nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒01‒18
seventeen papers chosen by
Stan Miles
Thompson Rivers University

  1. Insurance valuation: A two-step generalised regression approach By Karim Barigou; Valeria Bignozzi; Andreas Tsanakas
  2. Singapore; Financial Sector Assessment Program; Technical Note-Financial Stability Analysis and Stress Testing By International Monetary Fund
  3. Credit Risk in a Pandemic By Byström, Hans
  4. On the origin of systemic risk By Montagna, Mattia; Torri, Gabriele; Covi, Giovanni
  5. Portfolio Optimisation within a Wasserstein Ball By Silvana Pesenti; Sebastian Jaimungal
  6. Parametric measures of variability induced by risk measures By Fabio Bellini; Tolulope Fadina; Ruodu Wang; Yunran Wei
  7. COVID-19 and SME Failures By Sebnem Kalemli-Ozcan; Pierre-Olivier Gourinchas; Veronika Penciakova; Nick Sander
  8. Bank liquidity, bank lending, and "bad bank" policies By Morrison, Alan D; Wang, Tianxi
  9. Business Cycles as Collective Risk Fluctuations By Victor Olkhov
  10. Minimizing Spectral Risk Measures Applied to Markov Decision Processes By Nicole B\"auerle; Alexander Glauner
  11. Modeling asset allocation strategies and a new portfolio performance score By Apostolos Chalkis; Ioannis Z. Emiris
  12. Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility By Takahashi, Makoto; Watanabe, Toshiaki; Omori, Yasuhiro
  13. Disaster Property Insurance in Uzbekistan By World Bank
  14. Liquidity Stress Testing in Asset Management -- Part 1. Modeling the Liability Liquidity Risk By Thierry Roncalli; Fatma Karray-Meziou; Fran\c{c}ois Pan; Margaux Regnault
  15. Systemic Risk in Market Microstructure of Crude Oil and Gasoline Futures Prices: A Hawkes Flocking Model Approach By Hyun Jin Jang; Kiseop Lee; Kyungsub Lee
  16. Competing effects on the average age of infant death By Alexander, Monica; Root, Leslie
  17. Bull and Bear Markets During the COVID-19 Pandemic By Maheu, John M; McCurdy, Thomas H; Song, Yong

  1. By: Karim Barigou (SAF); Valeria Bignozzi; Andreas Tsanakas
    Abstract: Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.
    Date: 2020–12
  2. By: International Monetary Fund
    Abstract: This technical note Financial Stability Analysis and Stress Testing on Singapore contributes to the assessment of the stability and soundness of the financial sector with a comprehensive set of risk analyses. The work combines an examination of key risk indicators with detailed stress tests, which simulate the health of banks, insurers, nonfinancial corporates and households under severe yet plausible (counterfactual) adverse scenarios. Scenarios include global financial market turmoil, a major slowdown of economic activity in China, cyber-attacks and extreme flooding. The analyses include simulations of contagion within the international banking network, within the domestic banking system and between different types of financial institutions in the financial system. The stress tests reveal that the financial system is broadly resilient to severe adverse shocks; however, foreign exchange liquidity is a key vulnerability. The analyses suggest that Monetary Authority of Singapore should continue strengthening its surveillance by closing data gaps and developing its analytical tools. Further data collection on domestic interlinkages, household mortgage debt at the borrower level, insurers’ balance sheets would enhance surveillance.
    Keywords: Stress testing;Banking;Domestic systemically important banks;Insurance companies;Capital adequacy requirements;ISCR,CR,U.S. dollar,financial market,banking group,financial system,sensitivity analysis,solvency stress tests
    Date: 2019–07–15
  3. By: Byström, Hans (Department of Economics, Lund University)
    Abstract: Using different measures of how the Covid-19 pandemic progresses we find that the level of credit risk among US blue chip companies increases in tandem with the Covid-19 virus spreading. The credit risk increases dramatically during the pandemic, but we find it to be short of the levels seen during the 2008–2009 financial crisis. Furthermore, we find weekly ups and downs in credit risk and virus impact to be significantly positively correlated throughout the pandemic. Finally, Basel II capital requirements increase drastically when the pandemic strikes but, again, not to the levels seen during the financial crisis.
