Research articles for the 2020-10-11

AdaVol: An Adaptive Recursive Volatility Prediction Method
Nicklas Werge,Olivier Wintenberger

Quasi-Maximum Likelihood (QML) procedures are theoretically appealing and widely used for statistical inference. While there are extensive references on QML estimation in batch settings, the QML estimation in streaming settings has attracted little attention until recently. An investigation of the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model is conducted, and the classical batch optimization routines extended to the framework of streaming and large-scale problems. An adaptive recursive estimation routine for GARCH models named AdaVol is presented. The AdaVol procedure relies on stochastic approximations combined with the technique of Variance Targeting Estimation (VTE). This recursive method has computationally efficient properties, while VTE alleviates some convergence difficulties encountered by the usual QML estimation due to a lack of convexity. Empirical results demonstrate a favorable trade-off between AdaVol's stability and the ability to adapt to time-varying estimates for real-life data.

An Application of Deep Reinforcement Learning to Algorithmic Trading
Thibaut Théate,Damien Ernst

This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.

Bank Liquidity Provision Across the Firm Size Distribution
Chodorow-Reich, Gabriel,Darmouni, Olivier,Luck, Stephan,Plosser, Matthew C.
Using loan-level data covering two-thirds of all corporate loans from U.S. banks, we document that SMEs (i) obtain much shorter maturity credit lines than large firms; (ii) have less active maturity management and therefore frequently have expiring credit; (iii) post more collateral on both credit lines and term loans; (iv) have higher utilization rates in normal times; and (v) pay higher spreads, even conditional on other firm characteristics. We present a theory of loan terms that rationalizes these facts as the equilibrium outcome of a trade-off between commitment and discretion. We test the model's prediction that small firms may be unable to access liquidity when large shocks arrive using data on drawdowns in the COVID recession. Consistent with the theory, the increase in bank credit in 2020Q1 and 2020Q2 came almost entirely from drawdowns by large firms on pre-committed lines of credit. Differences in demand for liquidity cannot fully explain the differences in drawdown rates by firm size, as we show that large firms also exhibited much higher sensitivity of drawdowns to industry-level measures of exposure to the COVID recession. Finally, we match the bank data to a list of participants in the Paycheck Protection Program (PPP) and show that SME recipients of PPP loans reduced their non-PPP bank borrowing in 2020Q2 by between 53 and 125 percent of the amount of their PPP funds, suggesting that government-sponsored liquidity can overcome private credit constraints. 

Book-to-Market, Mispricing, and the Cross-Section of Corporate Bond Returns
Bartram, Söhnke M.,Grinblatt, Mark,Nozawa, Yoshio
We study the role played by “bond book-to-market” ratios in U.S. corporate bond pricing. Controlling for numerous risk factors tied to default and priced asset risk, including yield-to-maturity, we find that the ratio of a corporate bond’s book value to its market price strongly predicts the bond’s future return. The quintile of bonds with the highest bond book-to-market ratios outperforms the quintile with the lowest ratios by more than 3% per year, other things equal. Additional evidence on signal delay, scope of signal efficacy, and factor risk rejects the thesis that the corporate bond market is perfectly informationally efficient, although significant positive alpha spreads are erased by transaction costs.

COVID-19 Pandemic and Stress Testing the Eurozone Credit Portfolios
Yarovaya, Larisa,Mirza, Nawazish,Rizvi, Syed Kumail Abbas,Naqvi, Bushra
This research conduct stress tests to assess the impact of COVID-19 drag on loan portfolios of 255 credit institutions in ten most affected EU member states. We employ quarterly data at the firm level to segregate the credit exposures into seven categories of consumers and corporate borrowers. Based on the macro and micro level model specifications, and using six distressed economic scenarios, our results indicate a significant deterioration in asset quality across exposure types, institutional size, and countries’ profile. We also document a substantial increase in the probability of default and a considerable reduction in capital adequacy across our sample.

