Research articles for the 2020-03-30

A Dual Method For Backward Stochastic Differential Equations with Application to Risk Valuation
Andrzej Ruszczynski,Jianing Yao

We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we show that a piecewise-constant dual control provides a good approximation on a short interval. A dynamic programming algorithm extends the approximation to a finite time horizon. Finally, we illustrate the application of the procedure to financial risk management in conjunction with nested simulation and on an multidimensional portfolio valuation problem.

A Framework for Online Investment Algorithms
Andrew Paskaramoorthy,Terence van Zyl,Tim Gebbie

The artificial segmentation of an investment management process into a workflow with silos of offline human operators can restrict silos from collectively and adaptively pursuing a unified optimal investment goal. To meet the investor's objectives, an online algorithm can provide an explicit incremental approach that makes sequential updates as data arrives at the process level. This is in stark contrast to offline (or batch) processes that are focused on making component level decisions prior to process level integration. Here we present and report results for an integrated, and online framework for algorithmic portfolio management. This article provides a workflow that can in-turn be embedded into a process level learning framework. The workflow can be enhanced to refine signal generation and asset-class evolution and definitions. Our results confirm that we can use our framework in conjunction with resampling methods to outperform naive market capitalisation benchmarks while making clear the extent of back-test over-fitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.

A Mean-Field Game Approach to Equilibrium Pricing, Optimal Generation, and Trading in Solar Renewable Energy Certificate (SREC) Markets
Arvind Shrivats,Dena Firoozi,Sebastian Jaimungal

SREC markets are a market-based system designed to incentivize solar energy generation. A regulatory body imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a certificate for each MWh generated. Regulated firms seek to navigate the market to minimize the cost imposed on them, by modulating their SREC generation and trading activities. As such, the SREC market can be viewed through the lens of a large stochastic game with heterogeneous agents, where agents interact through the market price of the certificates. We study this stochastic game by solving the mean-field game (MFG) limit with sub-populations of heterogeneous agents. Our market participants optimize costs accounting for trading frictions, cost of generation, SREC penalty, and generation uncertainty. Using techniques from variational analysis, we characterize firms' optimal controls as the solution of a new class of McKean-Vlasov FBSDE and determine the equilibrium SREC price. We further prove that MFG strategy has the $\epsilon$-Nash property for the finite player game. Finally, we numerically solve the MV-FBSDEs and conclude by demonstrating how firms behave in equilibrium using simulated examples.

A Second Order Cumulant Spectrum Test That a Stochastic Process is Strictly Stationary and a Step Toward a Test for Graph Signal Strict Stationarity
Denisa Roberts,Douglas Patterson

This article develops a statistical test for the null hypothesis of strict stationarity of a discrete time stochastic process in the frequency domain. When the null hypothesis is true, the second order cumulant spectrum is zero at all the discrete Fourier frequency pairs in the principal domain. The test uses a window averaged sample estimate of the second order cumulant spectrum to build a test statistic with an asymptotic complex standard normal distribution. We derive the test statistic, study the properties of the test and demonstrate its application using 137Cs gamma ray decay data. Future areas of research include testing for strict stationarity of graph signals, with applications in learning convolutional neural networks on graphs, denoising, and inpainting.

A Statistical Classification of Cryptocurrencies
Pele, Daniel Traian,Wesselhöfft, Niels,Härdle, Wolfgang K.,Kolossiatis, Michalis,Yatracos, Yannis
The aim of this paper is to derive the main factors that separate cryptocurrencies from the classical assets, by using various classification techniques applied to the daily time series of log-returns. In this sense, a daily time series of asset returns (either cryptocurrencies or classical assets) can be characterized by a multidimensional vector with statistical components like variance, skewness, kurtosis, tail probability, quantiles, conditional tail expectation or GARCH parameters. By using dimension reduction techniques (Factor Analysis) and classification models (Binary Logistic Regression, Discriminant Analysis, Support Vector Machines, K-means clustering, Variance Components Split methods) for a representative sample of cryptocurrencies, stocks, exchange rates and commodities, we are able to classify cryptocurrencies as a new asset class with unique features in the tails of the log-returns distribution. The main result of our paper is the complete separation of the cryptocurrencies from the other type of assets, by using the Maximum Variance Components Split method. In addition, we observe a synchronicity in the evolution of of the cryptocurrencies, compared to the classical assets, mainly due to the tails behaviour of the log-return distribution.

A dynamical approach to Zipf's law
Giordano De Marzo,Andrea Gabrielli,Andrea Zaccaria,Luciano Pietronero

The rank-size plots of very different systems are usually described in terms of Zipf's law, but the deep meaning of this scaling law is still controversial. We show how usual criteria used to identify Zipf's scaling fail to capture an essential feature of this phenomenon, that is the coherent evolution of the system and the space in which it is embedded. We therefore identify a Zipfian dynamics in terms of dynamical constraints which relate in time the level of sampling and the parameters of the probability distribution of sizes. This permits to introduce the concepts of coherent evolution, which is the key ingredient to develop a genuine Zipf's behaviour. We finally discuss a number of practical examples. For instance, earthquakes can evolve only incoherently and thus show only a spurious Zipf's law, while natural language dynamics is intrinsically Zipfian due to dynamical coherence imposed by grammar rules.

An iterative splitting method for pricing European options under the Heston model
Hongshan Li,Zhongyi Huang

In this paper, we propose an iterative splitting method to solve the partial differential equations in option pricing problems. We focus on the Heston stochastic volatility model and the derived two-dimensional partial differential equation (PDE). We take the European option as an example and conduct numerical experiments using different boundary conditions. The iterative splitting method transforms the two-dimensional equation into two quasi one-dimensional equations with the variable on the other dimension fixed, which helps to lower the computational cost. Numerical results show that the iterative splitting method together with an artificial boundary condition (ABC) based on the method by Li and Huang (2019) gives the most accurate option price and Greeks compared to the classic finite difference method with the commonly-used boundary conditions in Heston (1993).

