nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒10‒25
25 papers chosen by
Stan Miles
Thompson Rivers University

  1. A Framework for Measures of Risk under Uncertainty By Tolulope Fadina; Yang Liu; Ruodu Wang
  2. Systemic Risk and Portfolio Diversification: Evidence from the Futures Market By Radoslav Raykov
  3. An empirical characterization of volatility dynamics in the DAX By Virla, Leonardo Quero
  4. Implied Volatility-Based Hedging Decisions with Futures and Options Markets By Meng, Shu; Goodwin, Barry K.
  5. How do central banks identify risks? A survey of indicators By Banco de España Strategic Plan 2024: Risk identification for the financial and macroeconomic stability
  6. Computing the Probability of a Financial Market Failure: A New Measure of Systemic Risk By Robert Jarrow; Philip Protter; Alejandra Quintos
  7. Numeraire-invariant quadratic hedging and mean--variance portfolio allocation By Ale\v{s} \v{C}ern\'y; Christoph Czichowsky; Jan Kallsen
  8. The impact of bank liquidity risk on risk-taking and bank lending: evidence from European bank By Hongyan Liang
  9. Robustifying Markowitz By Härdle, Wolfgang; Klochkov, Yegor; Petukhina, Alla; Zhivotovskiy, Nikita
  10. Identifying Ultimate Beneficial Owners : A Risk-Based Approach to Improving the Transparency of International Financial Flows By Crama, Yves; Hübner, Georges; Leruth, Luc; Renneboog, Luc
  11. Evolutionary Foundation for Heterogeneity in Risk Aversion By Yuval Heller; Ilan Nehama
  12. Identifying Ultimate Beneficial Owners : A Risk-Based Approach to Improving the Transparency of International Financial Flows By Crama, Yves; Hübner, Georges; Leruth, Luc; Renneboog, Luc
  13. Sizing hedge funds' Treasury market activities and holdings By Ayelen Banegas; Phillip J. Monin; Lubomir Petrasek
  14. Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models By Jingtang Ma; Wensheng Yang; Zhenyu Cui
  15. Sector Volatility Prediction Performance Using GARCH Models and Artificial Neural Networks By Curtis Nybo
  16. Modelling Time-Varying Volatility Interactions By Susana Campos-Martins; Cristina Amado
  17. The long-term effects of experienced macroeconomic shocks on wealth By Viola Angelini; Irene Ferrari
  18. Foreign Currency Funding of Major Japanese Banks - Review of the March 2020 market turmoil - By Ryo Aoki; Kunimasa Antoku; Shunsuke Fukushima; Tomoyuki Yagi; Shinichiro Watanabe
  19. Asymmetric Price Transmission in the Soybean Complex: A Multivariate Quantile Approach By Yang, Yao; Karali, Berna
  20. Learning about Unprecedented Events: Agent-Based Modelling and the Stock Market Impact of COVID-19 By Bazzana, Davide; Colturato, Michele; Savona, Roberto
  21. Expectations and Aggregate Risk By Lorenzo Bretscher; Aytek Malkhozov; Andrea Tamoni
  22. The start-up decision under default risk By Comincioli, Nicola; Panteghini, Paolo M.; Vergalli, Sergio
  23. Sorting over the Dual Risk of Coastal Housing Market By Chen, Zhenshan; Towe, Charles A.
  24. Macroprudential Policy during COVID-19: The Role of Policy Space By Katharina Bergant; Kristin Forbes
  25. Impact of public news sentiment on stock market index return and volatility By Anese, Gianluca; Corazza, Marco; Costola, Michele; Pelizzon, Loriana

  1. By: Tolulope Fadina; Yang Liu; Ruodu Wang
    Abstract: A risk analyst assesses potential financial losses based on multiple sources of information. Often, the assessment does not only depend on the specification of the loss random variable, but also various economic scenarios. Motivated by this observation, we design a unified axiomatic framework for risk evaluation principles which quantifies jointly a loss random variable and a set of plausible probabilities. We call such an evaluation principle a generalized risk measure. We present a series of relevant theoretical results. The worst-case, coherent, and robust generalized risk measures are characterized via different sets of intuitive axioms. We establish the equivalence between a few natural forms of law invariance in our framework, and the technical subtlety therein reveals a sharp contrast between our framework and the traditional one. Moreover, coherence and strong law invariance are derived from a combination of other conditions, which provides additional support for coherent risk measures such as Expected Shortfall over Value-at-Risk, a relevant issue for risk management practice.
