve Bank of India, for instance, upholds the consumersâ€™ right to suitability in its Charter of Customer Rights. However, practitioners have often asserted that suitability is challenging to implement in practice because of the costs of granular suitability assessments and the absence of the detailed consumer information needed for them. With the growing use of personal information and analytics in provision of financial services, that challenge appears solvable â€" unlocking the potential for providers to undertake suitability assessments when selling or designing financial products. These new techniques however raise a new set of issues for consideration. First, the inappropriate use of personal information may itself create new harms for consumers. This raises questions about the boundaries of appropriate data use by all stakeholders in this new digital environment. Second, suitability frameworks may need to be updated for a new world where users can interact with integrated platforms as their â€œfront-endâ€ to finance. Such integrated front-ends allow various product features to be brought together by multiple providers. This begs the question of whether suitability frameworks evolved for a more analogue financial landscape need to be updated for digital financial services. These and related concerns re-open many questions on the need for new suitability frameworks for providers and regulators operating in the data-driven environment of modern digital finance.

Productivity, Model Uncertainty, and the New Home Sales Price in the U.S.
Huang, Xiaohui (Ivy)
SSRN
In this paper, I study the recent swing in the new home sales price in a dynamic stochastic general equilibrium model that features sector-specific total factor productivity (TFP) shocks, agents who concern about model uncertainty (in the Knightian sense), indivisible labor in householdsâ€™ preferences, and adjustment costs of factor inputs. My paper dedicates to answer the question that whether a one-time temporary change in the model uncertainty level, along with the productivity shocks, can account for a significant amount of the boom and bust observed in the recent new home sales price in U.S. I construct the TFP shocks for the construction and non-construction sectors separately using aggregate macroeconomic data. The benchmark model, with only TFP shocks and no model uncertainty, can account for 25 percent of the surge and more than 60 percent of the slump in the new home sales price. Model uncertainty is specified in the Knightian sense. Agents who fear about model misspecification are not fully confident that the model precisely defines the true data generating process, thus they form expectations in an extremely pessimistic way and guard themselves against the worst-case scenario in decision-making. Quantitative analysis shows that with a one-time temporary change in the model uncertainty level the benchmark model is able to capture 35 percent of the increase in the new home sales price. I simulate the distributions of TFP shocks under the worst-case model. The results show that the worst-case state transition law, compared to the transition law under the approximating model, acts more like a negative mean shift rather than a variance dispersion. Besides, a rise in model uncertainty does not increase the correlation between TFP shocks under the worst-case model.

Risk-Sensitive Credit Portfolio Optimization under Partial Information and Contagion Risk
Lijun Bo,Huafu Liao,Xiang Yu
arXiv

This paper investigates the finite time risk-sensitive portfolio optimization in a regime-switching credit market with physical and information-induced default contagion. The Markov regime-switching process is assumed to be unobservable, which has countable states that affect default intensities of surviving assets. The stochastic control problem is formulated under partial observations of asset prices and default events. By proving a novel martingale representation theorem based on incomplete and phasing out filtration, we characterize the value function in an equivalent form. This allows us to connect the control problem to a new type of quadratic BSDE with jumps, in which the driver term has non-standard structures and carries the conditional filter as an infinite-dimensional parameter. By proposing some truncation techniques and establishing a uniform a priori estimates, we obtain the existence of a solution to this BSDE using the convergence of solutions associated to some truncated BSDEs. The verification theorem can be concluded with the aid of our BSDE results, which in turn yields the uniqueness of the solution to the BSDE.

SABR smiles for RFR caplets
Sander Willems
arXiv

We present a natural extension of the SABR model to price both backward and forward-looking RFR caplets in a post-Libor world. Forward-looking RFR caplets can be priced using the market standard approximations of Hagan et al. (2002). We provide closed-form effective SABR parameters for pricing backward-looking RFR caplets. These results are useful for smile interpolation and for analyzing backward and forward-looking smiles in normalized units.

