nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒12‒05
twenty-one papers chosen by
Stan Miles
Thompson Rivers University

  1. Forecasting VaR and CVaR based on a skewed exponential power mixture, in compliance with the new market risk regulation By Saissi Hassani, Samir; Dionne, Georges
  2. Expectile-based capital allocation By Said Khalil
  3. Факторы риска, прибыльности и вероятности дефолта в российском банковском секторе By Bekirova, Olga; Zubarev, Andrey
  4. European firms, Panic Borrowing and Credit Lines Drawdowns: What did we learn from the COVID-19 shock? By Mario Cerrato; Hormoz Ramian; Shengfeng Mei
  5. Deep Learning for Inflexible Multi-Asset Hedging of incomplete market By Ruochen Xiao; Qiaochu Feng; Ruxin Deng
  6. Determinants and real effects of joint hedging: An empirical analysis of the US petroleum industry By Dionne, Georges; El Hraiki, Rayane; Mnasri, Mohamed
  7. Fire sales and ex ante valuation of systemic risk: A financial equilibrium networks approach By Spiros Bougheas; Adam Hal Spencer
  8. Climate Change and the Role of Regulatory Capital: A Stylized Framework for Policy Assessment By Michael Holscher; David Ignell; Morgan Lewis; Kevin J. Stiroh
  9. Assessing the difference between integrated quantiles and integrated cumulative distribution functions By Yunran Wei; Ricardas Zitikis
  10. The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters By Tzougas, George; Makariou, Despoina
  11. Risk sharing and monetary policy transmission By Hauptmeier, Sebastian; Holm-Hadulla, Fédéric; Renault, Théodore
  12. Measuring Transition Risk in Investment Funds By Ricardo Crisostomo
  13. Bitcoin flash crash on May 19, 2021: What did really happen on Binance? By Baumgartner, Tim; Güttler, André
  14. State-dependent Asset Allocation Using Neural Networks By Reza Bradrania; Davood Pirayesh Neghab
  15. Set it and Forget it? Financing Retirement in an Age of Defaults By Lucas Goodman; Anita Mukherjee; Shanthi Ramnath
  16. Bayesian nonparametric disclosure risk assessment By Favaro, Stefano; Panero, Francesca; Rigon, Tommaso
  17. The Welfare Effects of Bank Liquidity and Capital Requirements By Skander J. Van den Heuvel
  18. The spectre of terrorism and the stock market By Hanna, Alan J.; Turner, John D.; Walker, Clive B.
  19. Change of measure in a Heston-Hawkes stochastic volatility model By David R. Ba\~nos; Salvador Ortiz-Latorre; Oriol Zamora Font
  20. Stock price reaction to power outages following extreme weather events: Evidence from Texas power outage By Sherry Hu; Kose John; Balbinder Singh Gill
  21. A Data-driven Case-based Reasoning in Bankruptcy Prediction By Wei Li; Wolfgang Karl H\"ardle; Stefan Lessmann

  1. By: Saissi Hassani, Samir (HEC Montreal, Canada Research Chair in Risk Management); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: Our data, relating to a period of extreme market turmoil, show typical leptokurtosis and skewness, leading us to consider the skewed exponential power distribution of Fernández et al. (1995), referred to as the SEP3. We demonstrate that the conditional forecasting of VaR and CVaR, made up of a mixture of two SEP3 densities, can efficiently cover market risk at regulatory levels of 1% and 2.5%, as well as at the additional 5% level. The SEP3 mixture outcomes are benchmarked using a variety of competing models, including the generalized Pareto distribution. Appropriate scoring functions help focus quickly on valuable models, which should undergo five conventional backtests. As a sixth backtest, we argue for and apply the CVaR part of the optimality test of Patton et al. (2019) to assess the conditional adequacy of CVaR. Various additional statistical approaches are employed to validate models in response to Basel recommendations. We propose a novel criterion for CVaR accuracy assessment, based on its positioning in relation to the empirical CVaR‒ and CVaR+. An additional aim of this paper is to present a collaborative framework that relies on both comparative and conventional backtesting tools, all in compliance with the recent Basel regulation for market-risk.