    Date: 2021–10
  2. By: Radoslav Raykov
    Abstract: This paper explores the extent to which correlated investments in the futures market concentrated systemic risk on large Canadian banks around the 2008 crisis. We find that core banks took positions against the periphery, increasing their systemic risk as a group. On the portfolio level, position similarity was the main systemic risk driver for core banks, while cross-price correlations drove the systemic risk of noncore banks. Core banks were more diversified, but their portfolios also overlapped more. By contrast, non-core banks were less diversified, but also overlapped less. This significantly nuances the debate on concentration versus diversification as systemic risk sources.
    Keywords: Financial institutions; Financial markets
    JEL: G10 G20
    Date: 2021–10
  3. By: Virla, Leonardo Quero
    Abstract: This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term, an estimation of volatility in the DAX is provided. Such estimation is then plugged into a quantile regression model where potential economic determinants are analyzed. The results suggest that stock market volatility in Germany reached its historical peak between 2000 and 2004. Moreover, animal spirits play an important role across different quantiles of the volatility distribution, whereas the relevance of established risk factors proposed in the literature is limited to specific cases. Overall, the findings stress the importance of appropriate distributional assumptions when analyzing extreme financial events.
    Keywords: Asset prices,volatility,GARCH,quantile regression,DAX
    JEL: G12 G17
    Date: 2021
  4. By: Meng, Shu; Goodwin, Barry K.
    Keywords: Agricultural Finance, Risk and Uncertainty, Agribusiness
    Date: 2021–08
  5. By: Banco de España Strategic Plan 2024: Risk identification for the financial and macroeconomic stability (Banco de España)
    Abstract: For central banks, it is crucial to develop and maintain risk identification frameworks that allow them to detect in good time and address potential threats to financial stability with the most appropriate policy tools. This paper reviews the main indicators developed for this purpose by the Banco de España and by other central banks and prudential authorities. In this way, this stocktaking exercise contributes to improving the transparency and effective communication of the financial stability-related tasks carried out at the Banco de España. Some of the indicators are used in regular Banco de España surveillance activities, whereas others pertain to specific research activities. We classify our set of measures into two broad categories depending on the risk monitored: standard or systemic risks. Given the multidimensional nature of systemic risk, its identification goes beyond the sum of the standard risks explored in this paper (namely credit, macroeconomic, market, and liquidity and bank risks). This survey also classifies indicators by the type of institutional segment that triggers risks; namely, sovereigns, households, non-financial corporations, banks, non-bank financial sector, residential real estate and the financial markets. This work shows how the measures developed and regularly used at the Banco de España allow potential vulnerabilities to be comprehensively monitored. Nevertheless, maintaining an adequate risk-identification framework requires continuous adaptation to new theoretical developments and econometric tools, and, more importantly, to emerging challenges. In this respect, there is a current drive to develop new indicators to assess potential risks arising from climate change and those linked to the risk of system-wide cyber incidents. It is expected that the monitoring needs related to these risks will increase in the future.
    Keywords: risk identification, systemic risk, systemic risk indicators, standard risk indicators, financial stability
    JEL: E58 C43 G10 G21 G32 G50
    Date: 2021–09
  6. By: Robert Jarrow; Philip Protter; Alejandra Quintos
    Abstract: This paper characterizes the probability of a market failure defined as the simultaneous default of two globally systemically important banks (G-SIBs), where the default probabilities are correlated. The characterization employs a multivariate Cox process across the G-SIBs. Various theorems related to market failure probabilities are derived, including the impact of increasing the number of G-SIBs in an economy and changing the initial conditions of the economy's state variables.
    Date: 2021–10
  7. By: Ale\v{s} \v{C}ern\'y; Christoph Czichowsky; Jan Kallsen
    Abstract: The paper investigates quadratic hedging in a general semimartingale market that does not necessarily contain a risk-free asset. An equivalence result for hedging with and without numeraire change is established. This permits direct computation of the optimal strategy without choosing a reference asset and/or performing a numeraire change. New explicit expressions for optimal strategies are obtained, featuring the use of oblique projections that provide unified treatment of the case with and without a risk-free asset. The main result advances our understanding of the efficient frontier formation in the most general case where a risk-free asset may not be present. Several illustrations of the numeraire-invariant approach are given.