Testing the Day-of-the-Week Effect in the Indian Stock Market using the AR-GARCH Model
Dash, Mihir
SSRN
This study combines three distinct empirical models of stock returns into a single model: the autoregressive model, which suggests that stock returns are determined by its own past values, the (generalised) autoregressive conditional heteroscedasticity model, which suggests that stock returns volatility is determined by its past values and by returns shocks, and the day-of-the-week effect, which suggests that stock returns are higher on particular days of the week (usually Fridays). All three models represent departures from the Random Walk Hypothesis (RWH), in the sense of proposing a certain degree of predictability in stock returns. The study examines the extent to which the AR-GARCH model with day-of-the-week dummy variables for twenty major stocks from the Indian banking sector. The stock price data was collected from the National Stock Exchange (NSE). The study period selected was Apr. 1, 2018 to Mar. 31, 2019, a period of one year.

The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time
Fabian Stephany,Niklas Stoehr,Philipp Darius,Leonie Neuhäuser,Ole Teutloff,Fabian Braesemann
arXiv

While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.

The Era of Digital Financial Innovation: Lessons from Economic History on Regulation
Longworth, David
SSRN
While digital finance innovation has the potential to provide businesses and consumers with lower costs and a greater range of financial services, it has raised questions regarding the risks it poses to consumers, individual financial institutions, the financial system and the economy at large â€" and how Canadian financial regulators should respond to those risks. This Commentary focuses on the major macro-level risks that might arise, and sets out to answer three questions. What do we know from economic history about financial innovation in general and banking crises? What are some key areas arising from current and future digital financial innovation that regulators need to examine? And what are the implications for actions by Canadian regulators? Economic history gives us many examples of instances when financial innovations, rapid growth in credit supply and increased reliance on short-term financing have led to financial instability and crises. Therefore, at a time of rapid changes in the financial sector, it is important that regulators pay close attention to what is happening and take appropriate action. After discussing key areas of concern for Canadian regulators, I recommend that they should: (i) Require the explainability of machine-learning models used for lending decisions. As it is, artificial intelligence (AI) is being used in lending decisions at both regulated and largely unregulated institutions. If not properly examined by internal risk managers, it can lead to unsafe lending decisions. (ii) Take care not to rush into open banking regulations, for example regarding money-moving apps, that could increase the likelihood of bank runs. (iii) Collect better and more timely data by type of financial institution on types of credit and short-term financing. (iv) Extend the coverage of stress tests to examine stresses related to rapid new borrowing from non-bank financial institutions, or shadow banks, that are not prudentially regulated. At the micro level, regulators will have to weigh closely the costs of new regulations against the benefits of financial innovations. At the macro level, however, the steps suggested in this Commentary should have little or no effect on the vast majority of digital financial innovations that are underway or contemplated in the near future. Therefore, there would be no real trade-off between the increased stability coming from these actions and the increased competition, efficiency and range of financial services that should come from digital financial innovation.

The Janus Face of Bank Geographic Complexity
Aldasoro, IÃ±aki,Hardy, Bryan,Jager, Maximilian
SSRN
We study the relationship between bank geographic complexity and risk using a unique dataset of 96 global bank holding companies (BHCs) over 2008-2016. From data on the affiliate network of internationally active banking entities, we construct a measure of geographic coverage and complexity for each BHC. We find that higher geographic complexity heightens banks' capacity to absorb local economic shocks, reducing their risk. However, higher geographic complexity is also associated with a higher vulnerability to global shocks and less impact of prudential regulation, increasing their risk. Geographic complexity helps more (with respect to local shocks) and hurts less (with respect to global shocks) if countries' business cycles are misaligned. Large, international regulatory reforms such as the implementation of the GSIB framework and the European Single Supervisory Mechanism reduce bank risk, but geographic complexity weakens this effect. Bank geographic complexity therefore has a Janus face, decreasing some but increasing other aspects of bank risk.