    Keywords: Conditional forecasting; VaR; CVaR; Backtesting; Basel regulation for market risk; Heavy tailed distributions
    JEL: C44 C46 C52 G21 G24 G28 G32
    Date: 2022–07–07
  2. By: Said Khalil (INSEA - Institut National de Statistique et d’Economie Appliquée [Rabat])
    Abstract: In this paper, we focus on capital allocation using Euler principle with expectiles risk measures. We study the allocation composition for several actuarial models. The dependence impact is examined using some copulas and the comonotonic case is studied. The marginal contributions expressions are also given for all the studied models.
    Keywords: Risk management,Risk theory,Dependence modelling,Capital allocation,Expectiles,Elicitability,Copulas,2010 Mathematics Subject Classification: 62H05,91B05,91G05
    Date: 2022–10–16
  3. By: Bekirova, Olga; Zubarev, Andrey
    Abstract: Banks, acting as intermediaries in conducting settlements and providing liquidity to economic agents, play an important role in modern economic systems. At the same time, banking activity is associated with many risks that necessitates control from the regulator. Over the past 9 years, the Russian banking sector has experienced a transformation that resulted in a more than halving of the number of players in the banking system. However, a revoking a bank's license is not always associated with financial difficulties. In this paper, based on quarterly data on the financial statements of Russian banks for the period from mid-2013 to early 2022, using econometric methods of analysis, we estimated the factors that affect both the probability of bank default as well as other indicators of its activity – the risk of insolvency and profitability. The Z-score was used as an indicator of insolvency risk and the return on assets was used as an indicator of profitability. The results obtained showed that balance sheet ratios are significantly correlated with the probability of bank default, its risk of insolvency and profitability. The results support the “too-big-to-fail” hypothesis for the Russian banking sector, since larger banks have a lower probability of default, but a higher risk of insolvency. The insolvency risk is significantly negatively correlated with the probability of default and profitability.
    Keywords: banking sector, banking license revocation, insolvency risk, Z-score, return on assets, liquidity creation, Bank of Russia
    JEL: G21 G28 G33
    Date: 2022–10
  4. By: Mario Cerrato; Hormoz Ramian; Shengfeng Mei
    Abstract: “Riskier European companies draw €32bn from bank credit lines” (FT, May, 2020). The Financial Times in May 2020 highlighted a large group of European firms, took out of their credit lines an impressive €32bn to stay afloat during the pandemic shock. This was an impressive flight to liquidity as no one ever thought the whole market would draw their credit lines at once and so quickly. The Financial Times reported that the majority of firms withdrawing their credit lines were in the consumer, material and industrial sectors. In this paper we investigate why European firms drew down credit lines. We show that these firms were facing a fall in the expected revenue and a worsening of credit risk and therefore they used credit lines to top-up their liquidity position.
    Keywords: Corporate credit lines, cash holding, investment, default risk
    JEL: G21 G32 G33
    Date: 2022–05
  5. By: Ruochen Xiao; Qiaochu Feng; Ruxin Deng
    Abstract: Models trained under assumptions in the complete market usually don't take effect in the incomplete market. This paper solves the hedging problem in incomplete market with three sources of incompleteness: risk factor, illiquidity, and discrete transaction dates. A new jump-diffusion model is proposed to describe stochastic asset prices. Three neutral networks, including RNN, LSTM, Mogrifier-LSTM are used to attain hedging strategies with MSE Loss and Huber Loss implemented and compared.As a result, Mogrifier-LSTM is the fastest model with the best results under MSE and Huber Loss.
