# Research articles for the 2021-08-05

Ambiguity in a Pandemic Recession, Asset Prices, and Lockdown Policy
Morimoto, Keiichi,Suzuki, Shiba
SSRN
Using an asset pricing model of a multi-sector production economy with pandemic disasters, we explain the average stock price boom and significant cross-sectional variation of stock returns in the US and Japan during the COVID-19 pandemic recession. Two features of the pandemic, ambiguity and sector-specific shocks, are crucial determinants of the unusual asset price dynamics. Extending the model, we analyze the welfare effects of lockdown policies during pandemics for heterogeneous households and obtain the following results. Enforcing a lockdown improves the welfare of the asset-holders and the household working in the sector with positive sector-specific shocks. A Pareto optimal lockdown policy controls the tightness of the lockdown so as to maximize the welfare of the household working in the sector with negative sector- specific shocks.

An Introduction to: Robin Hui Huang, Fintech Regulation in China: Principles, Policies and Practices (Cambridge University Press, 2021)
Huang, (Robin) Hui
SSRN
This is a brief introduction to the book: Robin Hui Huang, Fintech Regulation in China: Principles, Policies and Practices (Cambridge University Press, 2021)This book is the first monograph on the regulation of financial technology (Fintech) in China. Fintech brings about paradigm changes to the traditional financial system, presenting both challenges and opportunities. At the international level, there has been a fierce competition for the coveted title of global Fintech hub. One of the key enablers of success in this race is regulation. As the world's leader in Fintech, China's regulatory experience is of both academic and practical significance. This book presents a systematic and contextualized account of China's Fintech regulation, and in doing so, tries to identify and analyze relevant institutional factors contributing to the development of the Chinese law. It also takes a comparative approach to critically evaluating the Chinese experience. The book illustrates why and how China's Fintech regulation has been developed, if and how it differs from the rest of the world, and what can be learned from the Chinese experience.

Antimonopoly regulation method in energy markets based on the Vickrey-Clarke-Groves mechanism
arXiv

We evaluate the applicability of the generic Vickrey-Clarke-Groves (VCG) mechanism as an antimonopoly measure against a profit-maximizing producer with market power operating a portfolio of generating units at the centralized two-settlement energy market. The producer may indicate in its bid not only the altered cost function but also the distorted values of the technical parameters of its generating units, which enter the system-wide constraints of the centralized dispatch optimization problem. To ensure the applicability of the VCG method in this setting, we identify an additional assumption on the changes of the feasible set of the centralized dispatch optimization problem induced by variations of the producer's technical parameters. In the framework of the generic VCG mechanism, we propose an antimonopoly regulation method based on a regulator estimate of the producer's truthful bid. If this estimate is exact, the producer's maximum profit coincides with that in the case of the truthful bidding when no antimonopoly measure is applied. If the estimate is not exact, the error affects neither the producer's (weakly) dominant bid nor its optimal nodal output but manifests itself in the total uplift payment. This ensures an efficient allocation in the form of the optimal output/consumption schedule and shields the (pre-uplift) market prices from the producer's market power. We compare the suggested method with the alternative antimonopoly regulation approach based on the replacement of the producer's bid by a bid composed by the regulator.

Buffetology's Use in Stock Trading Amid the COVID-19 Pandemic
Bhakti, Dwi,Widjaja, Hidayat Sofyan,Septyanto, Dihin
SSRN
The impact of the COVID-19 Pandemic has affected the whole of human life, including the world capital market and all its problems. COVID-19 has influenced trading patterns in the capital market, both in terms of trade rules that are superstructure and capital market infrastructure itself. Indonesia's economic turmoil is not only caused by the pandemic COVID-19, but also by several financial scandals such as the Jiwasraya, Asabari and several moral hazards by stock market brokers. The moment of capital outrage at the moment is like a honeymoon in stock trading that occurs during an extreme event. Some events that have caused the world capital market to fall deeply, are the Black Tuesday 1988, the monetary crisis in Indonesia and Asia in 1998, the global financial crisis in 2008, and most recently the pandemic COVID-19 phenomenon that began in 2020. The extent to which Buffettology's role in investment decision making by Investment managers in the Indonesian capital market is the aim of the author's research. The main finding of this study is the prospectus of vigilant leader company as a determinant in making investment decisions both in normal situations and in the midst of a pandemic. This research will be very essential for investment and financial decision makers and for the Financial Services Authority (OJK) in Indonesia. This is because the wrong decisions of investment managers, as well as the company's performance with a stock price characterized by Roller Coaster (called saham gorengan) is a special concern for OJK to take decisive action for the sustainability of a healthy and conducive Indonesia Capital Market for investors.