The Jarrow & Turnbull setting revisited
Thomas Krabichler,Josef Teichmann
arXiv

We consider a financial market with zero-coupon bonds that are exposed to credit and liquidity risk. We revisit the famous Jarrow & Turnbull setting in order to account for these two intricately intertwined risk types. We utilise the foreign exchange analogy that interprets defaultable zero-coupon bonds as a conversion of non-defaultable foreign counterparts. The relevant exchange rate is only partially observable in the market filtration, which leads us naturally to an application of the concept of platonic financial markets. We provide an example of tractable term structure models that are driven by a two-dimensional affine jump diffusion. Furthermore, we derive explicit valuation formulae for marketable products, e.g., for credit default swaps.

The Role of Money in the Business Cycle
Zhao Jianglin
arXiv

The aim of this paper is to reemphasize the money theory of exchange which is centered on the function of exchange medium of money, and make a contribution towards linearization of the quantity equation of exchange. A dynamical quantity equation is presented and an important balanced path of economic evolution is derived. To understand the business cycle we propose a hypothesis of natural cycle and driving cycle concerning the evolution of the balanced path and plentiful conclusions can be made.

The What, When and Where of Limit Order Books
Johannes Bleher,Michael Bleher,Thomas Dimpfl
arXiv

We model the limit order book (LOB) as a continuous Markov process and develop an algebra to describe its dynamics based on the fundamental events of the book: order arrivals and cancellations. We show how all observables (prices, returns, and liquidity measures) are governed by the same variables which also drive arrival and cancellation rates. The sensitivity of our model is evaluated in a simulation study and an empirical analysis. We estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts on several frequencies. The in-sample results based on contemporaneous information suggest that our model describes up to 90% of the variation of close-to-close returns, the adjusted $R^2$ still ranges at around 80%. In the more realistic setting where only past information enters the model, we still observe an adjusted $R^2$ in the range of 15%. The direction of the next return can be predicted, out-of-sample, with an accuracy of over 75% for short time horizons below 10 minutes. Out-of-sample, on average, we obtain $R^2$ values for the Mincer-Zarnowitz regression of around 2-3% and an $RMSPE$ that is 10 times lower than values documented in the literature.

The new face of multifractality: Multi-branchedness and the phase transitions in time series of mean inter-event times
Jarosław Klamut,Ryszard Kutner,Tomasz Gubiec,Zbigniew R. Struzik
arXiv

Empirical time series of inter-event or waiting times are investigated using a modified Multifractal Detrended Fluctuation Analysis operating on fluctuations of mean detrended dynamics. The core of the extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent $h(q)$ -- the fundamental exponent in the study of multifractals. The consequence of this behavior is the non-monotonic behavior of the coarse H\"older exponent $\alpha (q)$ leading to multi-branchedness of the spectrum of dimensions. The Legendre-Fenchel transform is used instead of the routinely used canonical Legendre (single-branched) contact transform. Thermodynamic consequences of the multi-branched multifractality are revealed. These are directly expressed in the language of phase transitions between thermally stable, metastable, and unstable phases. These phase transitions are of the first and second orders according to Mandelbrot's modified Ehrenfest classification. The discovery of multi-branchedness is tantamount in significance to extending multifractal analysis.

Time-inconsistent consumption-investment problems in incomplete markets under general discount functions
Yushi Hamaguchi
arXiv

In this paper, we study a time-inconsistent consumption-investment problem with random endowments in a possibly incomplete market under general discount functions. We provide a necessary condition and a verification theorem for an open-loop equilibrium consumption-investment pair in terms of a coupled forward-backward stochastic differential equation. Moreover, we prove the uniqueness of the open-loop equilibrium pair by showing that the original time-inconsistent problem is equivalent to an associated time-consistent one.

Trade Credit and the Transmission of Unconventional Monetary Policy
Adelino, Manuel,Ferreira, Miguel A.,Giannetti, Mariassunta,Pires, Pedro
SSRN
We show that trade credit in production networks is important for the transmission of unconventional monetary policy. We find that firms with bonds eligible for purchase under the European Central Bankâ€™s Corporate Sector Purchase Program act as financial intermediaries and extend more trade credit to their customers. The increase in trade credit flows is more pronounced from core countries to periphery countries and towards financially constrained customers. Customers increase investment and employment in response to the additional financing, while suppliers with eligible bonds increase their customer base, potentially favoring upstream industry concentration. Our findings suggest that the trade credit channel of monetary policy produces heterogeneous effects on regions, industries, and firms.

