nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒07‒25
seventeen papers chosen by
Stan Miles
Thompson Rivers University

  1. Market Risk and Volatility Weighted Historical Simulation After Basel III By Jean-Paul Laurent; Hassan Omidi Firouzi
  2. The SKEW index: extracting what has been left By Bevilacqua, Mattia; Tunaru, Radu
  3. Measuring Capital at Risk in the UK banking sector: a microstructural network approach By Covi, Giovanni; Brookes, James; Raja, Charumathi
  4. Portfolio optimization under CV@R constraint with stochastic mirror descent By Gadat, Sébastien; Costa, Manon; Huang, Lorick
  5. Pricing for a vulnerable bull spread options using a mixed modified fractional Hull-White-Vasicek model By Eric Djeutcha; Jules Sadefo Kamdem
  6. Hedging Valuation Adjustment and Model Risk By Claudio Albanese; Cyril Bénézet; Stéphane Crépey
  7. Aggregate Lapsation Risk By Ralph S. J. Koijen; Hae Kang Lee; Stijn Van Nieuwerburgh
  8. Regulatory complexity, uncertainty, and systemic risk: are regulators hedgehogs or foxes? By Maurizio Trapanese
  9. Information Geometry of Risks and Returns By Andrei N. Soklakov
  10. The shifts and the shocks: bank risk, leverage, and the macroeconomy By Kuvshinov, Dmitry; Richter, Björn; Zimmermann, Kaspar
  11. Ownership concentration and firm risk: The moderating role of mid-sized blockholders By Rossetto, Silvia; Selmane, Nassima; Staglianò, Raffaele
  12. When It Rains, It Pours: Cyber Risk and Financial Conditions By Thomas M. Eisenbach; Anna Kovner; Michael Junho Lee
  13. A model of system-wide stress simulation: market-based finance and the Covid-19 event By di Iasio, Giovanni; Alogoskoufis, Spyridon; Kördel, Simon; Kryczka, Dominika; Nicoletti, Giulio; Vause, Nicholas
  14. Skewness preferences: Evidence from online poker By Dertwinkel-Kalt, Markus; Kasinger, Johannes; Schneider, Dmitrij
  15. Gold, Bitcoin, and Portfolio Diversification: Lessons from the Ukrainian War By Kim Oosterlinck; Ariane Reyns; Ariane Szafarz
  16. The fractional volatility model and rough volatility By R. Vilela Mendes
  17. Financial and Macroeconomic Indicators of Recession Risk By Michael T. Kiley

  1. By: Jean-Paul Laurent (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne); Hassan Omidi Firouzi (LABEX Refi - ESCP Europe - Ecole Supérieure de Commerce de Paris)
    Abstract: Regulatory capital requirements for market risk, also known as the Fundamental Review of the Trading Book (FRTB), were disclosed by the Basel Committee on January 2016. This major overhaul of the Basel 2.5 framework challenges risk model specification and backtesting. Given the prevalence of historical simulation approach within large financial institutions, we focus on the Filtered (Volatility Weighted) Historical Simulation (VWHS) approach associated with a EWMA volatility filter. Volatility dynamics is then directed by a single parameter. We discuss how this decay parameter, chosen within a reasonable range, at banks' discretion, impacts capital metrics, backtesting statistics, as prescribed by the Basel Committee, and fouls the regulatory benchmarking of internal risk models. We show a trade-off between the resilience of risk models to periods of turmoil and the magnitude of capital metrics. Under the new regulatory rules, this would favour plain historical simulation, as compared with filtered or volatility weighed historical simulation. Understanding why, might be helpful for regulated banks, regarding the management of their market risk models, and supervisors involved in internal model approval.
    Keywords: Backtesting,Historical Simulation,Market Risk,Fundamental Review of the Trading Book,Basel III,Capital Requirements
    Date: 2022–05–26
  2. By: Bevilacqua, Mattia; Tunaru, Radu
    Abstract: This study disentangles a measure of implied skewness that is related to downward movements in the U.S. equity index from the corresponding implied skewness that is associated with upward movements. A positive SKEW index is constructed from S&P 500 call options, whereas a negative SKEW index is constructed from the S&P 500 put options. We show that the positive SKEW is linked to market sentiment, whereas the negative SKEW is related to existing tail risk measures. The negative SKEW is proposed as a more objective prudent tail risk measure, and it is found to be able to predict recessions, market downturns, and uncertainty indicators up to one year in advance. The predictive power of the negative SKEW is also confirmed when we control for other tail risk measures and also out-of-sample.