COVID-19 and Auto Loan Origination Trends
SSRN
We study the impact of the COVID-19 crisis on auto loan origination activity during 2020. We focus on the dynamic impact of the crisis across lending channels, Equifax Risk Score (Risk Score) segments, and relevant geographic characteristics such as urbanization rate. We measure a significant drop in auto loan originations in Marchâ€'April followed by a near rebound in Mayâ€'June. Originations remain slightly depressed until October and fall again in Novemberâ€'December. We document the largest drop and the smallest rebound in the subprime segment. We do not find any suggestive evidence that used car loan originations exhibited patterns significantly different from the rest of the market. We also document a more pronounced impact in the Northeast and the Pacific, seemingly influenced by the higher urbanization rate in these regions. Bank-financed originations experienced the largest drop and the smallest rebound, thus resulting in a loss of market share and continuing a 10-year trend of bank share loss in auto lending. We find that the drop in auto loans originated by banks was particularly significant among subprime borrowers. The impact of the COVID-19 crisis across origination channels contrasts with the experience during the Great Recession when banks contributed the largest support to the auto loan origination segment during periods of stress and finance company-originated auto loans were depressed.

CRPS Learning
Jonathan Berrisch,Florian Ziel
arXiv

Combination and aggregation techniques can significantly improve forecast accuracy. This also holds for probabilistic forecasting methods where predictive distributions are combined. There are several time-varying and adaptive weighting schemes such as Bayesian model averaging (BMA). However, the quality of different forecasts may vary not only over time but also within the distribution. For example, some distribution forecasts may be more accurate in the center of the distributions, while others are better at predicting the tails. Therefore, we introduce a new weighting method that considers the differences in performance over time and within the distribution. We discuss pointwise combination algorithms based on aggregation across quantiles that optimize with respect to the continuous ranked probability score (CRPS). After analyzing the theoretical properties of pointwise CRPS learning, we discuss B- and P-Spline-based estimation techniques for batch and online learning, based on quantile regression and prediction with expert advice. We prove that the proposed fully adaptive Bernstein online aggregation (BOA) method for pointwise CRPS online learning has optimal convergence properties. They are confirmed in simulations and a probabilistic forecasting study for European emission allowance (EUA) prices.

Disclosing the Parentsâ€™ Perspectives on No-Homework Policy: A Phenomenological Study
Garing, Roy
SSRN
This qualitative-phenomenological study was initiated to explore and understand the lived experiences and different perceptions of parents on no-homework policy in one of the schools in Governor Generoso, Davao Oriental. Subsequently, this qualitative exploration hoped to draw out conclusions on the perspectives of the parents. The data source in this study derived from seven (7) research participants for the in-depth interview and another seven (7) parents for the focus group discussion. The research participants of this study were the selected Grade Five to Grade Six parents in one of the schools in Governor Generoso, Davao Oriental who were usually having many assignments compared to lower grade levels. The following themes emerged from analysis based on the perspectives of participant interviews: no-homework policy can be optional; no-homework policy is unfavorable and unhelpful to students; no-homework policy causes students to become irresponsible; and no-homework policy causes less learning among students. Concerning the experiences of parents in dealing with the assignments of their children, five major themes were manifested such as: having difficulties in answering homework; being able to help children; having fun while doing the assignment; being unable to finish work or chores; and bonding opportunity. Moreover, the participants about their challenges in dealing with the assignments of their children, four major themes emerged: understanding how to answer the lesson; having balance and time management; giving encouragement; and having follow-ups and rules. Lastly, their suggestions as regards the no-homework policy revealed four emergent themes: parents should always be responsible and helpful to their children; parents should have time management and balance; teachers should give appropriate, simple and uncostly homework; and teachers should give homework to students for continued learning.