Trading Foreign Exchange Triplets
Álvaro Cartea,Sebastian Jaimungal,Tianyi Jia
arXiv

We develop the optimal trading strategy for a foreign exchange (FX) broker who must liquidate a large position in an illiquid currency pair. To maximize revenues, the broker considers trading in a currency triplet which consists of the illiquid pair and two other liquid currency pairs. The liquid pairs in the triplet are chosen so that one of the pairs is redundant. The broker is risk-neutral and accounts for model ambiguity in the FX rates to make her strategy robust to model misspecification. When the broker is ambiguity neutral (averse) the trading strategy in each pair is independent (dependent) of the inventory in the other two pairs in the triplet. We employ simulations to illustrate how the robust strategies perform. For a range of ambiguity aversion parameters, we find the mean Profit and Loss (P&L) of the strategy increases and the standard deviation of the P&L decreases as ambiguity aversion increases.

Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion
Anton J. Heckens,Sebastian M. Krause,Thomas Guhr
arXiv

The measured correlations of financial time series in subsequent epochs change considerably as a function of time. When studying the whole correlation matrices, quasi-stationary patterns, referred to as market states, are seen by applying clustering methods. They emerge, disappear or reemerge, but they are dominated by the collective motion of all stocks. In the jargon, one speaks of the market motion, it is always associated with the largest eigenvalue of the correlation matrices. Thus the question arises, if one can extract more refined information on the system by subtracting the dominating market motion in a proper way. To this end we introduce a new approach by clustering reduced-rank correlation matrices which are obtained by subtracting the dyadic matrix belonging to the largest eigenvalue from the standard correlation matrices. We analyze daily data of 262 companies of the S&P 500 index over a period of almost 15 years from 2002 to 2016. The resulting dynamics is remarkably different, and the corresponding market states are quasi-stationary over a long period of time. Our approach adds to the attempts to separate endogenous from exogenous effects.

Untying Interconnectedness: Topology, Stability and the Post-crisis Reforms
Turing, Dermot
SSRN
â€˜Interconnectednessâ€™ was considered to be a cause of the 2008 financial crisis, stimulating a number of studies into the topology of financial markets. Yet the analysis of instability within networks has tended to focus on a type of â€˜contagionâ€™ which imagines serial insolvencies, with non-performance of due obligations causing solvency issues for connected institutions. A more realistic assessment of the 2008 crisis was that it was due to a drying-up of available cash. A taxonomy of contagion is proposed, and the illiquidity model of contagion is then analyzed with reference to the observed core-periphery structure of financial market networks. Finally, the post-crisis reforms are judged against the view of â€˜interconnectednessâ€™ which emerges.

Why Are US Corporate Boards Under-diversified Among Genders and Races?
Lemayian, Zawadi,Pownall, Grace,Short, Justin
SSRN
The popular and academic literatures report that women, blacks, Hispanics, and Native Americans are under-represented on US corporate boards of directors, relative to their incidence in the US labor force. We confirm this observation by tabulating the presence of female and under-represented racial minority (URM) directors on the boards of the S&P 1500 firms. We examine two hypotheses that may explain this empirical regularity. The first is the scarcity of qualified women and URM candidates, causing firms to compete for their services and putting price pressure on the compensation of these candidates, leading to higher compensation for female and non-URM directors. The second is a perception that women and URM directors in general are less valuable to firms, leading to lower compensation for female and non-white directors relative to their white male peers. We find evidence that women (URM) make significantly higher (insignificantly lower) compensation than males and non-URM directors, controlling for firm and director variables (like age, education, achievements, and committee memberships and chairmanships). However, when we compare the compensation of women and URM directors against male and non-URM directorsâ€™ compensation on the same boards, that significant difference reverses. Our interpretation is that females and URM directors are chosen by larger firms with larger and more active boards (and higher base compensation), but once chosen they are paid significantly less than their non-URM male peers, even though they have more education and more achievements than those peers.