nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒04‒11
eighteen papers chosen by

  1. Sensitivity to large losses and $\rho$-arbitrage for convex risk measures By Martin Herdegen; Nazem Khan
  2. Optimal market completion through financial derivatives with applications to volatility risk By Matt Davison; Marcos Escobar-Anel; Yichen Zhu
  3. The impact of ESG ratings on implied and historical volatility By Burger, Eric; Grba, Fabian; Heidorn, Thomas
  4. Risk-Taking, Competition and Uncertainty: Do Contingent Convertible (CoCo) Bonds Increase the Risk Appetite of Banks? By Mahmoud Fatouh; Ioana Neamtu; Sweder van Wijnbergen
  5. Imperfect Competition in Derivatives Markets By Christina Brinkmann
  6. Updated Primer on the Forward-Looking Analysis of Risk Events (FLARE) Model: A Top-Down Stress Test Model By Sergio A. Correia; Matthew P. Seay; Cindy M. Vojtech
  7. Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war By Alhonita YATIE
  8. The Fragility of Market Risk Insurance By Ralph Koijen; Motohiro Yogo
  9. Optimal Liquidity Control and Systemic Risk in an Interbank Network with Liquidity Shocks and Regime-dependent Interconnectedness By Chotipong Charoensom; Thaisiri Watewai
  10. Can Volatility Solve the Naive Portfolio Puzzle? By Michael Curran; Patrick O'Sullivan; Ryan Zalla
  11. Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data By Mehmet Balcilar; Rangan Gupta; Christian Pierdzioch
  12. Insuring Longevity Risk and Long-Term Care: Bequest, Housing and Liquidity By Mengyi Xu; Jennifer Alonso Garcia; Michael Sherris; Adam Shao
  13. Optimal pooling and distributed inference for the tail index and extreme quantiles By Daouia, Abdelaati; Padoan, Simone A.; Stupfler, Gilles
  14. Resolution of Final Crises By Sebastián Fanelli; Martín Gonzalez-Eiras
  15. A Cautionary Tale of Fat Tails By Chetan Dave; Scott J. Dressler; Samreen Malik
  16. Computing Black Scholes with Uncertain Volatility-A Machine Learning Approach By Kathrin Hellmuth; Christian Klingenberg
  17. Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data By Bu, R.; Li, D.; Linton, O.; Wang, H.
  18. Hidden defaults By Horn, Sebastian; Reinhart, Carmen M.; Trebesch, Christoph

  1. By: Martin Herdegen; Nazem Khan
    Abstract: This paper studies mean-risk portfolio selection in a one-period financial market, where risk is quantified by a star-shaped risk measure $\rho$. We introduce two new axioms: weak and strong sensitivity to large losses. We show that the first axiom is key to ensure the existence of optimal portfolios and the second one is key to ensure the absence of $\rho$-arbitrage. This leads to a new class of risk measures that are suitable for portfolio selection. We show that $\rho$ belongs to this class if and only if $\rho$ is real-valued and the smallest positively homogeneous risk measure dominating $\rho$ is the worst-case risk measure. We then specialise to the case that $\rho$ is convex and admits a dual representation. We derive necessary and sufficient dual characterisations of (strong) $\rho$-arbitrage as well as the property that $\rho$ is suitable for portfolio selection. Finally, we introduce the new risk measure of Loss Sensitive Expected Shortfall, which is similar to and not more complicated to compute than Expected Shortfall but suitable for portfolio selection -- which Expected Shortfall is not.
    Date: 2022–02
  2. By: Matt Davison; Marcos Escobar-Anel; Yichen Zhu
    Abstract: This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We introduce an efficient and accurate simulation-based method, applicable to generalized diffusion models, to approximate the optimal derivatives-based portfolio strategy. We build upon the double optimization approach (i.e. expected utility maximization and risk exposure minimization) proposed in Escobar-Anel et al. (2022); demonstrating that strangle options are the best choices for market completion within equity options. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when only long-term maturity equity options are available.
    Date: 2022–02
  3. By: Burger, Eric; Grba, Fabian; Heidorn, Thomas
    Abstract: The economic and political developments of the past years show an increasing importance of a possible risk-reducing of the company due to good ESG performance. Our work contributes by examining the impact of relatively better ESG performance of companies on their implied and historical share volatility. Our regressions show a clear relationship between the volatilities and the ESG ratings of the market-leading agencies (Bloomberg, Refinitiv, Sustainalytics and MSCI) and our combined score. A better ESG performance measured by the company's ESG rating(s) has a risk-reducing effect in the form of lower stock volatility. However, our combined rating has the strongest impact.