Analysis of the Effect of COVID-19 on the Stock Market and Potential Investing Strategies
Yan, Binxin,Stuart, Logan,Tu, Andy,Zhang, Tony
In this paper, we analyze the potential effects that the coronavirus, “COVID-19”, will have on the economy and then we propose possible ways that an individual could profit off a market affected by a global viral outbreak. We look at past outbreaks and come to the conclusion that often markets will react adversely to these such incidents in the short run but that in the long run, markets eventually correct themselves and increase. In order to profit off of such a market, we propose shorting industries that will be immediately affected by the virus in the short run and then eventually buying back into those industries after their price has dropped significantly. Specifically, we look at the travel industry, technology sector, entertainment industry, and gold as potential areas where great profit can be made.

Autocorrelation of returns in major cryptocurrency markets
Eugene Tartakovsky,Ksenia Plesovskikh,Anastasiia Sarmakeeva,Alexander Bibik

This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze autocorrelation of returns in major cryptocurrency markets using the following methods: Pearson's autocorrelation coefficient of different orders, Ljung-Box test, and first-order Pearson's autocorrelation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant autocorrelation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.

Becoming Sensitive: Males' Risk and Time Preferences after the 2008 Financial Crisis
Jetter, Michael,Magnusson, Leandro M.,Roth, Sebastian
This paper presents evidence suggesting men's (but not women's) risk and time preferences have systematically become sensitive to local economic conditions since the 2008 financial crisis. Studying longitudinal, nationally representative data for 22,579 Australian-based respondents in up to 11 surveys from 2002-2015, men respond with increased risk aversion and impatience to a rise in their region's unemployment rate â€" but only since 2008. We find no such relationship for women or before the crisis. This conclusion persists when accounting for individual-level fixed effects, demographics, national economic conditions, the individual's employment situation, income, wealth, as well as region- and time-specific unobservables. Exploring a potential mechanism, higher regional unemployment rates are also linked to men (but not women) being more unhappy since 2008. This 'happiness channel' only partially explains the link between the local unemployment rate and risk preferences.

Bolsa en España. ITBM: 1940-2020 (27marzo), (Spanish Stock Exchange. ITBM. 1940-27 March 2020)
Fernandez, Pablo,de Apellániz, Eduardo
Spanish Abstract: El descenso de la Bolsa en España (ITBM) en el mes de marzo (hasta el viernes 27) ha sido un 22,9%. Es un importante descenso, pero los ha habido (de momento) mayores. Observando la evolución bursátil de los últimos 80 años, se corrobora que la bolsa española ya ha tenido descensos muy superiores a los del mes de marzo de 2020.Se muestra la evolución del ITBM desde 1940 hasta el 27 de marzo de 2020. Se utiliza el Índice Total de la Bolsa de Madrid (ITBM) porque el IBEX 35 sólo tiene 31 años de historia. Se muestra que la evolución del ITBM y del IBEX 35 con dividendos es prácticamente igual.English Abstract: The decrease in the Spanish Stock Market (ITBM) in the month of March (until Friday 27) has been 22.9%. It is a significant decline, but there have been (so far) greater. Observing the stock market evolution of the last 80 years, it is corroborated that the Spanish stock market has already had declines much higher than those of March 2020. The evolution of the ITBM from 1940 to March 27, 2020 is shown. The Total Index of the Madrid Stock Exchange (ITBM) is used because the IBEX 35 has only 31 years of history. The evolution of the ITBM and the IBEX 35 with dividends is shown to be practically the same.

By Force of Habit: Self-Trapping in a Dynamical Utility Landscape
José Moran,Antoine Fosset,Davide Luzzati,Jean-Philippe Bouchaud,Michael Benzaquen

Historically, rational choice theory has focused on the utility maximization principle to describe how individuals make choices. In reality, there is a computational cost related to exploring the universe of available choices and it is often not clear whether we are truly maximizing an underlying utility function. In particular, memory effects and habit formation may dominate over utility maximisation. We propose a stylized model with a history-dependent utility function where the utility associated to each choice is increased when that choice has been made in the past, with a certain decaying memory kernel. We show that self-reinforcing effects can cause the agent to get stuck with a choice by sheer force of habit. We discuss the special nature of the transition between free exploration of the space of choice and self-trapping. We find in particular that the trapping time distribution is precisely a Zipf law at the transition, and that the self-trapped phase exhibits super-aging behaviour.

Connectivity and Savings Propensity among Odisha Tribals
Viswanath, P.V.
Rural areas in India are underdeveloped relative to urban areas. One result of this has been a migration to urban areas; such migration, in turn, sharpens the problem of sustainability of rural communities. Even though there are some social benefits to urbanization, there are also disadvantages; and if urban migration is rapid and unplanned, social services in urban areas are likely to be strained. Given this, it is important to pursue strategies to develop rural areas. Such solutions can either involve external intervention with outside resources or alternatively, development using internal resources. The second solution is clearly more sustainable, as well as politically more feasible. An important part of such a self-reliant strategy involves rural saving. This paper uses the results of a survey to examine the factors affecting saving in a rural part of Odisha populated primarily by tribals. Our tentative findings are that savings propensity is determined partly by the extent to which individuals feel connected to the broader economy, and partly by cultural factors. One implication of these findings is that connecting rural areas to other, possibly urban, locations could elicit greater saving and this could lead to greater development, employment possibilities, economic betterment and all the consequent social welfare implications.