    Date: 2021–10
  8. By: Hongyan Liang (Faculty of Business Administration Gies College of Business University of Illinois Urbana-Champaign Champaign IL 61820, USA Author-2-Name: Zilong Liu Author-2-Workplace-Name: Discover Financial Service Riverwoods IL 60015, USA Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)
    Abstract: "Objective - This paper uses a sample of annual observations of European banks to examine whether the liquidity risk affects a bank's risk-taking behavior and its future loan growth. Methodology – A sample of European banks (27 member countries of the European Union plus U.K.) over the period of 2005 to 2019 are used in this study. Liquidity risk is measured by the ratio of liquid assets to total assets. Given the longitudinal nature of the data, the authors use panel regression with bank fixed effects to control for unobserved characteristics that might affect the dependent variable. Findings – The authors find that banks holding more liquid assets take less risk and show a higher subsequent loan growth rate. These results hold for both small and large banks. Novelty – To the authors' best knowledge, this is one of the earliest studies to carefully examine the effects of liquidity risk on risk-taking behavior and loan growth rate for European banks. Our research suggests that the current Basel III requirement on liquidity ratio can decrease bank's risking-taking behavior while not necessarily impact their future loan growth. Type of Paper - Empirical"
    Keywords: Bank Liquidity Risk; Risk-taking Behavior; Loan Growth; Basel III
    JEL: G21 G01 G18
    Date: 2021–09–30
  9. By: Härdle, Wolfgang; Klochkov, Yegor; Petukhina, Alla; Zhivotovskiy, Nikita
    Abstract: Markowitz mean-variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice. They perform poorly out of sample due to estimation error, they experience extreme weights together with high sen- sitivity to change in input parameters. The heavy-tail characteristics of financial time series are in fact the cause for these erratic fluctuations of weights that conse- quently create substantial transaction costs. In robustifying the weights we present a toolbox for stabilizing costs and weights for global minimum Markowitz portfolios. Utilizing a projected gradient descent (PGD) technique, we avoid the estimation and inversion of the covariance operator as a whole and concentrate on robust estimation of the gradient descent increment. Using modern tools of robust statistics we con- struct a computationally efficient estimator with almost Gaussian properties based on median-of-means uniformly over weights. This robustified Markowitz approach is confirmed by empirical studies on equity markets. We demonstrate that robustified portfolios reach higher risk-adjusted performance and the lowest turnover compared to shrinkage based and constrained portfolios.
    Date: 2021
  10. By: Crama, Yves; Hübner, Georges; Leruth, Luc; Renneboog, Luc (Tilburg University, School of Economics and Management)
    Date: 2021
  11. By: Yuval Heller; Ilan Nehama
    Abstract: We examine the evolutionary basis for risk aversion with respect to aggregate risk. We study populations in which agents face choices between aggregate risk and idiosyncratic risk. We show that the choices that maximize the long-run growth rate are induced by a heterogeneous population in which the least and most risk averse agents are indifferent between aggregate risk and obtaining its linear and harmonic mean for sure, respectively. Moreover, approximately optimal behavior can be induced by a simple distribution according to which all agents have constant relative risk aversion, and the coefficient of relative risk aversion is uniformly distributed between zero and two.
    Date: 2021–10
  12. By: Crama, Yves; Hübner, Georges; Leruth, Luc; Renneboog, Luc (Tilburg University, Center For Economic Research)
    Keywords: ownership concentration; shareholder categories; harmonization of regulation; ownership disclosure rules
    Date: 2021
  13. By: Ayelen Banegas; Phillip J. Monin; Lubomir Petrasek
    Abstract: Hedge funds play an increasingly important role in U.S. Treasury (UST) cash and futures markets, a role that has been widely discussed following the March 2020 U.S. Treasury sell-off. In this note, we analyze hedge funds' holdings of UST securities and their UST market activities in normal times and in times of financial market stress using regulatory data from the SEC Form PF.
    Date: 2021–10–06
  14. By: Jingtang Ma; Wensheng Yang; Zhenyu Cui
    Abstract: Rough volatility models have recently been empirically shown to provide a good fit to historical volatility time series and implied volatility smiles of SPX options. They are continuous-time stochastic volatility models, whose volatility process is driven by a fractional Brownian motion with Hurst parameter less than half. Due to the challenge that it is neither a semimartingale nor a Markov process, there is no unified method that not only applies to all rough volatility models, but also is computationally efficient. This paper proposes a semimartingale and continuous-time Markov chain (CTMC) approximation approach for the general class of rough stochastic local volatility (RSLV) models. In particular, we introduce the perturbed stochastic local volatility (PSLV) model as the semimartingale approximation for the RSLV model and establish its existence , uniqueness and Markovian representation. We propose a fast CTMC algorithm and prove its weak convergence. Numerical experiments demonstrate the accuracy and high efficiency of the method in pricing European, barrier and American options. Comparing with existing literature, a significant reduction in the CPU time to arrive at the same level of accuracy is observed.