COVID-19 Times: Where Lies Financial Safety?
Virk, Nader,Coles, Dylan,Hagemense, Dominik
This study examines the impact of multiple facets of the COVID-19 (C-19) pandemic on return variation and volatility of different financial securities. These facets include morbidity and mortality rates; market sentiment and policy interventions. Results suggest that the effects of the wide-ranging policy responses have positively affected returns; however, it bears no effect on the unrelenting volatility â€" almost across all assets. We report that our C-19 index, fear gauges and sentiment index are plausible and significant predictors for asset return and volatility changes in the pre-pandemic announcement (PA) sample. The variations in return and volatility in USD, gold and bond premia are affected by infection fundamental and sentiment related changes across periods. When it comes to equities only changes in EUROSTOXX’s return and volatility changes are significantly predicted by infection data and policy changes. For the US equities only economic policy intervention influences return and volatility shifts in the post PA period. We conclude that predictions for bond premia and USD changes by investors sentiment display investors’ flight to safety across samples. Overall, the global price recovery in several financial securities is hinging on policy intervention and is adding to the future uncertainty, potential bubble formation and testing the tightness of USD’s reserve currency status when global equities and gold prices are simultaneously picking up.

Deep Reinforcement Learning for Asset Allocation in US Equities
Miquel Noguer i Alonso,Sonam Srivastava

Reinforcement learning is a machine learning approach concerned with solving dynamic optimization problems in an almost model-free way by maximizing a reward function in state and action spaces. This property makes it an exciting area of research for financial problems. Asset allocation, where the goal is to obtain the weights of the assets that maximize the rewards in a given state of the market considering risk and transaction costs, is a problem easily framed using a reinforcement learning framework. It is first a prediction problem for expected returns and covariance matrix and then an optimization problem for returns, risk, and market impact. Investors and financial researchers have been working with approaches like mean-variance optimization, minimum variance, risk parity, and equally weighted and several methods to make expected returns and covariance matrices' predictions more robust. This paper demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem, learning to solve the problem using time series and deep neural networks. We demonstrate this on daily data for the top 24 stocks in the US equities universe with daily rebalancing. We use a deep reinforcement model on US stocks using different architectures. We use Long Short Term Memory networks, Convolutional Neural Networks, and Recurrent Neural Networks and compare them with more traditional portfolio management. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks. In Finance, no training to test error generalization results come guaranteed. We can say that the modeling framework can deal with time series prediction and asset allocation, including transaction costs.

Effects of Socioeconomic Characteristics on Income and Financial Behavior Amidst the COVID-19 Pandemic in the Democratic Republic of the Congo
Safari, Kulondwa,Bisimwa, Aganze
The Democratic Republic of the Congo as many other countries across the world has been affected by the COVID-19 pandemic. The government of the country declared a state of emergency in March 2020, which restricted travels within and outside the country, schools were closed and many other public activities were suspended. The containment measures taken to limit the spread of this new virus had a negative impact on economic activities. The present study analyzes how different socioeconomic groups were affected financially by the adverse effects of the COVID-19 pandemic in the post conflict eastern region of the DRC. A sample of 312 individuals was selected in three provinces in the eastern part of the Democratic Republic of the Congo. Ordered logit models were applied. Findings reveal that the individuals who were mostly affected financially by the effects of COVID-19 were married, females, youth, public and private sector employees, self-employed individuals and low income earners. Their income has been reduced and they reduced their spending, savings and investments. The present study suggests that social interventions programs are needed and they should prioritize the most vulnerable socioeconomic groups.

Jump Models with delay -- option pricing and logarithmic Euler-Maruyama scheme
Nishant Agrawal,Yaozhong Hu

In this paper, we In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European option is obtained together with the hedging portfolios. The option price is evaluated analytically at the last delayed period by using the Fourier transformation technique. But in general there is no analytical expression for the option price. To evaluate the price numerically we then use the Monte-Carlo method. To this end we need to simulate the delayed stochastic differential equations with jumps. We propose a logarithmic numerical scheme to approximate the equation and prove that all the approximations remain positive and the rate of convergence of the scheme is proved to be 0.5.

Learning in a Small/Big World
Benson Tsz Kin Leung

Savage (1972) lays down the foundation of Bayesian decision theory, but asserts that it is not applicable in big worlds where the environment is complex. Using the theory of finite automaton to model belief formation, this paper studies the characteristics of optimal learning behavior in small and big worlds, where the complexity of the environment is low and high, respectively, relative to the cognitive ability of the decision maker. Confirming Savage's claim, optimal learning behavior is closed to Bayesian in small worlds but significantly different in big worlds. In addition, I show that in big worlds, the optimal learning behavior could exhibit a wide range of well-documented non-Bayesian learning behavior, including the use of heuristic, correlation neglect, persistent over-confidence, inattentive learning, and other behaviors of model simplification or misspecification. These results establish a clear and testable relationship between the prominence of non-Bayesian learning behavior, complexity and cognitive ability.