    Date: 2022–11
  6. By: Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); El Hraiki, Rayane (HEC Montreal, Canada Research Chair in Risk Management); Mnasri, Mohamed (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We study the intensity of joint hedging of oil and gas prices by US petroleum firms. We aim to explain the rationale for and find the determinants of joint hedging, as well as its impact on firm market value, performance, and riskiness. Joint hedging that takes into account the interdependence between risks should have a positive impact on firm value in the presence of multiple risks. We verify this theory in an innovative way, by testing the effects of hedging oil and gas prices simultaneously and by using an instrumental variable framework to attenuate the problem of endogeneity between firm value and risk management. We find evidence of higher market value, higher performance, and lower riskiness for firms with a high propensity to jointly hedge their oil and gas production to a greater extent. We show that joint hedging dominates single-commodity hedging.
    Keywords: Joint hedging; enterprise risk management; oil price; gas price; hedging intensity; bivariate probit; causality; firm value
    JEL: C13 C23 C25 G23 G32
    Date: 2022–08–23
  7. By: Spiros Bougheas; Adam Hal Spencer
    Abstract: We introduce endogenous fire sales into a simple network model. For any given initial distribution of shocks across the network, we develop a clearing algorithm to solve for the financial equilibrium. We then utilise the results to perform ex ante risk assessment and derive risk premia for every balance sheet item where liabilities are differentiated according to priority rights. We find that risk premia reflect both idiosyncratic risk and risk of contagion (network risk). Moreover, we show that network risk magnifies the gap between the risk premia of equity and debt. We also perform comparative statics, showing that changes to the distribution of shocks and network structure can have substantial effects on the level of systemic losses.
    Keywords: Networks; Fire Sales; Systemic Risk Premia; Risk Assessment
    Date: 2022
  8. By: Michael Holscher; David Ignell; Morgan Lewis; Kevin J. Stiroh
    Abstract: This paper presents a stylized framework to assess conceptually how the financial risks of climate change could interact with a regulatory capital regime. We summarize core features of a capital regime such as expected and unexpected losses, regulatory ratios and risk-weighted assets, and minimum requirements and buffers, and then consider where climate-related risk drivers may be relevant. We show that when considering policy implications, it is critically important to be precise about how climate change may impact the loss-generating process for banks and to be clear about the specific policy objective. While climate change could potentially impact the regulatory capital regime in several ways, an internally coherent approach requires a strong link between specific assumptions and beliefs about how these financial risks may manifest as bank losses and what objectives regulators are pursuing. We conclude by identifying several potential research opportunities to better understand these complex issues and inform policy development.
    Keywords: Climate change; Regulatory capital
    JEL: G21 G28
    Date: 2022–10–18
  9. By: Yunran Wei; Ricardas Zitikis
    Abstract: When developing large-sample statistical inference for quantiles, also known as Values-at-Risk in finance and insurance, the usual approach is to convert the task into sums of random variables. The conversion procedure requires that the underlying cumulative distribution function (cdf) would have a probability density function (pdf), plus some minor additional assumptions on the pdf. In view of this, and in conjunction with the classical continuous-mapping theorem, researchers also tend to impose the same pdf-based assumptions when investigating (functionals of) integrals of the quantiles, which are natural ingredients of many risk measures in finance and insurance. Interestingly, the pdf-based assumptions are not needed when working with integrals of quantiles, and in this paper we explain and illustrate this remarkable phenomenon.
    Date: 2022–10
  10. By: Tzougas, George; Makariou, Despoina
    Abstract: We introduce a multivariate Poisson-Generalized Inverse Gaussian regression model with varying dispersion and shape for modeling different types of claims and their associated counts in nonlife insurance. The multivariate Poisson-Generalized Inverse Gaussian regression model is a general class of models which, under the approach adopted herein, allows us to account for overdispersion and positive correlation between the claim count responses in a flexible manner. For expository purposes, we consider the bivariate Poisson-Generalized Inverse Gaussian with regression structures on the mean, dispersion, and shape parameters. The model's implementation is demonstrated by using bodily injury and property damage claim count data from a European motor insurer. The parameters of the model are estimated via the Expectation-Maximization algorithm which is computationally tractable and is shown to have a satisfactory performance.