For Whom Corporate Leaders Bargain: Presentation Slides
Bebchuk, Lucian A.,Kastiel, Kobi,Tallarita, Roberto
SSRN

Green Bond, Renewable Energy Stocks and Carbon Price: Dynamic Connectedness, Hedging and Investment Strategies during COVID-19 pandemic
Tiwari, Aviral Kumar,Aikins Abakah, Emmanuel Joel,Gabauer, David,Dwumfour, Richard Adjei
SSRN
This study has been inspired by the emergence of socially responsible investment practices in mainstream investment activity wherein it examines the transmission of return patterns between green bonds, carbon prices, and renewable energy stocks using daily data spanning from 1st January 2013 to 22nd September 2020. In this study, our dataset comprises the price indices of S&P Green Bond, Solactive Global Solar, Solactive Global Wind, S&P Global Clean Energy and Carbon. We employ the TVP-VAR approach to investigate the return spillovers and connectedness, and various portfolio techniques including minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. Additionally, a LASSO dynamic connectedness model is used for robustness purposes. The empirical results from the TVP VAR indicate that the dynamic total connectedness across the assets is heterogeneous over time and economic event dependent. Moreover, our findings suggest clean energy dominates all other markets and is seen to be the main net transmitter of shocks in the entire network with Green Bonds and Solactive Global Wind emerging to be the major recipients of shocks in the system. Based on the hedging effectiveness, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset except for Green Bonds. Finally, the minimum connectedness portfolio reaches the highest Sharpe ratio implying that information concerning the return transmission process is helpful for portfolio creation. The same pattern has been observed during the COVID-19 pandemic period.

House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach
Mahdieh Yazdani
arXiv

In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.

Implementing the BBE Agent-Based Model of a Sports-Betting Exchange
Dave Cliff,James Hawkins,James Keen,Roberto Lau-Soto
arXiv

We describe three independent implementations of a new agent-based model (ABM) that simulates a contemporary sports-betting exchange, such as those offered commercially by companies including Betfair, Smarkets, and Betdaq. The motivation for constructing this ABM, which is known as the Bristol Betting Exchange (BBE), is so that it can serve as a synthetic data generator, producing large volumes of data that can be used to develop and test new betting strategies via advanced data analytics and machine learning techniques. Betting exchanges act as online platforms on which bettors can find willing counterparties to a bet, and they do this in a way that is directly comparable to the manner in which electronic financial exchanges, such as major stock markets, act as platforms that allow traders to find willing counterparties to buy from or sell to: the platform aggregates and anonymises orders from multiple participants, showing a summary of the market that is updated in real-time. In the first instance, BBE is aimed primarily at producing synthetic data for in-play betting (also known as in-race or in-game betting) where bettors can place bets on the outcome of a track-race event, such as a horse race, after the race has started and for as long as the race is underway, with betting only ceasing when the race ends. The rationale for, and design of, BBE has been described in detail in a previous paper that we summarise here, before discussing our comparative results which contrast a single-threaded implementation in Python, a multi-threaded implementation in Python, and an implementation where Python header-code calls simulations of the track-racing events written in OpenCL that execute on a 640-core GPU -- this runs approximately 1000 times faster than the single-threaded Python. Our source-code for BBE is freely available on GitHub.

Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency
Yang Bai,Kuntara Pukthuanthong
arXiv

We design a novel framework to examine market efficiency through out-of-sample (OOS) predictability. We frame the asset pricing problem as a machine learning classification problem and construct classification models to predict return states. The prediction-based portfolios beat the market with significant OOS economic gains. We measure prediction accuracies directly. For each model, we introduce a novel application of binomial test to test the accuracy of 3.34 million return state predictions. The tests show that our models can extract useful contents from historical information to predict future return states. We provide unique economic insights about OOS predictability and machine learning models.