    Keywords: credit risk; Covid-19; equity market; debt market; CDS; Merton model; Basel II
    JEL: G10 G33 I18
    Date: 2021–01–04
  4. By: Montagna, Mattia; Torri, Gabriele; Covi, Giovanni
    Abstract: Systemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. On one hand, shocks coming from correlated exposures towards the real economy may induce correlation in banks' default probabilities thereby increasing the likelihood for systemic-tail events like the 2008 Great Financial Crisis. On the other hand, financial contagion also plays an important role in generating large-scale market failures, amplifying the initial shocks coming from the real economy. To study the sources of these rare phenomena, we propose a new definition of systemic risk (i.e. the probability of a large number of banks going into distress simultaneously) and thus we develop a multilayer microstructural model to study empirically the determinants of systemic risk. The model is then calibrated on the most comprehensive granular dataset for the euro area banking sector, capturing roughly 96% or EUR 23.2 trillion of euro area banks' total assets over the period 2014-2018. The output of the model decompose and quantify the sources of systemic risk showing that correlated economic shocks, financial contagion mechanisms, and their interaction are the main sources of systemic events. The results obtained with the simulation engine resemble common market-based systemic risk indicators and empirically corroborate findings from existing literature. This framework gives regulators and central bankers a tool to study systemic risk and its developments, pointing out that systemic events and banks’ idiosyncratic defaults have different drivers, hence implying different policy responses. JEL Classification: D85, G17, G33, L14
    Keywords: financial contagion, microstructural models, systemic risk
    Date: 2020–12
  5. By: Silvana Pesenti; Sebastian Jaimungal
    Abstract: We consider the problem of active portfolio management where a loss-averse and/or gain-seeking investor aims to outperform a benchmark strategy's risk profile while not deviating too much from it. Specifically, an investor considers alternative strategies that co-move with the benchmark and whose terminal wealth lies within a Wasserstein ball surrounding it. The investor then chooses the alternative strategy that minimises their personal risk preferences, modelled in terms of a distortion risk measure. In a general market model, we prove that an optimal dynamic strategy exists and is unique, and provide its characterisation through the notion of isotonic projections. Finally, we illustrate how investors with different risk preferences invest and improve upon the benchmark using the Tail Value-at-Risk, inverse S-shaped distortion risk measures, and lower- and upper-tail risk measures as examples. We find that investors' optimal terminal wealth distribution has larger probability masses in regions that reduce their risk measure relative to the benchmark while preserving some aspects of the benchmark.
    Date: 2020–12
  6. By: Fabio Bellini; Tolulope Fadina; Ruodu Wang; Yunran Wei
    Abstract: We study general classes of parametric measures of variability with applications in risk management. Particular focus is put on variability measures induced by three classes of popular risk measures: the Value-at-Risk, the Expected Shortfall, and the expectiles. Properties of these variability measures are explored in detail, and a characterization result is obtained via the mixture of inter-ES differences. Convergence properties and asymptotic normality of their empirical estimators are established. We provide an illustration of the three classes of variability measures applied to financial data and analyze their relative advantages.
    Date: 2020–12
  7. By: Sebnem Kalemli-Ozcan; Pierre-Olivier Gourinchas; Veronika Penciakova; Nick Sander
    Abstract: We estimate the impact of the COVID-19 crisis on business failures among small and medium size enterprises (SMEs) in seventeen countries using a large representative firm-level database. We use a simple model of firm cost-minimization and measure each firm’s liquidity shortfall during and after COVID-19. Our framework allows for a rich combination of sectoral and aggregate supply, productivity, and demand shocks. We estimate a large increase in the failure rate of SMEs under COVID-19 of nearly 9 percentage points, ab-sent government support. Accommodation & Food Services, Arts, Entertainment & Recreation, Education, and Other Services are among the most affected sectors. The jobs at risk due to COVID-19 related SME business failures represent 3.1 percent of private sector employment. Despite the large impact on business failures and employment, we estimate only moderate effects on the financial sector: the share of Non Performing Loans on bank balance sheets would increase by up to 11 percentage points, representing 0.3 percent of banks’ assets and resulting in a 0.75 percentage point decline in the common equity Tier-1 capital ratio. We evaluate the cost and effectiveness of various policy interventions. The fiscal cost of an intervention that narrowly targets at risk firms can be modest (0.54% of GDP). However, at a similar level of effectiveness, non-targeted subsidies can be substantially more expensive (1.82% of GDP). Our results have important implications for the severity of the COVID-19 recession, the design of policies, and the speed of the recovery.
