nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒03‒27
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. The systemic risk approach based on implied and realized volatility By Paweł Sakowski; Rafał Sieradzki; Robert Ślepaczuk
  2. Quantifying Systemic Risk in the Presence of Unlisted Banks: Application to the European Banking Sector By Daniel Dimitrov; Sweder van Wijnbergen
  3. Using skewed exponential power mixture for VaR and CVaR forecasts to comply with market risk regulation By Saissi Hassani, Samir; Dionne, Georges
  4. Elicitability of Return Risk Measures By M\"ucahit Ayg\"un; Fabio Bellini; Roger J. A. Laeven
  5. The Market-Based Probability of Stock Returns By Victor Olkhov
  6. Factor mimicking portfolios for climate risk By Gianluca De Nard; Robert F. Engle; Bryan Kelly
  7. Ruin Probabilities for Risk Processes in Stochastic Networks By Hamed Amini; Zhongyuan Cao; Andreea Minca; Agn\`es Sulem
  8. A Tale of Tail Covariances (and Diversified Tails) By Jan Rosenzweig
  9. A Tale of Two Currencies: Cash and Crypto By Ravi Kashyap
  10. Forecasting Macroeconomic Tail Risk in Real Time: Do Textual Data Add Value? By Philipp Ad\"ammer; Jan Pr\"user; Rainer Sch\"ussler
  11. Simultaneous upper and lower bounds of American option prices with hedging via neural networks By Ivan Guo; Nicolas Langren\'e; Jiahao Wu
  12. The Politics of Bank Failures in Russia By Zuzana FungáÄ ová; Alexei Karas; Laura Solanko; Laurent Weill
  13. Exchange rate risk and sovereign debt risk in South Africa: A Regime Dependent Approach By Mathias Manguzvane; Mduduzi Biyase

  1. By: Paweł Sakowski (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance, Quantitative Finance Research Group); Rafał Sieradzki (New York University Stern School of Business; Cracow University of Economics); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance, Quantitative Finance Research Group)
    Abstract: We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2021 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (IVRVSRI) shows that the reaction of stock markets varies across different geographical locations and the persistence of the shocks depends on the historical volatility and long-term average volatility level in a given market. The methodology applied is based on the logic “the simpler is always better than the more complex, if it leads to the same results”. Such an approach significantly limits the model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise measure of the current systemic risk in the stock markets. Moreover, IVRVSRI seems to be a valid indication of current systemic risk in equity markets and it can be used for other types of assets and high-frequency data.
    Keywords: systemic risk, implied volatility, realized volatility, volatility indices, equity index options, market volatility
    JEL: G14 G15 C61 C22
    Date: 2023
  2. By: Daniel Dimitrov; Sweder van Wijnbergen
    Abstract: We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from publicly traded banks, privately held institutions, and cooperative banks, extending approaches that rely on information from the public equity market only. We account for correlated losses between the institutions, overcoming a modeling weakness in earlier studies. We also offer a modeling extension to account for fat tails and skewness of asset returns. The model is applied to a universe of banks where we find discrepancies between the capital adequacy of the largest contributors to systemic risk relative to less systemically important banks on a European scale.
