|
on Risk Management |
|
Issue of 2020‒05‒11
thirty-two papers chosen by |
| By: | Alessandro Gnoatto; Athena Picarelli; Christoph Reisinger |
| Abstract: | In this paper, we present a novel computational framework for portfolio-wide risk management problems where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02633&r=all |
| By: | Takaaki Koike; Marius Hofert |
| Abstract: | We analyze dependence, tail behavior and multimodality of the conditional distribution of a loss random vector given that the aggregate loss equals an exogenously provided capital. This conditional distribution is a building block for calculating risk allocations such as the Euler capital allocation of Value-at-Risk. A level set of this conditional distribution can be interpreted as a set of severe and plausible stress scenarios the given capital is supposed to cover. We show that various distributional properties of this conditional distribution are inherited from those of the underlying joint loss distribution. Among these properties, we find that multimodality of the conditional distribution is an important feature related to the number of risky scenarios likely to occur in a stressed situation. Moreover, Euler allocation becomes less sound under multimodality than under unimodality. To overcome this issue, we propose a novel risk allocation called the maximum likelihood allocation (MLA), defined as the mode of the conditional distribution given the total capital. The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations, and constructing more flexible risk allocations based on multiple risky scenarios. Properties of the conditional distribution and MLA are demonstrated in numerical experiments. In particular, we observe that negative dependence among losses typically leads to multimodality, and thus to multiple risky scenarios and less sound risk allocations. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02950&r=all |
| By: | Antoine Savine; Brian Huge |
| Abstract: | Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels, but also differentials of labels to inputs. Differential ML is applicable in all situations where high quality first order derivatives wrt training inputs are available. In the context of financial Derivatives risk management, pathwise differentials are efficiently computed with automatic adjoint differentiation (AAD). Differential ML, combined with AAD, provides extremely effective pricing and risk approximations. We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations. The article focuses on differential deep learning (DL), arguably the strongest application. Standard DL trains neural networks (NN) on punctual examples, whereas differential DL teaches them the shape of the target function, resulting in vastly improved performance, illustrated with a number of numerical examples, both idealized and real world. In the online appendices, we apply differential learning to other ML models, like classic regression or principal component analysis (PCA), with equally remarkable results. This paper is meant to be read in conjunction with its companion GitHub repo https://github.com/differential-machine-learning, where we posted a TensorFlow implementation, tested on Google Colab, along with examples from the article and additional ones. We also posted appendices covering many practical implementation details not covered in the paper, mathematical proofs, application to ML models besides neural networks and extensions necessary for a reliable implementation in production. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02347&r=all |
| By: | Sergio Correia; Kevin F. Kiernan; Matthew P. Seay; Cindy M. Vojtech |
| Abstract: | This technical note describes the Forward-Looking Analysis of Risk Events (FLARE) model, which is a top-down model that helps assess how well the banking system is positioned to weather exogenous macroeconomic shocks. FLARE estimates banking system capital under varying macroeconomic scenarios, time horizons, and other systemic shocks. |
| Keywords: | Bank capital; Stress testing; Comprehensive capital analysis and review (CCAR); Dodd-Frank Act stress tests (DFAST); Financial stability and risk |
| JEL: | G18 G21 G28 |
| Date: | 2020–02–13 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2020-15&r=all |
| By: | Jager, Maximilian; Siemsen, Thomas; Vilsmeier, Johannes |
| Abstract: | We introduce a novel simulation-based network approach, which provides full-edged distributions of potential interbank losses. Based on those distributions we propose measures for (i) systemic importance of single banks, (ii) vulnerability of single banks, and (iii) vulnerability of the whole sector. The framework can be used for the calibration of macro-prudential capital charges, the assessment of systemic risks in the banking sector, and for the calculation of banks' interbank loss distributions in general. Our application to German regulatory data from End-2016 shows that the German interbank network was at that time in general resilient to the default of large banks, i.e. did not exhibit substantial contagion risk. Even though up to four contagion defaults could occur due to an exogenous shock, the system-wide 99.9% VaR barely exceeds 1.5% of banks' CET 1 capital. For single institutions, however, we found indications for elevated vulnerabilities and hence the need for a close supervision. |