    JEL: F3 G3 M40 J1
    Date: 2022–10–17
  11. By: Hauptmeier, Sebastian; Holm-Hadulla, Fédéric; Renault, Théodore
    Abstract: Using regionally disaggregated data on economic activity, we show that risk sharing plays a key role in shaping the real effects of monetary policy. With weak risk sharing, monetary policy shocks trigger a strong and durable response in output. With strong risk sharing, the response is attenuated, and output reverts to its initial level over the medium term. The attenuating impact of risk sharing via credit and factor markets concentrates over a two-year horizon, whereas fiscal risk sharing operates over longer horizons. Fiscal risk sharing especially benefits poorer regions by shielding them against persistent output contractions after tightening shocks. JEL Classification: C32, E32, E52
    Keywords: local projections, monetary policy, quantile regressions, regional heterogeneity, risk sharing
    Date: 2022–11
  12. By: Ricardo Crisostomo
    Abstract: We develop a comprehensive framework to measure the impact of the climate transition on investment portfolios. Our analysis is enriched by including geographical, sectoral, company and ISIN-level data to assess transition risk. We find that investment funds suffer a moderate 5.7% loss upon materialization of a high transition risk scenario. However, the risk distribution is significantly left-skewed, with the worst 1% funds experiencing an average loss of 21.3%. In terms of asset classes, equities are the worst performers (-12.7%), followed by corporate bonds (-5.6%) and government bonds (-4.8%). We discriminate among financial instruments by considering the carbon footprint of specific counterparties and the credit rating, duration, convexity and volatility of individual exposures. We find that sustainable funds are less exposed to transition risk and perform better than the overall fund sector in the low-carbon transition, validating their choice as green investments.
    Date: 2022–10
  13. By: Baumgartner, Tim; Güttler, André
    Abstract: Bitcoin plunged by 30% on May 19, 2021. We examine the outage the largest crypto exchange Binance experienced during the crash, when it halted trading for retail clients and stopped providing transaction data. We find evidence that Binance back-filled these missing transactions with data that does not conform to Benford's Law. The Bitcoin futures price difference between Binance and other exchanges was seven times larger during the crash period compared to a prior reference period. Data manipulation is a plausible explanation for our findings. These actions are in line with Binance aiming to limit losses for its futures-related insurance fund.
    Keywords: Benford's law,Binance,Bitcoin,cryptocurrency,crypto exchange,derivatives,extreme volatility,fraud,market crash,trading outage
    JEL: G10 G12 G14 K22
    Date: 2022
  14. By: Reza Bradrania; Davood Pirayesh Neghab
    Abstract: Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.
    Date: 2022–11
  15. By: Lucas Goodman; Anita Mukherjee; Shanthi Ramnath
    Abstract: Retirement savings abandonment is a rising concern connected to defined contribution systems and default enrollment. We use tax data on Individual Retirement Accounts (IRAs) to establish that for a recent cohort, 0.4% of retirement-age individuals abandoned an aggregate of $66 million, proxied by a failure to claim over ten years after a legal requirement to do so. Analysis of state unclaimed property databases suggests that workplace defined contribution plans are abandoned at a higher rate than IRAs. Finally, regression discontinuity estimates show that certain accounts created by default enrollment are at higher risk of abandonment by passive savers.
    Keywords: escheatment; defaults; retirement savings
    JEL: D83 H24 H31 J32 J14 J63
    Date: 2022–10–19
  16. By: Favaro, Stefano; Panero, Francesca; Rigon, Tommaso
    Keywords: Bayesian nonparametrics; data confidentiality; Dirichlet process prior; disclosure risk assessment; empirical Bayes; Pitman-Yor process prior; European Union’s Horizon 2020 research and innovation programme under grant agreement No 817257.