Coupled criticality analysis of inflation and unemployment
Z. Koohi Lai,A. Namaki,A. Hosseiny,G.R. Jafari,M. Ausloos

In this paper, we are interested to focus on the critical periods in the economy which are characterized by large fluctuations in macroeconomic indicators.

To capture unusual and large fluctuations of inflation and unemployment, we concentrate on the non-Gaussianity of their distributions.

To this aim, by using the coupled multifractal approach, we analyze US data for a period of 70 years from 1948 until 2018 and measure the non-Gausianity of the distributions. Then, we investigate how the non-Gaussianity of the variables affects the coupling structure of them. By applying the multifractal method, one can see that the non-Gaussianity depends on the scales. While the non-Gaussianity of unemployment is noticeable only for periods smaller than 1 year and for longer periods tends to Gaussian behavior, the non-Gaussianities of inflation persist for all time scales. Also, it is observed that the coupling structure of these variables tends to a Gaussian behavior after $2$ years.

Data Science in Economics
Saeed Nosratabadi,Amir Mosavi,Puhong Duan,Pedram Ghamisi

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets
J.M. Calabuig,H. Falciani,E.A. Sánchez-Pérez

We consider a quasi-metric topological structure for the construction of a new reinforcement learning model in the framework of financial markets. It is based on a Lipschitz type extension of reward functions defined in metric spaces. Specifically, the McShane and Whitney extensions are considered for a reward function which is defined by the total evaluation of the benefits produced by the investment decision at a given time. We define the metric as a linear combination of a Euclidean distance and an angular metric component. All information about the evolution of the system from the beginning of the time interval is used to support the extension of the reward function, but in addition this data set is enriched by adding some artificially produced states. Thus, the main novelty of our method is the way we produce more states -- which we call "dreams" -- to enrich learning. Using some known states of the dynamical system that represents the evolution of the financial market, we use our technique to simulate new states by interpolating real states and introducing some random variables. These new states are used to feed a learning algorithm designed to improve the investment strategy by following a typical reinforcement learning scheme.

Employment Practices Liability Insurance and Ex Post Moral Hazard
Meyers, Erin E.,Hersch, Joni
Employment Practices Liability Insurance (EPLI) is a form of insurance that protects employers from claims of discrimination, harassment, retaliation, and wrongful termination. EPLI contracts as currently written often do not exclude intentional actions or payment of punitive damages, creating potentially severe moral hazard problems. We propose potential alterations that would hold employers accountable for prohibited employment acts when upper management is involved, while still allowing the EPLI market to reduce risk to employers and help compensate victims. Specifically, the extent of employers’ fault, as evidenced through upper-management involvement, should correlate with their direct payment of damages. Three potential insurance-related options could achieve this goal. These include, in cases where upper-management is involved, (1) giving the insurer the right to pursue subrogation against the employer, (2) mandating that the employer pay a minimum coinsurance rate, and (3) granting the EEOC power to pursue uninsurable fines in the most egregious cases.

Equilibrium Effects of Intraday Order-Splitting Benchmarks
Jin Hyuk Choi,Kasper Larsen,Duane J. Seppi

This paper presents a continuous-time model of intraday trading, pricing, and liquidity with dynamic TWAP and VWAP benchmarks. The model is solved in closed-form for the competitive equilibrium and also for non-price-taking equilibria. The intraday trajectories of TWAP trading targets cause predictable intraday patterns of price pressure, and randomness in VWAP target trajectories induces additional randomness in intraday price-pressure patterns. TWAP and VWAP trading both reduce market liquidity and increase price volatility relative to just terminal trading targets alone. The model is computationally tractable, which lets us provide a number of numerical illustrations.

Event Study Methodology and The Computation of Damages for Secondary Market Misrepresentations: Striving for a Technicolor Palette
MacIntosh, Jeffrey G.
The use of event study methodology (“ESM”) for computing damages for secondary market misrepresentations is poorly understood by lawyers, judges, and policy makers in Canada. This paper reviews the economic foundations of ESM and discusses the various pitfalls that may arise, particularly in a market that is not informationally efficient, so that share prices respond slowly to new information. In such a market, the calculation of beta may be rendered problematic due to asynchronicity or the occurrence of confounding corporate events in the estimation period. While in an efficient market, a one- or two-day event window is often quite adequate, this is not the case in an informationally inefficient market. In that case, the event window will often be much longer; in addition, it must be tailored to the particular issuer and the facts at hand. The longer event window creates a far greater probability that confounding events will contaminate the data. Moreover, because long event windows typically exhibit a lower signal to noise ratio (particularly if trading is dominated by retail noise traders), the power of the test may be materially reduced, increasing the likelihood of a type II error and making it more difficult to establish statistical significance. In the context of civil litigation where the standard of proof is a balance of probabilities, and keeping in mind that there is a trade-off between type I and type II errors, it may be appropriate to set the level of statistical significance at 0.10, rather than the usual 0.05. Whether the market is efficient or inefficient, however, care must be taken to determine if either insider trading or rational market anticipation has moved the stock price prior to a corrective announcement, and the event window adjusted appropriately. While there is a dearth of empirical studies, I present evidence that many Canadian public companies do not trade in an informationally efficient market, such that many of the above-noted complications will often arise in the use of ESM. Finally, I examine the scheme for computing secondary market damages under the Ontario Securities Act (“OSA”). The OSA mandates the use of mechanical rules that are almost certain to materially mis-estimate damages in both efficient and inefficient markets.