Legal Air Cover
Bolton, Patrick,Gulati, G. Mitu,Panizza, Ugo
The economic harm being caused by the novel coronavirus may soon result in multiple sovereign debtors moving into default territory. But the existing playbook for dealing with multi-sovereign emerging market debt crises is blank. The only debt crisis scenario we know is protracted country-by-country and contract-by-contract negotiated workouts. As of this writing, expert groups are working on the design of a mechanism to run multiple sovereign debt workouts simultaneously. Those designs, however, will take time to configure and get international buy-in. This paper sets forth some options to provide temporary legal protection to the debtor countries in the meantime; while they are in need of diverting resources toward COVID amelioration. This is the notion of "legal air cover". The options we propose involve ex post state intervention in debt contracts. They are extreme and may come with risks. But we show that in the case of Greece, when intervention such as we envision was necessary, there were no negative spillovers on periphery Eurozone debt markets associated with the Greek ex post modification of contract terms.

Mutual fund asset allocation during COVID-19
Jacob, Joshy,Gupta, Nilesh ,Gopalakrishnan, Balagopal
The paper examines the investment decisions of equity mutual funds during various stages of the COVID-19 pandemic with monthly portfolio holdings data. We find that mutual funds favoured stocks with larger size, lower beta, and higher financial flexibility during the early months of the crisis. The preference for relatively safe firms suggests a reallocation towards safer assets given the high uncertainty in the early phase of the crisis. We also find that mutual funds preferred growth firms over the value firms. It is likely that value firms with greater invested capital would be less resilient to the shock brought about by the crisis. Institutional investors have also favoured group affiliated firms throughout the crisis, reflective of the ability of group firms to wade through the protracted economic shock. The paper brings out key firm-level characteristics that impact the asset allocation of institutional investors during the pandemic.

Resilient Urban Housing Markets: Shocks vs. Fundamentals
Amine Ouazad

In the face of a pandemic, urban protests, and an affordability crisis, is the desirability of dense urban settings at a turning point? Assessing cities' long term trends remains challenging. The first part of this chapter describes the short-run dynamics of the housing market in 2020. Evidence from prices and price-to-rent ratios suggests expectations of resilience. Zip-level evidence suggests a short-run trend towards suburbanization, and some impacts of urban protests on house prices. The second part of the chapter analyzes the long-run dynamics of urban growth between 1970 and 2010. It analyzes what, in such urban growth, is explained by short-run shocks as opposed to fundamentals such as education, industrial specialization, industrial diversification, urban segregation, and housing supply elasticity. This chapter's original results as well as a large established body of literature suggest that fundamentals are the key drivers of growth. The chapter illustrates this finding with two case studies: the New York City housing market after September 11, 2001; and the San Francisco Bay Area in the aftermath of the 1989 Loma Prieta earthquake. Both areas rebounded strongly after these shocks, suggesting the resilience of the urban metropolis.

Roughness in spot variance? A GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures
Anine E. Bolko,Kim Christensen,Mikko S. Pakkanen,Bezirgen Veliyev

In this paper, we develop a generalized method of moments approach for joint estimation of the parameters of a fractional log-normal stochastic volatility model. We show that with an arbitrary Hurst exponent an estimator based on integrated variance is consistent. Moreover, under stronger conditions we also derive a central limit theorem. These results stand even when integrated variance is replaced with a realized measure of volatility calculated from discrete high-frequency data. However, in practice a realized estimator contains sampling error, the effect of which is to skew the fractal coefficient toward "roughness". We construct an analytical approach to control this error. In a simulation study, we demonstrate convincing small sample properties of our approach based both on integrated and realized variance over the entire memory spectrum. We show that the bias correction attenuates any systematic deviance in the estimated parameters. Our procedure is applied to empirical high-frequency data from numerous leading equity indexes. With our robust approach the Hurst index is estimated around 0.05, confirming roughness in integrated variance.