Mobile Money as a Tool for Financial Inclusion in Ghana's Post-COVID-19 Context: Key Developments and Future Trajectory
Duho, King Carl Tornam,Quansah, Daniel Ninsin
SSRN
The COVID-19 pandemic has significantly disrupted all spheres of life including the financial sector. In developing economies, the high number of people and businesses that fall under the informal sector is something that continues to receive policy attention. The actors within the informal sector are mostly those within the rural or urban poor and the pro-poor population. Mobile money services, which are digitised technologies that allow mobile phone users to access financial services have been improving the state of financial inclusion. This study explores the current state of mobile money usage in Ghana, discussing the issues within the context of COVID-19. The study further provides a futuristic analysis of the emerging developments on the use of mobile money as a tool for enhancing financial inclusion. Essentially, these future projections are provided on the critical issues that need to be considered in a post-COVID-19 context. Although the analysis dwells heavily on Ghana, the issues raised can be applied to the context of other developing economies with similar informal sectors and pro-poor populations.

Optimal Consumption with Loss Aversion and Reference to Past Spending Maximum
Xun Li,Xiang Yu,Qinyi Zhang
arXiv

This paper studies an optimal consumption problem for a loss-averse agent with reference to past consumption maximum. To account for loss aversion on relative consumption, an S-shaped utility is adopted that measures the difference between the non-negative consumption rate and a fraction of the historical spending peak. We consider the concave envelope of the realization utility with respect to consumption, allowing us to focus on an auxiliary HJB variational inequality on the strength of concavification principle and dynamic programming arguments. By applying the dual transform and smooth-fit conditions, the auxiliary HJB variational inequality is solved in closed-form piecewisely and some thresholds of the wealth variable are obtained. The optimal consumption and investment control of the original problem can be derived analytically in the piecewise feedback form. The rigorous verification proofs on optimality and concavification principle are provided.

Quasi-likelihood analysis for marked point processes and application to marked Hawkes processes
Simon Clinet
arXiv

We develop a quasi-likelihood analysis procedure for a general class of multivariate marked point processes. As a by-product of the general method, we establish under stability and ergodicity conditions the local asymptotic normality of the quasi-log likelihood, along with the convergence of moments of quasi-likelihood and quasi-Bayesian estimators. To illustrate the general approach, we then turn our attention to a class of multivariate marked Hawkes processes with generalized exponential kernels, comprising among others the so-called Erlang kernels. We provide explicit conditions on the kernel functions and the mark dynamics under which a certain transformation of the original process is Markovian and $V$-geometrically ergodic. We finally prove that the latter result, which is of interest in its own right, constitutes the key ingredient to show that the generalized exponential Hawkes process falls under the scope of application of the quasi-likelihood analysis.

R&D Heterogeneity and Countercyclical Productivity Dispersion
Shuowen Chen,Yang Ming
arXiv

What causes countercyclicality of industry--level productivity dispersion in the U.S.? Empirically, we construct an index of negative profit shocks and show that both productivity dispersion and R&D intensity dispersion enlarge at the onset of the shock and gradually dissipate. Theoretically, we build a duopolistic technology--ladder model in which heterogeneous R&D costs determine firms' post--shock optimal behaviors and equilibrium technology gap. Quantitatively, we calibrate a parameterized model, simulate firms' post--shock responses and predict that productivity dispersion is due to the low--cost firm increasing R&D efforts and the high--cost firm doing the opposite. We provide two empirical tests for this mechanism.

Short-time implied volatility of additive normal tempered stable processes
Michele Azzone,Roberto Baviera
arXiv

Empirical studies have emphasized that the equity implied volatility is characterized by a negative skew inversely proportional to the square root of the time-to-maturity. We examine the short-time-to-maturity behavior of the implied volatility smile for pure jump exponential additive processes. An excellent calibration of the equity volatility surfaces has been achieved by a class of these additive processes with power-law scaling. The two power-law scaling parameters are $\beta$, related to the variance of jumps, and $\delta$, related to the smile asymmetry. It has been observed, in option market data, that $\beta=1$ and $\delta=-1/2$. In this paper, we prove that the implied volatility of these additive processes is consistent, in the short-time, with the equity market empirical characteristics if and only if $\beta=1$ and $\delta=-1/2$.