Financial Inclusion, At What Cost? : Quantification of Economic Viability of A Supply Side Roll Out
Markose, Sheri M.,Arun, Thankom,Ozili, Peterson K
The Prime Minster Jan-Dhan Yojna (PMJDY), started in 2014, follows in a long line of drives for financial inclusion in India, marked only by a much greater scope and ambition than previous roll outs. This top down approach to close the gap on the unbanked of India relies primarily on public sector banks with targets set for rural outreach. We develop an innovative approach using cross sectional bank level data from 2014 till 2017 to quantify the incentives and costs involved in targeting unbanked households. This gives a monetary estimate of the economic shortfalls or surpluses for participating banks, measured as bank balances relative to outlay costs and subsidies per PMJDY beneficiary. We model the double bind problem faced by banks to achieve economies of scale that arise from spreading the fixed infrastructure costs over the number of below poverty line (BPL) customers when there is a dearth of balances in these accounts. This lack of economic viability of PMJDY accounts is found in most public sector banks, a matter which is problematic in view of their extant financial fragility in India. We provide evidence for cross subsidization of rural bank accounts by urban accounts. We give estimates using fixed effects panel methods as to what cost public sector banks bear and also quantify the extent to which account ineffectiveness is ameliorated with exogenous factors, primarily the tie up of PMJDY accounts with bio-metric Aadhar cards and electronic direct benefit transfer of G2P payments.

Firm Efforts to Improve Employee Quality and Corporate Investment Efficiency
Anagnostopoulou, Seraina C.,Avgoustaki, Argyro,Garcia Osma, Beatriz
We examine the effect of employee quality development on investment efficiency. Employees at all levels of an organization, as well as the corporate executives, may play a significant role for efficient corporate investment. Thus, human capital development efforts that improve employee quality should be associated with efficient corporate investment. However, the outcome of such provisions can be controversial, as they are often ineffective, costly, have dis-synergies and can deprive other types of investments of funding, which could lead to deviations from optimal investment. Using a large sample of US firms for the period 2002-2016, our findings reveal that human resource practices are negatively associated with investment efficiency, inducing both over- and under-investment. Results are driven mainly by human resource practices with a more direct cash cost, and are more pronounced with respect to investment in NonCapex projects. We interpret our evidence as consistent with agency motivations driving the practices geared towards employee development, and with such expenditure ultimately not aligning the interests of employees and shareholders.

Flying under the Radar: Confidential Filings and IPO Lawsuits
Esmer, Burcu,Ozel, N. Bugra,Sridharan, Suhas A.
Despite strong incentives to increase visibility and disclosure before initial public offerings (IPOs), many firms take advantage of the confidential filing provisions of the JOBS Act of 2012 to obscure their financial and non-financial information prior to IPOs. We posit that one potential reason for holding back information prior to an IPO is to reduce litigation. We focus not on shareholder litigation but on litigation from other sources (e.g. competitors, ex- and current employees, and patent trolls). We show that firms that publicly file their registration statement with the SEC experience a 25% increase in lawsuits during pre-IPO period, whereas a matched sample of firms that file confidentially under the provisions of the JOBS Act do not experience such an increase. The difference between the two groups is concentrated among lawsuits in which the plaintiff is a business, rather than an individual, and among lawsuits that are more likely to be meritless. There is no disproportionate increase in lawsuits for the confidential filers following the IPO, which suggests that withholding information during the IPO period mitigates, rather than delays, opportunistic litigation.

Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation
Ergun Yukseltan,Ahmet Yucekaya,Ayse Humeyra Bilge,Esra Agca Aktunc

Due to expensive infrastructure and the difficulties in storage, supply conditions of natural gas are different from those of other traditional energy sources like petroleum or coal. To overcome these challenges, supplier countries require take-or-pay agreements for requested natural gas quantities. These contracts have many pre-clauses; if they are not met due to low/high consumption or other external factors, buyers must completely fulfill them. A similar contract is then imposed on distributors and wholesale consumers. It is thus important for all parties to forecast their daily, monthly, and annual natural gas demand to minimize their risk. In this paper, a model consisting of a modulated expansion in Fourier series, supplemented by deviations from comfortable temperatures as a regressor is proposed for the forecast of monthly and weekly consumption over a one-year horizon. This model is supplemented by a day-ahead feedback mechanism for the forecast of daily consumption. The method is applied to the study of natural gas consumption for major residential areas in Turkey, on a yearly, monthly, weekly, and daily basis. It is shown that residential heating dominates winter consumption and masks all other variations. On the other hand, weekend and holiday effects are visible in summer consumption and provide an estimate for residential and industrial use. The advantage of the proposed method is the capability of long term projections and to outperform time series methods.

High Water, No Marks? Biased Lending after Extreme Weather
Garbarino, Nicola,Guin, Benjamin
Policymakers have put forward proposals to ensure that banks do not underestimate long-term risks from climate change. To examine how lenders account for extreme weather, we compare matched repeat mortgage and property transactions around a severe flood event in England in 2013â€"14. First, lender valuations do not ‘mark-to-market’ against local price declines. As a result valuations are biased upwards. Second, lenders do not offset this valuation bias by adjusting interest rates or loan amounts. Third, borrowers with low credit risk self-select into high flood risk areas. Overall, these results suggest that lenders do not track closely the impact of extreme weather exâ€'post, and that public flood insurance programs may subsidise high income households.

Large deviations for fractional volatility models with non-Gaussian volatility driver
Stefan Gerhold,Christoph Gerstenecker,Archil Gulisashvili

We study stochastic volatility models in which the volatility process is a function of a continuous fractional stochastic process, which is an integral transform of the solution of an SDE satisfying the Yamada-Watanabe condition. We establish a small-noise large deviation principle for the log-price, and, for a special case of our setup, obtain logarithmic call price asymptotics for large strikes.