    Date: 2021–10
  15. By: Curtis Nybo
    Abstract: Recently artificial neural networks (ANNs) have seen success in volatility prediction, but the literature is divided on where an ANN should be used rather than the common GARCH model. The purpose of this study is to compare the volatility prediction performance of ANN and GARCH models when applied to stocks with low, medium, and high volatility profiles. This approach intends to identify which model should be used for each case. The volatility profiles comprise of five sectors that cover all stocks in the U.S stock market from 2005 to 2020. Three GARCH specifications and three ANN architectures are examined for each sector, where the most adequate model is chosen to move on to forecasting. The results indicate that the ANN model should be used for predicting volatility of assets with low volatility profiles, and GARCH models should be used when predicting volatility of medium and high volatility assets.
    Date: 2021–10
  16. By: Susana Campos-Martins (University of Oxford, University of Minho and NIPE); Cristina Amado (University of Minho and NIPE, CREATES and Aarhus University)
    Abstract: In this paper, we propose an additive time-varying (or partially time-varying) multivariate model of volatility, where a time-dependent component is added to the extended vector GARCH process for modelling the dynamics of volatility interactions. In our framework, co-dependence in volatility is allowed to change smoothly between two extreme states and second-moment interdependence is identified from these crisis-contingent strucural changes. The estimation of the new time-varying vector GARCH process is simplified using an equation-by-equation estimator for the volatility equations in the first step, and estimating the correlation matrix in the second step. A new Lagrange multiplier test is derived for testing the null hypothesis of constancy co-dependence volatility against a smoothly time-varying interdependence between financial markets. The test appears to be a useful statistical tool for evaluating the adequacy of GARCH equations by testing the presence of significant changes in cross-market volatility transmissions. Monte Carlo simulation experiments show that the test statistic has satisfactory empirical properties in finite samples. An application to sovereign bond yield returns illustrates the modelling strategy of the new specification.
    Keywords: Multivariate time-varying GARCH; Volatility spillovers; Time-variation;Lagrange multiplier test; Financial market interdependence.
    JEL: C12 C13 C32 C51 G15
    Date: 2021
  17. By: Viola Angelini (University of Groningen; NETSPAR); Irene Ferrari (Department of Economics, University Of Venice CÃ Foscari; NETSPAR)
    Abstract: This paper examines the long-term effects of experienced macro-economic shocks – defined as multi-year peak-to-trough GDP declines of at least 10 percent – on the wealth distribution, portfolio allocation, and risk attitudes of older individuals in Europe. We show that individuals who have experienced more economic depression episodes have lower wealth in absolute terms, a lower probability to invest in risky assets, and display higher risk aversion. When analysing early investment decisions, we find that individuals hit by a depression substitute risky investments with investment in housing, and that these early choices shape wealth in the long-term.
    Keywords: Wealth distribution, economic depressions, risk aversion, early investments
    JEL: D31 E21 G51
    Date: 2021
  18. By: Ryo Aoki (Bank of Japan); Kunimasa Antoku (Bank of Japan); Shunsuke Fukushima (Bank of Japan); Tomoyuki Yagi (Bank of Japan); Shinichiro Watanabe (Bank of Japan)
    Abstract: Most of the major Japanese banks have endeavored to stabilize their foreign currency funding by increasing long term market-based funding and corporate deposits while expanding their overseas lending. In March 2020, when tensions in the international financial and capital markets increased due to the spread of Covid-19, USD lending surged due to the drawdown of commitment lines and other factors. The efforts of individual banks to stabilize their USD funding, as well as the effectiveness of USD funds-supplying by the six major central banks, prevented a major disruption in Japanese banks' USD funding. However, the importance of enhancing the robustness of USD funding structures was reaffirmed, as evidenced by the apparent vulnerability of short-term market-based funding at the height of the stressed environment. Appropriate management of foreign currency liquidity risk is crucial, not only for the stable operation of individual banks but also for the stability of the financial system as a whole. Japanese banks, for which foreign currency funding is one of the most important management issues, need to maintain efforts to strengthen their funding base and upgrade their risk management.