| Keywords: | Interbank contagion,credit risk,systemic risk,loss simulation |
| JEL: | G17 G21 G28 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:232020&r=all |
| By: | Marco Migueis |
| Abstract: | Basel's new standardized approach (SA) for operational risk capital may allow for regulatory arbitrage through the use of insurance. Under the SA, banks will have incentive to insure recurring losses, which can meaningfully reduce capital requirements even as it does not meaningfully decrease tail operational loss exposure. Several alternatives to deal with this regulatory arbitrage strategy are discussed. |
| Date: | 2020–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2020-03-30&r=all |
| By: | Piero Mazzarisi; Silvia Zaoli; Carlo Campajola; Fabrizio Lillo |
| Abstract: | Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by [Hong et al., 2009]. We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.01160&r=all |
| By: | Christa Cuchiero; Wahid Khosrawi; Josef Teichmann |
| Abstract: | We propose a fully data driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows to calculate model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analyis on many samples of implied volatility smiles, showing the accuracy and stability of the method. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02505&r=all |
| By: | Stefanie Behncke |
| Abstract: | I analyse the effects of two macroprudential policy measures implemented in Switzerland: the activation of the countercyclical capital buffer (CCyB) and a cap on the loan-to-value (LTV) ratios. I use a difference-in-differences method to estimate the effects of these measures on risk indicators, such as their LTV and loan-to-income (LTI) ratios and mortgage growth rates. I find that both the CCyB and the LTV cap led to a reduction in high LTV mortgages. The banks affected by the CCyB also reduced their mortgage growth rates. I do not find any evidence that these measures had unintended consequences on LTI risks or on non-mortgage credit growth. |
| Keywords: | Banks, countercyclical capital buffer, financial stability, loan-to-value ratio, macroprudential policy, mortgages |
| JEL: | E5 G21 G28 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:snb:snbwpa:2020-06&r=all |
| By: | Lucio Fernandez-Arjona |
| Abstract: | Insurance companies make extensive use of Monte Carlo simulations in their capital and solvency models. To overcome the computational problems associated with Monte Carlo simulations, most large life insurance companies use proxy models such as replicating portfolios. In this paper, we present an example based on a variable annuity guarantee, showing the main challenges faced by practitioners in the construction of replicating portfolios: the feature engineering step and subsequent basis function selection problem. We describe how neural networks can be used as a proxy model and how to apply risk-neutral pricing on a neural network to integrate such a model into a market risk framework. The proposed model naturally solves the feature engineering and feature selection problems of replicating portfolios. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02318&r=all |
| By: | Ahnert, Lukas; Vogt, Pascal; Vonhoff, Volker; Weigert, Florian |
| Abstract: | This paper investigates the impact of stress testing results on bank's equity and CDS performance using a large sample of twelve tests from the US CCAR and the European EBA regimes in the time period from 2010 to 2018. We find that passing banks experience positive abnormal equity returns and tighter CDS spreads, while failing banks show strong drops in equity prices and widening CDS spreads. We also document strong market reactions at the announcement date of the stress tests. Although the institutional designs between US and European stress tests differ, we generally observe similar capital market consequences for both regimes. We complement existing studies by investigating the predictability of stress test outcomes and evaluating strategic options for affected banks and investors. |
| Keywords: | Banks,Stress Testing,Equity Performance,CDS Performance |
| JEL: | G00 G21 G28 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2003&r=all |
| By: | Lucio Fernandez Arjona (Zurich Insurance Group); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute) |
| Abstract: | We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios. |
| Keywords: | Solvency capital; dimensionality reduction; neural networks; nested Monte Carlo; replicating portfolios. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2028&r=all |