Network-Based Measures of Systemic Risk in Korea
Choi, Jaewon,Lee, Jieun
English Abstract: We estimate systemic risk in the Korean economy using the econometric measures of commonality and connectedness applied to stock returns. To assess potential systemic risk concerns arising from the high concentration of the economy in large business groups and a few export-oriented sectors, we perform three levels of estimation using individual stocks, business groups, and industry returns. Our results show that the measures perform well over our sample period by indicating heightened levels of commonality and interconnectedness during crisis periods. In out-of-sample tests, we show that the measures can predict future losses in the stock market during the crises. We also provide the recent readings of our measures, both at the market, chaebol, and industry levels. The measures indicate systemic risk is currently not a major concern in Korea, as they tend to be at the lowest level since 1998. Systemic risk within-chaebols or within-industries overall has not significantly increased in the recent sub-period. In contrast, commonality within the finance industry has not subsided, which we interpret as capturing the interconnectedness endemic to the finance industry, rather than indicating a heightened systemic risk within the banking sector.

On the parabolic equation for portfolio problems
Dariusz Zawisza

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.

Online Appendix for: 'Listing Gaps, Merger Waves, and the Privatization of American Equity Finance'
Lattanzio, Gabriele,Megginson, William L.,Sanati, Ali
This appendix contains supplementary material to the analyses reported in the paper. Section 1 presents tests for the hypothesis that shifts in technology and industry composition might have played a key role at causing the U.S. listing gap. By replicating our core analysis at the industry level we find no evidence that the dynamics of the number of listing is driven by industry specific shocks. Section 2 replicates our “U.S. capitalization premium” analyses after explicitly controlling for an aggregate measure of corporate profitability. Furthermore, it reports formal tests to assess whether intangible capital formation might explain the estimated U.S. capitalization premium. Section 3 compares in a uni-variate setting the historical M&A activity in the U.S. and in non-U.S. countries using different sub-samples of M&A transactions. Finally, section 4 tests the stability of the vector auto-regression model reported in Section 4.1. of the paper.

Optimal Behaviour in Solar Renewable Energy Certificate (SREC) Markets
Arvind Shrivats,Sebastian Jaimungal

SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certificates against the floor and pay a penalty for any lacking certificates. Certificates are tradable assets, allowing firms to purchase/sell them freely. In this work, we formulate a stochastic control problem for generating and trading in SREC markets from a regulated firm's perspective. We account for generation and trading costs, the impact both have on SREC prices, provide a characterization of the optimal strategy, and develop a numerical algorithm to solve this control problem. Through numerical experiments, we explore how a firm who acts optimally behaves under various conditions. We find that an optimal firm's generation and trading behaviour can be separated into various regimes, based on the marginal benefit of obtaining an additional SREC, and validate our theoretical characterization of the optimal strategy. We also conduct parameter sensitivity experiments and conduct comparisons of the optimal strategy to other candidate strategies.

Optimal periodic dividend strategies for spectrally positive L\'evy risk processes with fixed transaction costs
Benjamin Avanzi,Hayden Lau,Bernard Wong

We consider the general class of spectrally positive L\'evy risk processes, which are appropriate for businesses with continuous expenses and lump sum gains whose timing and sizes are stochastic. Motivated by the fact that dividends cannot be paid at any time in real life, we study $\textit{periodic}$ dividend strategies whereby dividend decisions are made according to a separate arrival process.

In this paper, we investigate the impact of fixed transaction costs on the optimal periodic dividend strategy, and show that a periodic $(b_u,b_l)$ strategy is optimal when decision times arrive according to an independent Poisson process. Such a strategy leads to lump sum dividends that bring the surplus back to $b_l$ as long as it is no less than $b_u$ at a dividend decision time. The expected present value of dividends (net of transaction costs) is provided explicitly with the help of scale functions. Results are illustrated.

Propaganda, Alternative Media, and Accountability in Fragile Democracies
Anqi Li,Davin Raiha,Kenneth W. Shotts

We develop a model of electoral accountability with mainstream and alternative media. In addition to regular high- and low-competence types, the incumbent may be an aspiring autocrat who controls the mainstream media and will subvert democracy if retained in office. A truthful alternative media can help voters identify and remove these subversive types while re-electing competent leaders. A malicious alternative media, in contrast, spreads false accusations about the incumbent and demotivates policy effort. If the alternative media is very likely be malicious and hence is unreliable, voters ignore it and use only the mainstream media to hold regular incumbents accountable, leaving aspiring autocrats to win re-election via propaganda that portrays them as effective policymakers. When the alternative media's reliability is intermediate, voters heed its warnings about subversive incumbents, but the prospect of being falsely accused demotivates effort by regular incumbents and electoral accountability breaks down.

Protecting Pegged Currency Markets from Speculative Investors
Eyal Neuman,Alexander Schied

We consider a stochastic game between a trader and a central bank in a target zone market with a lower currency peg. This currency peg is maintained by the central bank through the generation of permanent price impact, thereby aggregating an ever increasing risky position in foreign reserves. We describe this situation mathematically by means of two coupled singular control problems, where the common singularity arises from a local time along a random curve. Our first result identifies a certain local time as that central bank strategy for which this risk position is minimized. We then consider the worst-case situation the central bank may face by identifying that strategy of the strategic investor that maximizes the expected inventory of the central bank under a cost criterion, thus establishing a Stackelberg equilibrium in our model.