    Date: 2021–10–13
  19. By: Yang, Yao; Karali, Berna
    Keywords: Agricultural Finance, Risk and Uncertainty, Marketing
    Date: 2021–08
  20. By: Bazzana, Davide; Colturato, Michele; Savona, Roberto
    Abstract: We model the learning process of market traders during the unprecedented COVID-19 event. We introduce a behavioral heterogeneous agents’ model with bounded rationality by including a correction mechanism through representativeness (Gennaioli et al., 2015). To inspect the market crash induced by the pandemic, we calibrate the STOXX Europe 600 Index, when stock markets suffered from the greatest single-day percentage drop ever. Once the extreme event materializes, agents tend to be more sensitive to all positive and negative news, subsequently moving on to close-to-rational. We find that the deflation mechanism of less representative news seems to disappear after the extreme event.
    Keywords: Farm Management, Risk and Uncertainty
    Date: 2021–10–20
  21. By: Lorenzo Bretscher (London Business School - Department of Finance); Aytek Malkhozov (Board of Governors of the Federal Reserve System); Andrea Tamoni (Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick)
    Abstract: We estimate agents’ expectations about future fundamentals using a dynamic stochastic general equilibrium model augmented with anticipated shocks. Accounting for agents’ expectations at the business cycle horizon results in aggregate risk factor innovations that have significant explanatory power for the cross section of stock and bond returns. Further, exposure to macroeconomic fluctuations driven purely by expectations is important to explain the value premium. In contrast, exposure to macroeconomic fluctuations due to realized changes in fundamentals is important for the pricing of long-term bonds and cash-flow duration portfolios. We conclude that accounting for agents’ expectations advances our understanding of the aggregate risk.
    Keywords: News Shocks, Consumption-CAPM, Cross Section of Returns, Market-to-Book Decomposition
    JEL: G12 E32 E21 C63
    Date: 2021–10
  22. By: Comincioli, Nicola; Panteghini, Paolo M.; Vergalli, Sergio
    Abstract: This study introduces a real option model to investigate how fiscal policy affects a representative firm's investment decision and to measure its welfare effects. On the one hand, the effects of financial instability on the optimal investment timing and on the probability of default are studied. On the other hand, it is shown how the net present value of an investment project, the tax revenue generated and the welfare are influenced by financial instability. Then, a comparison of welfare effects of tax policy on start-ups, mature and obliged firms is provided. This comparison provides policy-makers a tool to shape their tax systems according to the characteristics of their firms. All presented analyses are supported by numerical simulations, based on realistic data.
    Keywords: Risk and Uncertainty
    Date: 2021–10–22
  23. By: Chen, Zhenshan; Towe, Charles A.
    Keywords: Environmental Economics and Policy, Research Methods/Statistical Methods, Resource/Energy Economics and Policy
    Date: 2021–08
  24. By: Katharina Bergant; Kristin Forbes
    Abstract: This paper uses the initial phase of the COVID-19 pandemic to examine how macroprudential frameworks developed over the past decade performed during a period of heightened financial and economic stress. It discusses a new measure of the macroprudential stance that better captures the intensity of different policies across countries and time. Then it shows that macroprudential policy has been used countercyclically—with stances tightened during the 2010’s and eased in response to COVID-19 by more than previous risk-off periods. Countries that tightened macroprudential policy more aggressively before COVID, as well as those that eased more during the pandemic, experienced less financial and economic stress. Countries’ ability to use macroprudential policy, however, was significantly constrained by the extent of existing “policy space”, i.e., by how aggressively policy was tightened before COVID-19. The use of macroprudential tools was not significantly affected by the space available to use other policy tools (such as fiscal policy, monetary policy, FX intervention, and capital flow management measures), and the use of other tools was not significantly affected by the space available to use macroprudential policy. This suggests that although macroprudential tools are being used countercyclically and should therefore help stabilize economies and financial markets, there appears to be an opportunity to better integrate the use of macroprudential tools with other policies in the future.
    JEL: E58 E61 E63 F38 G18 G28
    Date: 2021–10
  25. By: Anese, Gianluca; Corazza, Marco; Costola, Michele; Pelizzon, Loriana
    Abstract: Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
    Keywords: Public financial news,Stock market,NLP,Dictionary,LSTM neural networks,Investor sentiment,S&P 500
    JEL: G14 G17 C45 C63
    Date: 2021

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