    JEL: C1
    Date: 2021
  17. By: Skander J. Van den Heuvel
    Abstract: The stringency of bank liquidity and capital requirements should depend on their social costs and benefits. This paper investigates their welfare effects and quantifies their welfare costs using sufficient statistics. The special role of banks as liquidity providers is embedded in an otherwise standard general equilibrium growth model. Capital and liquidity requirements mitigate moral hazard from deposit insurance, which, if unchecked, can lead to excessive credit and liquidity risk at banks. However, these regulations are also costly because they reduce the ability of banks to create net liquidity and can distort investment. Equilibrium asset returns reveal the strength of demand for liquidity, yielding two simple sufficient statistics that express the welfare cost of each requirement as a function of observable variables only. Based on U.S. data, the welfare cost of a 10 percent liquidity requirement is equivalent to a permanent loss in consumption of about 0.02%, a modest impact. Even using a conservative estimate, the cost of a similarly-sized increase in the capital requirement is roughly ten times as large. Even so, optimal policy relies on both requirements, as the financial stability benefits of capital requirements are found to be broader.
    Keywords: Capital requirements; Convenience yields; Banking; Welfare; Liquidity requirements; Sufficient statistics
    JEL: G28 G21 E44
    Date: 2022–11–04
  18. By: Hanna, Alan J.; Turner, John D.; Walker, Clive B.
    Abstract: Terrorism is a major issue in the 21st century. In this paper we examine the effect of terrorism on the stock market. We go beyond previous studies to explore the spectre of terrorism on the market rather than terrorist activities. Using a narrative-based approach à la Shiller (2019), we find that the spectre of terrorism during the Northern Ireland Troubles reduced returns and increased volatility on the UK stock market.
    Keywords: terrorism,stock market,returns,volatility,narratives
    JEL: C00 E44 G12 G40 N24
    Date: 2022
  19. By: David R. Ba\~nos; Salvador Ortiz-Latorre; Oriol Zamora Font
    Abstract: We consider the stochastic volatility model obtained by adding a compound Hawkes process to the volatility of the well-known Heston model. A Hawkes process is a self-exciting counting process with many applications in mathematical finance, insurance, epidemiology, seismology and other fields. We prove a general result on the existence of a family of equivalent (local) martingale measures. We apply this result to a particular example where the sizes of the jumps are exponentially distributed.
    Date: 2022–10
  20. By: Sherry Hu; Kose John; Balbinder Singh Gill
    Abstract: In this study, we evaluate the effects of natural disasters on the stock (market) values of firms located in the affected counties. We are able to measure the change in stock prices of the firms affected by the 2021 Texas winter storm. To measure the abnormal return due to the storm, we use four different benchmark models: (1) the market-adjusted model, (2) the market model, (3) the Fama-French three-factor model, and (4) the Fama French plus momentum model. These statistical models in finance characterize the normal risk-return trade-off.
    Date: 2022–10
  21. By: Wei Li; Wolfgang Karl H\"ardle; Stefan Lessmann
    Abstract: There has been intensive research regarding machine learning models for predicting bankruptcy in recent years. However, the lack of interpretability limits their growth and practical implementation. This study proposes a data-driven explainable case-based reasoning (CBR) system for bankruptcy prediction. Empirical results from a comparative study show that the proposed approach performs superior to existing, alternative CBR systems and is competitive with state-of-the-art machine learning models. We also demonstrate that the asymmetrical feature similarity comparison mechanism in the proposed CBR system can effectively capture the asymmetrically distributed nature of financial attributes, such as a few companies controlling more cash than the majority, hence improving both the accuracy and explainability of predictions. In addition, we delicately examine the explainability of the CBR system in the decision-making process of bankruptcy prediction. While much research suggests a trade-off between improving prediction accuracy and explainability, our findings show a prospective research avenue in which an explainable model that thoroughly incorporates data attributes by design can reconcile the dilemma.
    Date: 2022–11

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