Readability of Notes to Consolidated Financial Statements and Corporate Bond Yield Spread
Chen, Tsung-Kang,Tseng, Yi-Jie
We examine the association between readability of ‘Notes to consolidated financial statements’ (Notes) in annual reports and corporate bond yield spread. We find that less readable narrative disclosure of Notes is significantly associated with greater bond yield spread. In addition, the association becomes stronger for firms in high-tech sectors or those with higher equity volatility, whereas it becomes weaker for firms with higher profitability or those with the reporting location of Notes being outside of the 10-K format file. We also provide evidence that Notes readability is helpful in explaining the puzzle of a positive yield spread always appearing when the bond approaches its maturity in practice. Overall, our results suggest that the Notes readability has a substantial association with bond yield spread and its term structure.

Relative Arbitrage: Sharp Time Horizons and Motion by Curvature
Martin Larsson,Johannes Ruf

We characterize the minimal time horizon over which any market with $d \geq 2$ stocks and sufficient intrinsic volatility admits relative arbitrage. If $d \in \{2,3\}$, the minimal time horizon can be computed explicitly, its value being zero if $d=2$ and $\sqrt{3}/(2\pi)$ if $d=3$. If $d \geq 4$, the minimal time horizon can be characterized via the arrival time function of a geometric flow of the unit simplex in $R^d$ that we call the minimum curvature flow.

Rethinking Financial Repression and Implicit Guarantee in China
An, Ping
I construct a model that can explain nearly all financial repression phenomena and main financial market equilibria in China. The model gets two insights: Foremost, the “financial repression” in China roots in the repressed household and state-owned enterprise (SOE) sectors rather than the finance sector. Against this background, the implicit guarantee is not the main risk of financial stability: On one hand, the implicit guarantee is an exogenous institutional factor, it already been contained in asset prices; on the other hand, the household and SOE’s repressions alleviate the implicit guarantee risk. If breaking the implicit guarantee, it will cause financial turmoil and households will bear the cost. To stabilize the financial system, the central bank needs to prevent redemption between banks and to recapitalize banks; while the effect of lender-of-last-resort is limited. If keeping the implicit guarantee, through improving liquidity in the inter-bank market and relaxing the SOE repression can raise financial market efficiency remarkably. Therefore, reform coordination between different sectors is more important for today’s China.

Scaling in Income Inequalities and its Dynamical Origin
Zoltan Neda,Istvan Gere,Tamas S. Biro,Geza Toth,Noemi Derzsy

We provide an analytically treatable model that describes in a unified manner income distribution for all income categories. The approach is based on a master equation with growth and reset terms. The model assumptions on the growth and reset rates are tested on an exhaustive database with incomes on individual level spanning a nine year period in the Cluj county (Romania). In agreement with our theoretical predictions we find that income distributions computed for several years collapse on a master-curve when a properly normalised income is considered. The Beta Prime distribution is appropriate to fit the collapsed data and it is shown that distributions derived for other countries are following similar trends with different fit parameters. The non-universal feature of the fit parameters suggests that for a more realistic modelling the model parameters have to be linked with specific socio-economic regulations.

Scheduling of Flexible Non-Preemptive Loads
Nathan Dahlin,Rahul Jain

A market consisting of a generator with thermal and renewable generation capability, a set of non-preemptive loads (i.e., loads which cannot be interrupted once started), and an independent system operator (ISO) is considered. Loads are characterized by durations, power demand rates and utility for receiving service, as well as disutility functions giving preferences for time slots in which service is preferred. Given this information, along with the generator's thermal generation cost function and forecast renewable generation, the social planner solves a mixed integer program to determine a load activation schedule which maximizes social welfare. Assuming price taking behavior, we develop a competitive equilibrium concept based on a relaxed version of the social planner's problem which includes prices for consumption and incentives for flexibility, and allows for probabilistic allocation of power to loads. Considering each load as representative of a population of identical loads with scaled characteristics, we demonstrate that the relaxed social planner's problem gives an exact solution to the original mixed integer problem in the large population limit, and give a market mechanism for implementing the competitive equilibrium.

SeLFIES: A New Pension Bond and Currency for Retirement
Merton, Robert C.,Muralidhar, Arun
There is a looming retirement crisis, as individuals are increasingly being asked to take responsibility for their own retirement planning and a majority of these individuals are financially unsophisticated. They cannot perform basic compounding calculations and do not understand the impact of inflation, both critical aspects of retirement planning. Yet, these individuals are being tasked with the responsibility for three complex, interconnected decisions: how much to save, how to invest (with many additional decisions), and how to decumulate one’s portfolio at retirement. Compounding these challenges, current financial instruments and products (e.g. T-Bills, TIPs, or Target Date Funds) are risky because they focus on the wrong goal - wealth at retirement, as opposed to how much retirement income can be guaranteed to support pre-retirement standard-of-living. Moreover, annuities are complex, costly, and illiquid and seldom used. Without financial innovation and a change in the metric for measuring retirement success, many individuals will retire poor â€" a financially and socially undesirable outcome for any country. This paper presents an easy, quick and efficient solution for countries to address all these challenges and improve retirement security by creating and issuing an innovative new bond â€" SeLFIES (Standard-of-Living indexed, Forward-starting, Income-only Securities). The SeLFIES bond is a single, liquid, low-cost, low-risk instrument, easy-to-understand for even the most financially unsophisticated individual, because it embeds accumulation, decumulation, compounding and inflation-adjustments. SeLFIES is good for governments too, as the bond lowers the risk of individuals retiring poor, improves balance sheet management, and funds infrastructure. The paper also discusses key design aspects of SeLFIES to show how they can ensure longevity risk protection and hedge standard-of-living risk, a key unmanaged risk globally today. Additionally, the paper by concludes by demonstrating the universality of the SeLFIES design as well as by showing how it serves a useful purpose by becoming the “currency of retirement.”