    Keywords: financial stability; implied skewness; market downturns; market sentiment; tail risk; ES/K002309/1; ES/R009724/1
    JEL: F3 G3 C1
    Date: 2021–04–01
  3. By: Covi, Giovanni (Bank of England); Brookes, James (Bank of England); Raja, Charumathi (Bank of England)
    Abstract: In this paper we construct and analyse the UK banking system’s Global Network of granular exposures which captures roughly 90% of the UK banking system’s total assets for the period 2018 Q1 to 2021 Q4. We thus study the microstructure of UK banking system focusing on the role played by concentration risk and interconnectedness across sectors. We then estimate the quarterly evolution of expected losses (Capital at Risk) for the UK banking sector, and via Monte Carlo simulations the stochastic distribution of UK banks’ losses to study the severity and likelihood of tail-events (Conditional Capital at Risk). In the end, we provide insights on the impact of the Covid-19 pandemic on UK banking system’s loss distribution by decomposing the sources of average and tail risks.
    Keywords: Financial network; systemic risk; stress testing; Covid-19 pandemic.
    JEL: D85 G21 G32 L14
    Date: 2022–05–27
  4. By: Gadat, Sébastien; Costa, Manon; Huang, Lorick
    Abstract: This article studies and solves the problem of optimal portfolio allocation with CV@R constraints when dealing with imperfectly simulated nancial assets. We use a Stochastic biased Mirror Descent to nd optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that satises suitable error bounds, under a risk management constraint. We establish almost sure asymptotic properties as well as the rate of convergence for the averaged algorithm. We then focus on the optimal tuning of the overall procedure to obtain an optimized numerical cost. Our results are then illustrated numerically on simulated as well as real data sets
    Keywords: Stochastic Mirror Descent; Biased observations,; Risk management constraint; Portfolio selection; Discretization
    Date: 2022–06–21
  5. By: Eric Djeutcha (UMa - University of Maroua); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)
    Abstract: In this paper, in order to serve credit risk management, we introduce a pricing model for a vulnerable Bull Spread options in a Mixed Modified Fractional Hull-White-Vasicek stochastic volatility and stochastic interest rate model. We use Milstein scheme to find the sample paths of asset price and its volatility, and the sample paths of interest rates of asset price movement. We use the double Mellin transform to obtain an analytical vulnerable bull spread call option formula and an analytical vulnerable bull spread put option formula under fractional stochastic volatility and fractional stochastic interest rates.
    Keywords: Bull spread option,Hull-White-Vasicek model,Double Mellin transform
    Date: 2022–05–23
  6. By: Claudio Albanese (Global Valuation); Cyril Bénézet (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - UEVE - Université d'Évry-Val-d'Essonne - ENSIIE - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise); Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPC - Université Paris Cité, UPC - Université Paris Cité)
    Abstract: We revisit Burnett (2021b,a)'s notion of hedging valuation adjustment (HVA) in the direction of model risk. The resulting HVA can be seen as the bridge between a global fair valuation model and the local models used by the different desks of the bank. However, model risk and dynamic hedging frictions, such as transaction costs à la Burnett (2021b,a), indeed deserve a reserve, but a risk-adjusted one, so not only an HVA, but also a contribution to the KVA of the bank. We also argue that the industry-standard XVA metrics are jeopardized by cash flows risk, which is in fact of the same mathematical nature than the one regarding pricing models, although at the higher level of aggregation characteristic of XVA metrics.