    Keywords: COVID-19 ;Labor;Supply shocks;Labor supply;Wages;WP,bankruptcy rate,cash flow,wage bill,employment decision,ghost firm
    Date: 2020–09–25
  8. By: Morrison, Alan D; Wang, Tianxi
    Abstract: Why are bank deposits demandable when they are also negotiable? We present a General Equilibrium model in which demandable debt exposes banks to liquidity risk so that they can signal their types and ensure that their liabilities can circulate as a means of payment. Banks can manage their liquidity risk by altering their deposit rate and their lending scale. When banks are transparent, so that depositors have homogenous information about their assets, they use only the former tool: their lending scale is effcient, and they do not experience liquidity crisis. When banks are opaque, so that depositors receive private signals of their quality, they ineffciently shrink the scale of their lending. A bank's stock of liquid assets affects its capacity for risk taking. A "bad bank" policy can resolve liquidity crises by reducing the opacity of the bank's assets.
    Keywords: Liquidity crises, demandable deposits, negotiable deposits, bad bank policies
    Date: 2021–01–12
  9. By: Victor Olkhov
    Abstract: We suggest use continuous numerical risk grades [0,1] of R for a single risk or the unit cube in Rn for n risks as the economic domain. We consider risk ratings of economic agents as their coordinates in the economic domain. Economic activity of agents, economic or other factors change agents risk ratings and that cause motion of agents in the economic domain. Aggregations of variables and transactions of individual agents in small volume of economic domain establish the continuous economic media approximation that describes collective variables, transactions and their flows in the economic domain as functions of risk coordinates. Any economic variable A(t,x) defines mean risk XA(t) as risk weighted by economic variable A(t,x). Collective flows of economic variables in bounded economic domain fluctuate from secure to risky area and back. These fluctuations of flows cause time oscillations of macroeconomic variables A(t) and their mean risks XA(t) in economic domain and are the origin of any business and credit cycles. We derive equations that describe evolution of collective variables, transactions and their flows in the economic domain. As illustration we present simple self-consistent equations of supply-demand cycles that describe fluctuations of supply, demand and their mean risks.
    Date: 2020–12
  10. By: Nicole B\"auerle; Alexander Glauner
    Abstract: We study the minimization of a spectral risk measure of the total discounted cost generated by a Markov Decision Process (MDP) over a finite or infinite planning horizon. The MDP is assumed to have Borel state and action spaces and the cost function may be unbounded above. The optimization problem is split into two minimization problems using an infimum representation for spectral risk measures. We show that the inner minimization problem can be solved as an ordinary MDP on an extended state space and give sufficient conditions under which an optimal policy exists. Regarding the infinite dimensional outer minimization problem, we prove the existence of a solution and derive an algorithm for its numerical approximation. Our results include the findings in B\"auerle and Ott (2011) in the special case that the risk measure is Expected Shortfall. As an application, we present a dynamic extension of the classical static optimal reinsurance problem, where an insurance company minimizes its cost of capital.
    Date: 2020–12
  11. By: Apostolos Chalkis; Ioannis Z. Emiris
    Abstract: We discuss a powerful, geometric representation of financial portfolios and stock markets, which identifies the space of portfolios with the points lying in a simplex convex polytope. The ambient space has dimension equal to the number of stocks, or assets. Although our statistical tools are quite general, in this paper we focus on the problem of portfolio scoring. Our contribution is to introduce an original computational framework to model portfolio allocation strategies, which is of independent interest for computational finance. To model asset allocation strategies, we employ log-concave distributions centered on portfolio benchmarks. Our approach addresses the crucial question of evaluating portfolio management, and is relevant to the individual private investors as well as financial organizations. We evaluate portfolio performance, in a certain time period, by providing a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on the expected proportion of allocations that the portfolio outperforms when a certain set of strategies take place in that time period. We also discuss how this set of strategies -- and the knowledge one may have about them -- could vary in our framework, and we provide additional versions of our score in order to obtain a more complete picture of its performance. In all cases, we show that the score computations can be performed efficiently. Last but not least, we expect this framework to be useful in portfolio optimization and in automatically identifying extreme phenomena in a stock market.