Scaling of inefficiencies in the U.S. equity markets: Evidence from three market indices and more than 2900 securities
John H. Ring IV,Colin M. Van Oort,David R. Dewhurst,Tyler J. Gray,Christopher M. Danforth,Brian F. Tivnan

Using the most comprehensive, commercially-available dataset of trading activity in U.S. equity markets, we catalog and analyze quote dislocations between the SIP National Best Bid and Offer (NBBO) and a synthetic BBO constructed from direct feeds. We observe a total of over 3.1 billion dislocation segments in the Russell 3000 during trading in 2016, roughly 525 per second of trading. However, these dislocations do not occur uniformly throughout the trading day. We identify a characteristic structure that features more dislocations near the open and close. Additionally, around 23% of observed trades executed during dislocations. These trades may have been impacted by stale information, leading to estimated opportunity costs on the order of $ 2 billion USD. A subset of the constituents of the S&P 500 index experience the greatest amount of opportunity cost and appear to drive inefficiencies in other stocks. These results quantify impacts of the physical structure of the U.S. National Market System.

The impact of COVID-19 on valuations of non-financial European firms
Rizvi, Syed Kumail Abbas,Yarovaya, Larisa,Mirza, Nawazish,Naqvi, Bushra
This paper assesses the impact of the COVID-19 pandemic on valuation of non-financial firms in the European Union (EU) using a stress testing scenario approach. Particularly, the paper investigates to what extent the COVID-19 may deteriorate the value of non-financial firms in the 10 EU countries in order to provide a robust anchor to policy makers in formulating strategic government interventions. We utilize a sample of 5342 listed non-financial firms across 10 EU member states that have consistent analyst coverage from 2010 to 2019. First, we estimate the input sensitivities of free cash flow and residual income models using a random effect panel employed to in-sample data. Second, based on these sensitivities, we compute the model driven ex post valuations and compare their robustness with actual price and analyst forecasts for the same period. Finally, we introduce multiple stress scenarios that may emanate from COVID-19, i.e. decline in expected sales and increase/decrease in cost of equity.Our findings show a significant loss in valuations across all sectors due to a possible decline in sales and increase in cost of equity. In the extreme cases, average firms in some sectors may lose up to 60% of their intrinsic value in one year. The results remained consistent regardless of the cash flow or residual income driven valuation. While the impact of global financial crisis (2007-2008) and European crisis (2010-2012) on non-financial firms is well-documented, this paper is the first study that analyzed the impact of the COVID-19 crisis on the non-financial firms’ valuation in the European Union and reports that pandemic is the main driver behind the shareholder value destruction.

Unexpected Returns on Bonds. The Case of the Pandemic Period in Poland
Olbrys, Joanna
Changes in the term structure of interest rates unknown a priori to investors induce unanticipated rates of return on all financial instruments, especially on bonds and bond portfolios. Unexpected returns on bond investments arise particularly often in economic downturns. During the COVID-19 pandemic period in Poland interest rates have been substantially reduced by Central Bank. Within three months, the WIBOR 1Y rate fell from 1.84% (February 27, 2020) to 0.3% (June 4, 2020). The consequences of the Central Bank decision have been crucial for investors. A one-time increase (decrease) in rates decreases (increases) the market value of assets. The literature offers some mathematical tools to investigate this problem. The aim of this study is to assess and analyse the influence of a considerable decline in spot rates on unexpected profits on Treasury bonds to support investors decisions and emphasize practical aspects of bond risk measurement. It is important to note that after the quite long time period of almost flat interest rate structure in Poland, the topic regarding bond risk caused by changes in interest rates has become significant again.

Which Firm Should We Save During COVID-19, Distressed Firms or Socially-Responsible Firms?
Lu, Fangzhou
What kind of firms should be saved by the government during economic downtown? Previous research indicates that firms with growth potential instead of zombie firms should be saved, and firms that are most distressed should be subsidized. This paper provides a new prospective and suggests that socially-responsible firms or firms which care about stakeholders should be saved. During an economic downtown, firms stop hiring regardless of their financial strength and act pre-cautiously, but firms that care about their stakeholders lay off less employees and keep hiring. For a one standard deviation increase in the percentage of population within a county that donated to a charity, the job posting number increases by 13% in that county during the COVID-19 period. At firm-level, I show that for a one standard deviation increase in a firm's CSR community score which largely measures a firm's donation level, a firm's job posting growth is 5% higher, 2.7% less likely to announce massive layoff plan and 3.5\% less likely to announce a hiring freeze policy. This result suggests that government subsidized loan program such as the Paycheck Protection Program (PPP) should target firms with good track record of corporate social responsibility performance.