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Stephan Zheng,Alexander Trott,Sunil Srinivasa,David C. Parkes,Richard Socher
arXiv

AI and reinforcement learning (RL) have improved many areas, but are not yet widely adopted in economic policy design, mechanism design, or economics at large. At the same time, current economic methodology is limited by a lack of counterfactual data, simplistic behavioral models, and limited opportunities to experiment with policies and evaluate behavioral responses. Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations. The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt, providing a tractable solution to the highly unstable and novel two-level RL challenge. From a simple specification of an economy, we learn rational agent behaviors that adapt to learned planner policies and vice versa. We demonstrate the efficacy of the AI Economist on the problem of optimal taxation. In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory. In complex, dynamic economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. It does so despite emergent tax-gaming strategies, while accounting for agent interactions and behavioral change more accurately than economic theory. These results demonstrate for the first time that two-level, deep RL can be used for understanding and as a complement to theory for economic design, unlocking a new computational learning-based approach to understanding economic policy.

The Effect of Bad Debt Adjustment on Repayment: Evidence From KR&Câ€™s Dataset in South Korea
Kim, Namhyun,Noh, Sanha
SSRN
This study investigates the effect of debt adjustment for bad debts on debt repayment and discusses the optimal principal reduction rate. We use a logit model and propensity score matching, employing the unique dataset of creditorsâ€™ ledger, debt adjustment information, and debtorsâ€™ characteristics from KR&C, which manages bad debts in Korea. Classifying debtors into beneficiaries and non-beneficiaries of the debt adjustment program, we find that the repayment amounts of beneficiaries are larger than those of non-beneficiaries. Additionally, a quadratic function consisting of expected repayment rates and reduction rates is postulated to explore the moral hazards that may arise if the principal reduction rate is expanded, and an appropriate reduction rate is derived. We find that if the reduction rate is expanded from 60% to 70%, it does not reach the maximum repayment rate, but the effectiveness of the debt adjustment system and the stability of the fund are maintained.

The Inelastic Market Hypothesis: A Microstructural Interpretation
Jean-Philippe Bouchaud
arXiv

We attempt to reconcile Gabaix and Koijen's (GK) recent Inelastic Market Hypothesis with the order-driven view of markets that emerged within the microstructure literature in the past 20 years. We review the most salient empirical facts and arguments that give credence to the idea that market price fluctuations are mostly due to order flow, whether informed or non-informed. We show that the Latent Liquidity Theory of price impact makes a precise prediction for GK's multiplier $M$, which measures by how many dollars, on average, the market value of a company goes up if one buys one dollar worth of its stocks. Our central result is that $M$ increases with the volatility of the stock and decreases with the fraction of the market cap. that is traded daily. We discuss several empirical results suggesting that the lion's share of volatility is due to trading activity.

The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion
Spiridon Penev,Pavel V. Shevchenko,Wei Wu
arXiv

We quantify model risk of a financial portfolio whereby a multi-period mean-standard-deviation criterion is used as a selection criterion. In this work, model risk is defined as the loss due to uncertainty of the underlying distribution of the returns of the assets in the portfolio. The uncertainty is measured by the Kullback-Leibler divergence, i.e., the relative entropy. In the worst case scenario, the optimal robust strategy can be obtained in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented which allow us to compare the performance of this robust strategy with the optimal non-robust strategy. For illustration, we also quantify the model risk associated with an empirical dataset.

Understanding the nature of the long--range memory phenomenon in socio-economic systems
Rytis Kazakevicius,Aleksejus Kononovicius,Bronislovas Kaulakys,Vygintas Gontis
arXiv

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations and agent-based models. Reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first-passage time distributions. Research has lead us to question whether the observed long-range memory is a result of actual long-range memory process or just a consequence of non-linearity of Markov processes. As our most recent result we discuss the long-range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional L\{e}vy stable motion. We test widely used long-range memory estimators on discrete fractional L\{e}vy stable motion represented by the ARFIMA sample series. Our newly obtained results seem indicate that new estimators of self-similarity and long-range memory for analyzing systems with non-Gaussian distributions have to be developed.

Will Corporations Deliver Value to All Stakeholders?
Bebchuk, Lucian A.,Tallarita, Roberto
SSRN