Semi-closed form solutions for barrier and American options written on a time-dependent Ornstein Uhlenbeck process
Peter Carr,Andrey Itkin

In this paper we develop a semi-closed form solutions for the barrier (perhaps, time-dependent) and American options written on the underlying stock which follows a time-dependent OU process with a log-normal drift. This model is equivalent to the familiar Hull-White model in FI, or a time dependent OU model in FX. Semi-closed form means that given the time-dependent interest rate, continuous dividend and volatility functions, one need to solve numerically a linear (for the barrier option) or nonlinear (for the American option) Fredholm equation of the first kind. After that the option prices in all cases are presented as one-dimensional integrals of combination of the above solutions and Jacobi theta functions. We also demonstrate that computationally our method is more efficient than the backward finite difference method used for solving these problems, and can also be as efficient as the forward finite difference solver while providing better accuracy and stability.

Stock Price Response to Earnings Announcements in a Major Emerging Economy
Al-Baidhani د. احمد البيضاني, Dr. Ahmed
The aim of this study is to evaluate the relevance and usefulness of accounting information, specifically earnings announcements, as the key determinant for stock price changes. The main objective is to investigate whether ERC behavior could explain more fully the stock price changes, as to the reason why the stock price change is not equal to the amount of announced earnings. The study reports significant findings from applying portfolio method, which shows major stock price reactions and very large earnings response coefficients to accounting earnings disclosed to the stock exchange markets of Mexico as a major emerging economy, for the period 2001-2014. The abnormal returns to disclosed events are significantly positive when earnings increase and negative when earnings decrease. Two measures of abnormal returns are regressed against the size of the announced earnings. The first regression uses measures from individual events. The second regression uses a new measure; that is, from portfolios made of all observations sorted by size of earnings into ten portfolios. The portfolio method used was aimed at controlling possible idiosyncratic-errors-in-variables problem using individual event measures. The findings using individual-event measures resulted in reasonable-size earnings response coefficient with high R2 explanatory power, a little higher than those reported in prior studies on other countries. Meanwhile, the portfolio method led to a much bigger size of earnings response coefficient that strongly support the value relevance accounting theory. This finding is new to this literature.

Streaming Perspective in Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data
Vladimír Holý,Petra Tomanová

We investigate the computational issues related to the memory size in the estimation of quadratic covariation using financial ultra-high-frequency data. In the multivariate price process, we consider both contamination by the market microstructure noise and the non-synchronous observations. We express the multi-scale, flat-top realized kernel, non-flat-top realized kernel, pre-averaging and modulated realized covariance estimators in a quadratic form and fix their bandwidth parameter at a constant value. This allows us to operate with limited memory and formulate such estimation approach as a streaming algorithm. We compare the performance of the estimators with fixed bandwidth parameter in a simulation study. We find that the estimators ensuring positive semidefiniteness require much higher bandwidth than the estimators without such constraint.

Structural developments in global financial intermediation: The rise of debt and non-bank credit intermediation
Patalano, Robert,Roulet, Caroline
This paper examines global credit intermediation through the lens of financial markets and financial intermediaries in the post-crisis period during which highly accommodative monetary policies contributed to investors' search for yield. It reviews the extent to which non-bank intermediation contributed to the rise of sovereign and corporate debt levels and exuberance in global credit markets. It also assesses forms of market-based finance that are contributing to financial vulnerabilities, including leverage loans and collateralised loan obligations (CLOs), fixed-income investment funds, and bank contingent convertible debt. Post-crisis policy frameworks should adapt to the shift toward market-based finance in many countries to allow better consideration of the interactions between monetary, prudential, and regulatory tools with respect to credit intermediation and risks. Policies should also consider the optimal combination of macroprudential and activities-based tools in non-bank credit intermediation to address vulnerabilities without undermining the benefits of market-based finance.

Sustainable Banking; Evaluation of the European Business Models
Saeed Nosratabadi,Gergo Pinter,Amir Mosavi,Sandor Semperger

Sustainable business models also offer banks competitive advantages such as increasing brand reputation and cost reduction. However, no framework is presented to evaluate the sustainability of banking business models. To bridge this theoretical gap, the current study using A Delphi-Analytic Hierarchy Process method, firstly, developed a sustainable business model to evaluate the sustainability of the business model of banks. In the second step, the sustainability performance of sixteen banks from eight European countries including Norway, the UK, Poland, Hungary, Germany, France, Spain, and Italy, assessed. The proposed business model components of this study were ranked in terms of their impact on achieving sustainability goals. Consequently, the proposed model components of this study, based on their impact on sustainability, are respectively value proposition, core competencies, financial aspects, business processes, target customers, resources, technology, customer interface, and partner network. The results of the comparison of the banks studied by each country disclosed that the sustainability of the Norwegian and German banks business models is higher than in other counties. The studied banks of Hungary and Spain came in second, the banks of the UK, Poland, and France ranked third, and finally, the Italian banks ranked fourth in the sustainability of their business models.