| By: | Ros, Greg |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkops:202016&r=all |
| By: | Gilad Francis; Nick James; Max Menzies; Arjun Prakash |
| Abstract: | We develop a new method to find the number of volatility regimes in a non-stationary financial time series. We use change point detection to partition a time series into locally stationary segments, then estimate the distributions of each piece. The distributions are clustered into a learned number of discrete volatility regimes via an optimisation routine. Using this method, we investigate and determine a clustering structure for indices, large cap equities and exchange-traded funds. Finally, we create and validate a dynamic portfolio allocation strategy that learns the optimal match between the current distribution of a time series with its past regimes, thereby making online risk-avoidance decisions in the present. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.09963&r=all |
| By: | Dinghai Xu (Department of Economics, University of Waterloo) |
| Abstract: | This paper focuses on investigating the impacts of the novel coronavirus (COVID-19) on the Canadian stock market volatility from a time-varying parameter volatility model point of view. |
| JEL: | C22 C58 G18 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:wat:wpaper:2001&r=all |
| By: | Michael Nwogugu |
| Abstract: | Although the CML (Capital Market Line), the Intertemporal-CAPM, the CAPM/SML (Security Market Line) and the Intertemporal Arbitrage Pricing Theory (IAPT) are widely used in portfolio management, valuation and capital markets financing; these theories are inaccurate and can adversely affect risk management and portfolio management processes. This article introduces several empirically testable financial theories that provide insights, and can be calibrated to real data and used to solve problems, and contributes to the literature by: i) explaining the conditions under which ICAPM/CAPM, IAPT and CML may be accurate, and why such conditions are not feasible; and explaining why the existence of incomplete markets and dynamic un-aggregated markets render CML, IAPT and ICAPM inaccurate; ii) explaining why the Consumption-Savings-InvestmentProduction framework is insufficient for asset pricing and analysis of changes in risk and asset values; and introducing a unified approach to asset pricing that simultaneously considers six factors, and the conditions under which this approach will work; iii) explaining why leisure, taxes and housing are equally as important as consumption and investment in asset pricing; iv) introducing the Marginal Rate of Intertemporal Joint Substitution (MRIJS) among Consumption, Taxes, Investment, Leisure, Intangibles and Housing - this model incorporates Regret Theory and captures features of reality that dont fit well into standard asset pricing models, and this framework can support specific or very general finance theories and or very complicated models; v) showing why the Elasticity of Intertemporal Substitution (EIS) is inaccurate and is insufficient for asset pricing and analysis of investor preferences. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.01709&r=all |
| By: | Lucio Fernandez-Arjona (University of Zurich); Damir Filipovi\'c (EPFL and Swiss Finance Institute) |
| Abstract: | We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.14149&r=all |
| By: | Alan L. Lewis |
| Abstract: | We study equity risk premiums in the United States during the COVID-19 pandemic. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.13871&r=all |
| By: | Svetlana Litvinova; Mervyn J. Silvapulle |
| Abstract: | We show that the full-sample bootstrap is asymptotically valid for constructing confidence intervals for high-quantiles, tail probabilities, and other tail parameters of a univariate distribution. This resolves the doubts that have been raised about the validity of such bootstrap methods. In our extensive simulation study, the overall performance of the bootstrap method was better than that of the standard asymptotic method, indicating that the bootstrap method is at least as good, if not better than, the asymptotic method for inference. This paper also lays the foundation for developing bootstrap methods for inference about tail events in multivariate statistics; this is particularly important because some of the non-bootstrap methods are complex. |
| Keywords: | full-sample bootstrap, intermediate order statistic, extreme value index, Hill estimator; tail probability, tail quantile. |
| JEL: | C13 C15 C18 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-15&r=all |
| By: | Tian Guo; Nicolas Jamet; Valentin Betrix; Louis-Alexandre Piquet; Emmanuel Hauptmann |