    Date: 2022–05–23
  7. By: Ralph S. J. Koijen; Hae Kang Lee; Stijn Van Nieuwerburgh
    Abstract: We study aggregate lapsation risk in the life insurance sector. Using the regulatory reporting of historical lapse rates by life insurers, we empirically document the counter-cyclicality of lapsation behavior. We construct two lapsation risk factors that explain a large fraction of the common variation in lapse rates of the 30 largest life insurance companies. The first is a cyclical factor that correlates with credit spreads and employment, while the second factor is a trend factor that correlates with the level of interest rates. Using a novel policy-level database from a large life insurer, we examine the heterogeneity in risk factor exposures based on policy and policyholder characteristics. Young policyholders with higher health risk are more likely to lapse their policies during economic downturns. We explore the implications for hedging and valuation of life insurance contracts. Ignoring aggregate lapsation risk results in cross-subsidization across policyholders with different lapsation risk, and in a mispricing of life insurance policies. Our results have implications for the welfare costs of business cycles.
    JEL: E32 E44 G12 G22 G52
    Date: 2022–06
  8. By: Maurizio Trapanese (Banca d'Italia)
    Abstract: This paper explores the relationship between regulatory complexity and systemic risk. Building upon the distinction between measurable risk and uncertainty, it outlines the fundamentals of the main regulatory frameworks of the last two decades (with a focus on the Basel Accords). The resulting outcome in terms of excessively regulatory complexity might turn out to be costly, and sub-optimal for crisis prevention. Since modern finance is characterized by uncertainty (rather than risk), less complex rules could be given greater consideration. Rebalancing regulation towards simplicity may produce Pareto-improving solutions, and encourage better decision making by authorities and regulated entities. However, addressing systemic risk in a complex financial system should not entail the replacement of overly complex rules with overly simple or less stringent regulations. The challenge is to define criteria and methods to assess the degree of unnecessary complexity in regulation. To this end, the paper proposes some options affecting the content of the rules, the regulatory policy mix for certain financial sectors, as well as the rulemaking process.
    Keywords: economic theory, uncertainty, financial crises, financial regulation
    JEL: B20 D81 G01 G28
    Date: 2022–06
  9. By: Andrei N. Soklakov
    Abstract: We often think of hedging and investments as having different, even competing goals. In reality optimal hedging and optimal investments are intimately connected. One person's optimal investment is another's optimal hedge. This follows from a geometric structure formed by probabilistic representations of market views and risk scenarios. Understanding this geometric structure is fundamental to risk recycling (and to product design in general).
    Date: 2022–06
  10. By: Kuvshinov, Dmitry; Richter, Björn; Zimmermann, Kaspar
    Abstract: This paper studies the long-run evolution of bank risk and its links to the macroeconomy. Using data for 17 advanced economies, we show that the riskiness of bank assets declined materially between 1870 and 2016. But even though bank assets have become safer, the losses on these assets are associated with increasingly large output gaps. Before 1945, bank asset returns had no excess predictive power for future economic activity, while after 1945 they have outperformed non-financials as a predictor of GDP. We provide evidence linking this increasing connectedness between banks and the macroeconomy to secular increases in financial and macroeconomic leverage. JEL Classification: G01, G15, G21, E44, N20, O16
    Keywords: banking crises, bank risk, leverage, long-run trends, macro-financial linkages
    Date: 2022–06
  11. By: Rossetto, Silvia; Selmane, Nassima; Staglianò, Raffaele
    Abstract: This study analyzes the relationship between mid-sized blockholders and firm risk. We show that ownership structure matters for firm risk, beyond the first largest blockholder. Firms with multiple blockholders take more risk than firms with just one blockholder, even when controlling for the stake of the largest blockholder. Consistent with the diversification argument, we find that firm risk increases by 22% when the number of blockholders increases from one to two. Our results are robust to controlling for blockholder type and firm characteristics. We carry out various robustness checks to tackle endogeneity issues. More generally, we provide evidence that firms’ decisions are affected by mid-sized blockholders, and not merely the largest blockholder. This is in line with theoretical predictions.