Tall wheatgrass (Thinopyrum ponticum (Podp)) in a real farm context, a sustainable perennial alternative to rye (Secale cereale L.) cultivation in marginal lands
Carlos S.Ciria,Carlos M.Sastre,Juan Carrasco,Pilar Ciria

In order to face the expected increasing demand of energy crops without creating conflicts of land occupation sustainability, farmers need to find reliable alternatives in marginal agricultural areas where the production of food hardly ever is economically and environmentally sustainable. The purpose of this work was the study of the viability of the introduction of new non food crops in marginal areas of real farms. This study compares the profit margin and the energy and environmental performance of growing tall wheatgrass, in the marginal area of a rainfed farm versus rye, the annual crop sowed traditionally in the marginal area of the farm. The cited farm owned 300 ha of which about 13 percent was marginal. The methodology was based on the use of the profit margin of the crops as indicator for the economic assessment and Life Cycle Assessment LCA as technique for the energy and the environmental evaluations. Results of the economic analysis showed a slight enhancement of the profit margin for tall wheatgrass 156 Euro ha-1 y-1 compared to rye 145 Euro ha-1 y-1. Environmental LCA was driven by CO2 fixation due to soil organic matter increase and reduced inputs consumption for tall wheatgrass that produced a Global Warming Potential GWP of -1.9 Mg CO2 eq ha-1 y-1 versus 1.6 Mg CO2 eq ha-1 y-1 obtained for rye. Tall wheatgrass cultivation primary energy consumption was less than 40 percent of rye s consumption. According to the results achieved it was concluded that tall wheatgrass is better option than rye from the energy and the environmental point of views and slight better option from the economic view. Considering these results, monetarization of the CO2 eq reductions of tall wheatgrass compared to rye is essential to improve its profit margin and promote the implantation of this new crop in marginal areas of farms.

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

While the coronavirus spreads around the world, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations have plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the expected economic shocks. To achieve higher effectiveness with cyclical and fiscal 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 the industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This data set 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 by weeks. The risk reports differ substantially between industries, both in magnitude and in nature. Based on natural language processing techniques, we can identify corona-related risk topics and their perceived relevance for different industries. Our approach allows to distinguish the industries by their reported risk awareness towards COVID-19. The preliminary findings are summarised an online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool could provide relevant empirical data to inform models on the immediate economic effects of the crisis. Such complementary empirical information could help policy-makers to effectively target financial support and to mitigate the economic shocks of the current crisis.

The Effect of Possible EU Diversification Requirements on the Risk of Banks’ Sovereign Bond Portfolios
Craig, Ben R.,Giuzio, Margherita,Paterlini, Sandra
Recent policy discussion includes the introduction of diversification requirements for sovereign bond portfolios of European banks. In this paper, we evaluate the possible effects of these constraints on risk and diversification in the sovereign bond portfolios of the major European banks. First, we capture the dependence structure of European countries' sovereign risks and identify the common factors driving European sovereign CDS spreads by means of an independent component analysis. We then analyse the risk and diversification in the sovereign bond portfolios of the largest European banks and discuss the role of “home bias”, i.e. the tendency of banks to concentrate their sovereign bond holdings in their domicile country. Finally, we evaluate the effect of diversification requirements on the tail risk of sovereign bond portfolios. Under our assumptions about how banks rebalance their portfolio to respond to the new requirements, demanding that banks modify their holdings to increase their portfolio diversification may be ineffective in reducing portfolio risk, including tail risk.

The Influence of Customer Relationship Management (CRM) Indicators on Customer Loyalty of Sharia Based Banking System
Lubis, Adelina
Objective - The purpose of this research is to determine whether CRM (Customer Relationship Management) indicators, namely complaint resolution, customer orientation, customer empowerment and customer knowledge affect the loyalty of sharia bank customers in North Sumatra. Methodology/Technique - The sample of this study is 120 Islamic banking customers in North Sumatra, namely customers at PT. BNI Syariah Tbk, PT. Bank Syariah Mandiri Tbk and PT. BRI Syariah Tbk. The analytical method used is multiple linear regression analysis. Findings - The results of this study are as partial complaint resolution, customer orientation, customer empowerment and customer knowledge variables have a significant effect on customer loyalty in Islamic Banking in North Sumatra and the hypothesis is accepted. The better CRM that is owned and implemented by Islamic Banking in North Sumatra will have an effect on increasing customer loyalty. Simultaneously complaint resolution, customer orientation, customer empowerment and customer knowledge variables significantly influence customer loyalty in Islamic Banking in North Sumatra and the hypothesis is accepted. Type of Paper - Empirical

The Trading Response of Individual Investors to Local Bankruptcies
Laudenbach, Christine,Loos, Benjamin,Pirschel, Jenny,Wohlfart, Johannes
We use data from a German online brokerage and a survey to show that retail investors sharply reduce risk-taking in response to nearby firm bankruptcies, which are not predictive of returns. The effects on trading are spatially highly concentrated, immediate and not persistent. They seem to operate through more pessimistic expected returns and increased risk aversion and do not reflect wealth effects or changes in background risks. Investors learn about bankruptcies through immediate coverage in local newspapers. Our findings suggest that non-informative local experiences that make downside risks of stock investment more salient contribute to idiosyncratic short-term fluctuations in trading.

Using News Articles and Financial Data to predict the likelihood of bankruptcy
Michael Filletti,Aaron Grech

Over the past decade, millions of companies have filed for bankruptcy. This has been caused by a plethora of reasons, namely, high interest rates, heavy debts and government regulations. The effect of a company going bankrupt can be devastating, hurting not only workers and shareholders, but also clients, suppliers and any related external companies. One of the aims of this paper is to provide a framework for company bankruptcy to be predicted by making use of financial figures, provided by our external dataset, in conjunction with the sentiment of news articles about certain sectors. News articles are used to attempt to quantify the sentiment on a company and its sector from an external perspective, rather than simply using internal figures. This work builds on previous studies carried out by multiple researchers, to bring us closer to lessening the impact of such events.