    Keywords: systemic risk; CDS rates; implied market measures; financial institutions; fat tails; O-SII buffers
    JEL: G01 G20 G18 G38
    Date: 2023–03
  3. By: Saissi Hassani, Samir (HEC Montreal, Canada Research Chair in Risk Management); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We demonstrate how a mixture of two SEP3 densities (skewed exponential power distribution of Fernández et al., 1995) can model the conditional forecasting of VaR and CVaR to efficiently cover market risk at regulatory levels of 1% and 2.5%, as well as at the additional 5% level. Our data consists of a sample of market asset returns, relating to a period of extreme market turmoil, showing typical leptokurtosis and skewness. The SEP3 mixture outcomes are benchmarked using various competing models, including the generalized Pareto distribution. Appropriate scoring functions quickly highlight valuable models, which undergo conventional backtests. As an additional 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. 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: 2023–03–10
  4. By: M\"ucahit Ayg\"un; Fabio Bellini; Roger J. A. Laeven
    Abstract: Informally, a risk measure is said to be elicitable if there exists a suitable scoring function such that minimizing its expected value recovers the risk measure. In this paper, we analyze the elicitability properties of the class of return risk measures (i.e., monotone and positively homogeneous risk measures). First, we provide dual representation results for convex and geometrically convex return risk measures. Next, we establish new axiomatic characterizations of Orlicz premia (i.e., Luxemburg norms). More specifically, we prove, under different sets of conditions, that Orlicz premia naturally arise as the only elicitable return risk measures. We provide a general family of consistent scoring functions for Orlicz premia and a myriad of specific examples. Finally, we illustrate the applicability of our results to evaluating point forecasts and model performance by means of numerical simulations.
    Date: 2023–02
  5. By: Victor Olkhov
    Abstract: We show how time-series of random market trade values and volumes completely describe stochasticity of stock returns. We derive equation that links up returns with current and past trade values and show how statistical moments of the trade values and volumes determine statistical moments of stock returns. We estimate statistical moments of the trade values and volumes by the conventional frequency-based probability. However we believe that frequencies of stock returns don't define its probabilities as market and financial concepts. We present the market-based treatment of the probability of stock returns that defines average returns during "trading day" that completely match conventional notion of the weighted value return of the portfolio. We derive how statistical moments of the market trade values and volumes define approximations of the characteristic functions and probability density functions of stock returns. We derive volatility of stock returns, autocorrelations of stock returns, returns-volume and returns-price correlations through corresponding relations between statistical moments of the market trade values and volumes. The market-based probability of stock returns reveals direct dependence of statistical properties of stock returns on market trade randomness and economic uncertainty. Any reasonable forecasting of stock returns should be based on well-grounded predictions of the market trades and economic environment.
    Date: 2023–02
  6. By: Gianluca De Nard; Robert F. Engle; Bryan Kelly
    Abstract: We propose and implement a procedure to optimally hedge climate change risk. First, we construct climate risk indices through textual analysis of newspapers. Second, we present a new approach to compute factor mimicking portfolios to build climate risk hedge portfolios. The new mimicking portfolio approach is much more efficient than traditional sorting or maximum correlation approaches by taking into account new methodologies of estimating large-dimensional covariance matrices in short samples. In an extensive empirical out-of-sample performance test, we demonstrate the superior all-around performance delivering markedly higher and statistically significant alphas and betas with the climate risk indices.
    Keywords: Climate change, factor model, portfolio selection, sustainable portfolio
    JEL: C58 G11 G18 Q54
    Date: 2023–03
  7. By: Hamed Amini; Zhongyuan Cao; Andreea Minca; Agn\`es Sulem
    Abstract: We study multidimensional Cram\'er-Lundberg risk processes where agents, located on a large sparse network, receive losses form their neighbors. To reduce the dimensionality of the problem, we introduce classification of agents according to an arbitrary countable set of types. The ruin of any agent triggers losses for all of its neighbours. We consider the case when the loss arrival process induced by the ensemble of ruined agents follows a Poisson process with general intensity function that scales with the network size. When the size of the network goes to infinity, we provide explicit ruin probabilities at the end of the loss propagation process for agents of any type. These limiting probabilities depend, in addition to the agents' types and the network structure, on the loss distribution and the loss arrival process. For a more complex risk processes on open networks, when in addition to the internal networked risk processes the agents receive losses from external users, we provide bounds on ruin probabilities.
    Date: 2023–02
  8. By: Jan Rosenzweig
    Abstract: This paper deals with tail diversification in financial time series through the concept of statistical independence by way of differential entropy and mutual information. By using moments as contrast functions to isolate the tails of the return distributions, we recover the tail covariance matrix, a specific two-dimensional slice of the mixed moment tensor, as a key driver of tail diversification. We further explore the links between the moment contrast approach and the original entropy formulation, and show an example of in- and out-of-sample diversification on a broad stock universe.