    Keywords: Environmental,Social and Governance (ESG),ESG Ratings,ESG Rating Filter,Equity Volatility,Historical Volatility,Implied Volatility,Company Risk Performance
    JEL: G11 G24 G32 Q56
    Date: 2022
  4. By: Mahmoud Fatouh (University of Essex); Ioana Neamtu (Bank of England); Sweder van Wijnbergen (University of Amsterdam)
    Abstract: We assess the impact of contingent convertible (CoCo) bonds and the wealth transfers they imply conditional on conversion on the risk-taking behaviour of the issuing bank. We also test for regulatory arbitrage: do banks try to maintain risk-taking incentives by issuing CoCo bonds, when regulators reduce them through higher capitalization ratios? While we test for, and reject sample selection bias, we show that CoCo bonds issuance has a strong positive effect on risk-taking behaviour, particularly with conversion parameters that reduce dilution of existing shareholders upon conversion. Higher economic volatility amplifies the impact of CoCo bonds on risk-taking.
    Keywords: contingent convertible bonds, risk-taking, bank capital structure, selection bias
    JEL: G01 G11 G21 G32
    Date: 2022–02–27
  5. By: Christina Brinkmann (University of Bonn)
    Abstract: Since the push towards central clearing in derivatives markets after the global financial crisis, an open question has been how the development has affected competition. This paper models imperfect competition between dealers in derivatives markets. Two risk-neutral dealers offer derivatives to risk-averse clients with a hedging need, and compete in price (fee) and quality (default probability). I find that with such two-dimensional competition, for given default probabilities, an equilibrium in prices exists that is preferred by both dealers. In this equilibrium the dealer with the lower default probability makes larger profits - a feature, that can produce market discipline to keep the own default probability low. If a central counterparty (CCP) is introduced as an innovation that removes the quality dimension of the competition, this market force pushing for higher qualities vanishes.
    Keywords: Derivatives, OTC Markets, Central Clearing, Imperfect Competition, Vertical Product Differentiation
    JEL: G12 G23 G28 L13 L15
    Date: 2022–03
  6. By: Sergio A. Correia; Matthew P. Seay; Cindy M. Vojtech
    Abstract: This is an updated technical note describes the Forward-Looking Analysis of Risk Events (FLARE) model, which is a top-down model that helps assess how well the banking system is positioned to weather exogenous macroeconomic shocks. FLARE estimates banking system capital under varying macroeconomic scenarios, time horizons, and other systemic shocks.
    Keywords: Bank capital; Financial insitutions; Stress test
    JEL: G21
    Date: 2022–03–04
  7. By: Alhonita YATIE
    Abstract: This paper studies the impact of fear, uncertainty and market volatility caused by the Ukraine-Russia war on crypto-assets returns (Bitcoin and Ethereum) and Gold returns. We use the searches on Wikipedia trends as proxies of uncertainty and fear and two volatility indices: S&P500 VIX and the Russian VIX (RVIX). The results show that Bitcoin, Ethereum and Gold failed as safe havens during this war.
    Keywords: War, Russia, Ukraine, crypto-assets, Gold, Safe haven
    JEL: H56 G32 G12 G15
    Date: 2022
  8. By: Ralph Koijen (University of Chicago); Motohiro Yogo (Princeton University)
    Abstract: Variable annuities, which package mutual funds with minimum return guarantees over long horizons, accounted for $1.5 trillion or 35% of U.S. life insurer liabilities in 2015. Sales decreased and fees increased during the global financial crisis, and insurers made guarantees less generous or stopped offering guarantees to reduce risk exposure. These effects persist in the low interest rate environment after the global financial crisis, and variable annuity insurers suffered large equity drawdowns during the COVID-19 crisis. We develop and estimate a model of insurance markets in which financial frictions and market power determine pricing, contract characteristics, and the degree of market completeness.