| Abstract: | Incorporating environmental, social, and governance (ESG) considerations into systematic investments has drawn numerous attention recently. In this paper, we focus on the ESG events in financial news flow and exploring the predictive power of ESG related financial news on stock volatility. In particular, we develop a pipeline of ESG news extraction, news representations, and Bayesian inference of deep learning models. Experimental evaluation on real data and different markets demonstrates the superior predicting performance as well as the relation of high volatility prediction to stocks with potential high risk and low return. It also shows the prospect of the proposed pipeline as a flexible predicting framework for various textual data and target variables. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02527&r=all |
| By: | Kim, Jihyun; Park, Joon; Wang, Bin |
| Abstract: | In the paper, we introduce and analyze a new methodology to estimate the volatility functions of jump diffusion models. Our methodology relies on the standard kernel estimation technique using truncated bipower increments. The relevant asymptotics are fully developed, which allow for the time span to increase as well as the sampling interval to decrease and accommodate both stationary and nonstationary recurrent processes. We evaluate the performance of our estimators by simulation and provide some illustrative empirical analyses. |
| Keywords: | nonparametric estimation; jump diffusion;aymptotics; diffusive and jump; volatility functions; Lévy measure; optimal bandwidth; bipower increment; threshold truncation. |
| JEL: | C14 C22 C58 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:124234&r=all |
| By: | Johannes Ruf; Weiguan Wang |
| Abstract: | We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.08891&r=all |
| By: | J. Su; Q. Zhong |
| Abstract: | Assessing the trend of the COVID-19 pandemic and policy effectiveness is essential for both policymakers and stock investors, but challenging because the crisis has unfolded with extreme speed and the previous index was not suitable for measuring policy effectiveness for COVID-19. This paper builds an index of policy effectiveness on fighting COVID-19 pandemic, whose building method is similar to the index of Policy Uncertainty, based on province-level paper documents released in China from Jan.1st to Apr.16th of 2020. This paper also studies the relationships among COVID-19 daily confirmed cases, stock market volatility, and document-based policy effectiveness in China. This paper uses the DCC-GARCH model to fit conditional covariance's change rule of multi-series. This paper finally tests four hypotheses, about the time-space difference of policy effectiveness and its overflow effect both on the COVID-19 pandemic and stock market. Through the inner interaction of this triad structure, we can bring forward more specific and scientific suggestions to maintain stability in the stock market at such exceptional times. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02034&r=all |
| By: | Naji Massad; J{\o}rgen Vitting Andersen |
| Abstract: | We introduce a non linear pricing model of individual stock returns that defines a stickiness parameter of the returns. The pricing model resembles the capital asset pricing model used in finance but has a non linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding stress to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the indexs price movements, can then be used to define their stickiness |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02351&r=all |
| By: | Alexander Arimond; Damian Borth; Andreas Hoepner; Michael Klawunn; Stefan Weisheit |
| Abstract: | Utilizing a generative regime switching framework, we perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network, (ii) its incentive function according to which it has been trained and (iii) the amount of data we feed. First, we compare neural networks with random seeding with networks that are initialized via estimations from the best-established model (i.e. the Hidden Markov). We find latter to outperform in terms of the frequency of VaR breaches (i.e. the realized return falling short of the estimated VaR threshold). Second, we balance the incentive structure of the loss function of our networks by adding a second objective to the training instructions so that the neural networks optimize for accuracy while also aiming to stay in empirically realistic regime distributions (i.e. bull vs. bear market frequencies). In particular this design feature enables the balanced incentive recurrent neural network (RNN) to outperform the single incentive RNN as well as any other neural network or established approach by statistically and economically significant levels. Third, we half our training data set of 2,000 days. We find our networks when fed with substantially less data (i.e. 1,000 days) to perform significantly worse which highlights a crucial weakness of neural networks in their dependence on very large data sets ... |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.01686&r=all |
| By: | Chung-Han Hsieh |