    Keywords: Corporate Governance; Ownership Structure; Firm Risk; Blockholders; Volatility of Operating Performance
    JEL: G11 G30 G32 G34
    Date: 2022–07–01
  12. By: Thomas M. Eisenbach; Anna Kovner; Michael Junho Lee
    Abstract: We analyze how systemic cyber risk in the wholesale payments network relates to adverse financial conditions. We show that at the onset of the COVID-19 pandemic, payment activity increased, became more concentrated, and showed intraday liquidity stress. Cyber vulnerability was elevated in late February and early March 2020, with the potential impact of a cyberattack about 40 percent greater than in the remainder of 2020. Policy interventions to stabilize markets mitigated cyber vulnerability, particularly corresponding to large increases in aggregate reserves. We observe that cyber vulnerability and other financial shocks cannot be treated as uncorrelated risks and policy solutions for cyber security need to be calibrated for adverse financial conditions.
    Keywords: cyber; banks; networks; payments; COVID-19
    JEL: G12 G21 G28
    Date: 2022–06–01
  13. By: di Iasio, Giovanni; Alogoskoufis, Spyridon; Kördel, Simon; Kryczka, Dominika; Nicoletti, Giulio; Vause, Nicholas
    Abstract: We build a model to simulate how the euro area market-based financial system may function under stress. The core of the model is a set of representative agents reflecting key economic sectors, which interact in asset, funding, and derivatives markets and face solvency and liquidity constraints on their behaviour. We illustrate the model's behaviour in a two-layer approach. In Layer 1 the deterioration in the outlook for the corporate sector triggers portfolio reallocation by the model's agents. Layer 2 adds a rating downgrade shock where a fraction of investment grade corporate bonds is downgraded to high yield, which creates further rebalancing pressure and price movements. The model predicts (i) asset flows (buying and selling of marketable securities) across agents and (ii) balance sheet losses. It also provides quantitative evidence on equilibrium effects of the macroprudential regulation of nonbanks, which we illustrate by varying investment fund cash buffers. JEL Classification: G17, G21, G22, G23
    Keywords: COVID-19, market-based finance, stress testing, systemic risk
    Date: 2022–06
  14. By: Dertwinkel-Kalt, Markus; Kasinger, Johannes; Schneider, Dmitrij
    Abstract: We investigate what statistical properties drive risk-taking in a large set of observational panel data on online poker games (n=4,450,585). Each observation refers to a choice between a safe "insurance" option and a binary lottery of winning or losing the game. Our setting offers a real-world choice situation with substantial incentives where probability distributions are simple, transparent, and known to the individuals. We find that individuals reveal a strong and robust preference for skewness. The effect of skewness is most pronounced among experienced and losing players but remains highly significant for winning players, in contrast to the variance effect.
    Keywords: Online Poker,Risk Attitudes,Risk Preferences,Choice under Risk
    JEL: D01 D81 G40
    Date: 2022
  15. By: Kim Oosterlinck; Ariane Reyns; Ariane Szafarz
    Abstract: How do major disruptive events, such as wars, affect the correlations between gold, Bitcoin, and financial assets? We address this question by estimating a dynamic conditional correlation (DCC) model before and during the 2022 Russian invasion of Ukraine. The results show that, after the outbreak of the war, the correlation between gold and stock markets dropped, confirming the diversification potential of gold during crises. The correlation between Bitcoin and oil declined as well. Meanwhile, the gold/Bitcoin correlation slightly decreased. Overall, our preliminary evidence suggests that gold and Bitcoin act as complements—rather than substitutes—for diversification purposes during international crises.
    Keywords: Bitcoin; Gold; Portfolio diversification; 2022 Russian invasion
    JEL: G11 G15 F65 E44
    Date: 2022–06–29
  16. By: R. Vilela Mendes
    Abstract: The question of the volatility roughness is interpreted in the framework of a data-reconstructed fractional volatility model, where volatility is driven by fractional noise. Some examples are worked out and also, using Malliavin calculus for fractional processes, an option pricing equation and its solution are obtained.
    Date: 2022–06
  17. By: Michael T. Kiley
    Abstract: Recessions impose sizable hardship, with large increases in the unemployment rate and related dislocations. In addition, recessions can lead to large shifts in financial markets. As a result, economists and financial market professionals have considered prediction models to assess the probability of a recession.
    Date: 2022–06–21

This nep-rmg issue is ©2022 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.