    Date: 2020–12
  12. By: Takahashi, Makoto; Watanabe, Toshiaki; Omori, Yasuhiro
    Abstract: This paper compares the volatility predictive abilities of some time-varying volatility models such as thestochastic volatility (SV) and exponential GARCH (EGARCH) models using daily returns, the heterogeneous au-toregressive (HAR) model using daily realized volatility (RV) and the realized SV (RSV) and realized EGARCH(REGARCH) models using the both. The data are the daily return and RV of Dow Jones Industrial Aver-age (DJIA) in US and Nikkei 225 (N225) in Japan. All models are extended to accommodate the well-knownphenomenon in stock markets of a negative correlation between today's return and tomorrow's volatility. Weestimate the HAR model by the ordinary least squares (OLS) and the EGARCH and REGARCH models bythe quasi-maximum likelihood (QML) method. Since it is not straightforward to evaluate the likelihood of theSV and RSV models, we apply a Bayesian estimation via Markov chain Monte Carlo (MCMC) to them. Byconducting predictive ability tests and analyses based on model confidence sets, we confirm that the models us-ing RV outperform the models without RV, that is, the RV provides useful information on forecasting volatility.Moreover, we find that the realized SV model performs best and the HAR model can compete with it. Thecumulative loss analysis suggests that the differences of the predictive abilities among the models are partlycaused by the rise of volatility.
    Keywords: Exponential GARCH (EGARCH) model, Heterogeneous autoregressive (HAR) model, Markov chain Monte Carlo (MCMC), Realized volatility, Stochastic volatility, Volatility forecasting
    JEL: C11 C22 C53 C58 G17
    Date: 2021–01
  13. By: World Bank
    Keywords: Environment - Natural Disasters Finance and Financial Sector Development - Insurance & Risk Mitigation Urban Development - Hazard Risk Management
    Date: 2020–01
  14. By: Thierry Roncalli; Fatma Karray-Meziou; Fran\c{c}ois Pan; Margaux Regnault
    Abstract: This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal to develop mathematical and statistical approaches, and provide appropriate answers. In this first part that focuses on liability liquidity risk modeling, we propose several statistical models for estimating redemption shocks. The historical approach must be complemented by an analytical approach based on zero-inflated models if we want to understand the true parameters that influence the redemption shocks. Moreover, we must also distinguish aggregate population models and individual-based models if we want to develop behavioral approaches. Once these different statistical models are calibrated, the second big issue is the risk measure to assess normal and stressed redemption shocks. Finally, the last issue is to develop a factor model that can translate stress scenarios on market risk factors into stress scenarios on fund liabilities.
    Date: 2021–01
  15. By: Hyun Jin Jang; Kiseop Lee; Kyungsub Lee
    Abstract: We propose the Hawkes flocking model that assesses systemic risk in high-frequency processes at the two perspectives -- endogeneity and interactivity. We examine the futures markets of WTI crude oil and gasoline for the past decade, and perform a comparative analysis with conditional value-at-risk as a benchmark measure. In terms of high-frequency structure, we derive the empirical findings. The endogenous systemic risk in WTI was significantly higher than that in gasoline, and the level at which gasoline affects WTI was constantly higher than in the opposite case. Moreover, although the relative influence's degree was asymmetric, its difference has gradually reduced.
    Date: 2020–12
  16. By: Alexander, Monica; Root, Leslie
    Abstract: In recent decades, the relationship between the average length of life for those who die in the first year of life — the lifetable quantity 1𝑎0 — and the level of infant mortality, on which its calculation is often based, has broken down. The very low levels of infant mortality in the developed world correspond to a range of 1𝑎0 quantities. We illustrate the competing effect of falling mortality and reduction in preterm births on 1𝑎0, through two populations with very different levels of premature birth — infants born to non-Hispanic white mothers and to non- Hispanic black mothers in the United States. Through simulation, we further demonstrate that falling mortality reduces 1𝑎0, while a reduction in premature births increases it. We use these observations to motivate the formulation of a new approximation formula for 1𝑎0 in low- mortality contexts, which is a function of both the infant mortality rate and the ratio of infant to under-five mortality. Model results and validation show that this model outperforms existing alternatives.
    Date: 2020–12–26
  17. By: Maheu, John M; McCurdy, Thomas H; Song, Yong
    Abstract: The COVID-19 pandemic has caused severe disruption to economic and financial activity worldwide. We assess what happened to the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. Using the model of Maheu, McCurdy and Song (2012), we provide smoothed estimates and out-of-sample forecasts associated with stock market dynamics during the pandemic. We identify bull and bear market regimes including their bull correction and bear rally components, demonstrate the model's performance in capturing periods of significant regime change, and provide forecasts that improve risk management and investment decisions. The paper concludes with out-of-sample forecasts of market states one year ahead.
    Keywords: predictive density, long-horizon returns, Markov switching
    JEL: C1 C11 C22 G1 G11 G17
    Date: 2020–11

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