| Abstract: | In this paper, we consider a simple discrete-time optimal betting problem using the celebrated Kelly criterion, which calls for maximization of the expected logarithmic growth of wealth. While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recast the problem into quadratic programming and obtain the closed-form approximate solution. The focal point of this paper is to fill some voids in the existing results by providing some interesting properties when such an approximate solution is used. Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and it associated variance, expected growth property, variance of logarithmic growth, and results related to the so-called survivability (no bankruptcy) are provided. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.14048&r=all |
| By: | Jarrow, Robert A.; Kwok, Simon S. |
| Abstract: | Financial bubbles arise when the underlying asset’s market price departs from its fundamental value. Unlike other bubble tests that use time series data and assume a reduced-form price process, we infer the existence of bubbles nonparametrically using option price data. Under no-arbitrage and acknowledging data constraints, we can partially identify asset price bubbles using a cross section of European option prices. In the empirical analysis, we obtain interval estimates of price bubbles embedded in the S&P 500 Index. The estimated index bubbles are then used to construct profitable momentum trading strategies that consistently outperform a buy-and-hold trading strategy. |
| Keywords: | asset price bubble, fundamental value, risk-neutral probability measure, state price distribution, tail truncation, partial identification, nonparametric estimation, local polynomial fit |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:syd:wpaper:2020-04&r=all |
| By: | Christine Laudenbach (House of Finance, Goethe University, Frankfurt); Benjamin Loos (University of Technology, Sidney); Jenny Pirschl (Goethe University, Frankfurt); Johannes Wohlfart (CEBI, Department of Economics, University of Copenhagen) |
| Abstract: | 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 e ects 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 e ects 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 uctuations in trading. |
| Keywords: | Individual investors, risk-taking, trading, experiences |
| JEL: | D14 G11 |
| Date: | 2020–03–23 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kucebi:2008&r=all |
| By: | Wenzhi Ding; Ross Levine; Chen Lin; Wensi Xie |
| Abstract: | Using data on over 6,000 firms across 56 economies during the first quarter of 2020, we evaluate the connection between corporate characteristics and stock price reactions to COVID-19 cases. We find that the pandemic-induced drop in stock prices was milder among firms with (a) stronger pre-2020 finances (more cash, less debt, and larger profits), (b) less exposure to COVID-19 through global supply chains and customer locations, (c) more CSR activities, and (d) less entrenched executives. Furthermore, the stock prices of firms with greater hedge fund ownership performed worse, and those of firms with larger non-financial corporate ownership performed better. We believe ours is the first paper to assess international, cross-firm stock price reactions to COVID-19 as functions of these pre-shock corporate characteristics. |
| JEL: | F23 G3 I10 M12 M14 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27055&r=all |
| By: | Berardino Palazzo; Jie Yang |
| Abstract: | In this note, we show that the key driver of the 2019:Q1 increase in the leverage ratio appears to be a change in accounting rules – which requires the inclusion of operating leases as financial liabilities on U.S. corporations' balance sheets – and also provide a methodology for adjusting the leverage ratio to allow for cleaner historical comparisons. |
| Date: | 2019–12–13 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2019-12-13-2&r=all |
| By: | Vishwas Kukreti; Hirdesh K. Pharasi; Priya Gupta; Sunil Kumar |
| Abstract: | In this brief review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. We, also, present our perspective on the use of recently developed entropy measures such as structural entropy and eigen-entropy for continuous monitoring of correlation-based networks. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.09448&r=all |
| By: | Humayra Shoshi; Indranil SenGupta |
| Abstract: | In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets. The refinement of the BN-S model is obtained with various machine and deep learning algorithms. The refinement leads to the extraction of a deterministic parameter from the empirical data set. The problem is transformed to an appropriate classification problem with a couple of different approaches: the volatility approach and the duration approach. The analysis is implemented to the Bakken crude oil data and the aforementioned deterministic parameter is obtained for a wide range of data sets. With the implementation of this parameter in the refined model, the resulting model performs much better than the classical BN-S model. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.14862&r=all |