    Date: 2023–02
  9. By: Ravi Kashyap
    Abstract: We discuss numerous justifications for why crypto-currencies would be highly conducive for the smooth functioning of today's society. We provide several comparisons between cryptocurrencies issued by blockchain projects, crypto, and conventional government issued currencies, cash or fiat. We summarize seven fundamental innovations that would be required for participants to have greater confidence in decentralized finance (DeFi) and to obtain wealth appreciation coupled with better risk management. The conceptual ideas we discuss outline an approach to: 1) Strengthened Security Blueprint; 2) Rebalancing and Trade Execution Suited for Blockchain Nuances 3) Volatility and Variance Adjusted Weight Calculation 4) Accommodating Investor Preferences and Risk Parity Construction; 5) Profit Sharing and Investor Protection; 6) Concentration Risk Indicator and Performance Metrics; 7) Multi-chain expansion and Select Strategic Initiatives including the notion of a Decentralized Autonomous Organization (DAO). Incorporating these concepts into several projects would also facilitate the growth of the overall blockchain eco-system so that this technology can, have wider mainstream adoption and, fulfill its potential in transforming all aspects of human interactions.
    Date: 2023–02
  10. By: Philipp Ad\"ammer; Jan Pr\"user; Rainer Sch\"ussler
    Abstract: We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions (now- and forecasts) of employment, output, inflation and consumer sentiment. Our results suggest that news data contain valuable information not captured by economic indicators, particularly for left-tail forecasts. Methods that capture quantile-specific non-linearities produce superior forecasts relative to methods that feature linear predictive relationships. However, adding news-based data substantially increases the performance of quantile-specific linear models, especially in the left tail. Variable importance analyses reveal that left tail predictions are determined by both economic and textual indicators, with the latter having the most pronounced impact on consumer sentiment.
    Date: 2023–02
  11. By: Ivan Guo; Nicolas Langren\'e; Jiahao Wu
    Abstract: In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds of the option price, and the second one accomplishes the same goal with one global network. The avoidance of extra simulations and the use of neural networks significantly reduce the computational complexity and allow us to price Bermudan options with frequent exercise opportunities in high dimensions, as illustrated by the provided numerical experiments. As a by-product, these methods also derive a hedging strategy for the option, which can also be used as a control variate for variance reduction.
    Date: 2023–02
  12. By: Zuzana FungáÄ ová; Alexei Karas; Laura Solanko; Laurent Weill
    Abstract: We study whether bank failure probability systematically varies over the election cycle in Russia. Using monthly data for 2002-2020 and controlling for standard bank risk indicators we find that bank failure is less likely during periods preceding presidential elections. We explore whether this effect is more pronounced for banks whose failure is associated with greater political costs, such as important players in the household deposit market or important players in regional markets. We find no evidence for this latter effect. Overall, our results provide mixed evidence that political cycles matter for the occurrence of bank failures in Russia.
    Keywords: Bank Failure, Election, Russia
    Date: 2022
  13. By: Mathias Manguzvane (College of Business and Economics, School of Economics, University of Johannesburg); Mduduzi Biyase (College of Business and Economics, School of Economics, University of Johannesburg)
    Abstract: We provide novel evidence of the regime specific effect of exchange rate risk on sovereign debt risk in South Africa. Using monthly data from 2008 to 2021 through a Markov regime switching model with time varying probabilities for the transitions, our results show that exchange rate risk matters in determining movements in sovereign debt risk as measured by sovereign credit default swaps (CDS). The results suggest that exchange rate risk exacts a positive and significant impact on sovereign debt risk in both the high risk regime and low risk regime. However, we notice that the magnitude of the impact differs from one regime to the other, implying that sovereign debt risk responds differently to exchange rate risk bull and bear markets
    Keywords: Sovereign debt, Exchange rate, Markov Regime Switching Model, credit default swaps
    Date: 2023

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