    Keywords: Insurance, Financial Crisis, Risk
    JEL: G22 G32
    Date: 2022–03
  9. By: Chotipong Charoensom; Thaisiri Watewai
    Abstract: We propose a novel interbank network model in which banks face systemic liquidity shocks, fight-to-quality liquidity flows, and collapses of the interbank network during crises, and study their impacts on the optimal liquidity control and the systemic risk of the interbank network. We find that banks respond to negative shocks by holding positive precautionary liquidity, but once the shock size is sufficiently large, the benefit of precautionary liquidity reduces, and banks lower their precautionary liquidity. Lending (borrowing) banks also hold positive (negative) interbank liquidity provision. Banks hold more provision for more interconnected networks, but when the network is too interconnected, it is too costly to hold large provision, causing banks to lower the provision. On the contrary, a higher degree of the fight-to-quality effect tends to make banks act more aggressively on both precautionary liquidity and interbank provision. As a result, the systemic risk tends to increase in the size of the negative shock, but is quite insensitive to the degree of the fight-to-quality effect. Our analysis shows that the systemic risk increases if the interbank market collapses or becomes too interconnected during crises. Rewards and penalties from regulators can help reduce the systemic risk, but they come with a cost and have different implications on the banks' optimal policies.
    Keywords: Liquidity shock; Interbank Interconnectedness; Fight-to-quality; Systemic risk; Precautionary liquidity; Interbank liquidity provision; Regime switching; Stochastic control
    Date: 2022–03
  10. By: Michael Curran (Department of Economics, Villanova School of Business, Villanova University); Patrick O'Sullivan (Schroders Investment Management, 1 London Wall Place, London, UK.); Ryan Zalla (Economics Department, University of Pennsylvania, 133 South 36th Street, Philadelphia, PA 19104, USA.)
    Abstract: We investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of portfolios and econometric models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often out-perform the naive portfolio across multiple datasets and assessment criteria.
    Keywords: mean-variance, naive portfolio, volatility
    JEL: G11 G17
    Date: 2022–02
  11. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Turkish Republic of North Cyprus, Via Mersin 10, Famagusta 99628, Turkey; Department of Economics, OSTIM Technical University, Ankara 06374, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: We investigate whether oil-price uncertainty helps in forecasting international stock returns of ten advanced and emerging countries. We consider an out-of-sample period of 1925:08 to 2021:09, with an in-sample period 1920:08-1925:07, and employ a quantile-predictiveregression approach, which is more informative relative to a linear model, as it investigates the ability of oil-price uncertainty to forecast the entire conditional distribution of stock returns, rather than only its conditional-mean. A quantile-based approach accounts for nonlinearity (including regime changes), non-normality, and outliers. Based on a recursive estimation scheme, we draw the following main conclusions: the quantile-predictiveregression approach using oil-price uncertainty as a predictor statistically outperforms the corresponding quantile-based constant-mean model for all ten countries at certain quantiles (capturing normal, bear, and bull markets), and over specific forecast horizons, compared to forecastability being detected for eight countries under the linear predictive model. Moreover, we detect forecasting gains in many more horizons (at particular quantiles) compared to the linear case. In addition, an oil-price uncertainty-based state-contingent spillover analysis reveals that the ten equity markets are tighter connected during the upper regime, suggesting that heightened oil-market volatility erodes the benefits from diversification across equity markets.
    Keywords: international stock markets, oil price uncertainty, forecasting, quantile regression
    JEL: C22 C53 G15 Q41
    Date: 2022–03
  12. By: Mengyi Xu; Jennifer Alonso Garcia; Michael Sherris; Adam Shao
    Date: 2022
  13. By: Daouia, Abdelaati; Padoan, Simone A.; Stupfler, Gilles
    Abstract: This paper investigates pooling strategies for tail index and extreme quantile estimation from heavy-tailed data. To fully exploit the information contained in several samples, we present general weighted pooled Hill estimators of the tail index and weighted pooled Weissman estimators of extreme quantiles calculated through a nonstandard geometric averaging scheme. We develop their large-sample asymptotic theory across a fixed number of samples, covering the general framework of heterogeneous sample sizes with di↵erent and asymptotically dependent distributions. Our results include optimal choices of pooling weights based on asymptotic variance and MSE minimization. In the important application of distributed inference, we prove that the variance-optimal distributed estimators are asymptotically equivalent to the benchmark Hill and Weissman estimators based on the unfeasible combination of subsamples, while the AMSE-optimal distributed estimators enjoy a smaller AMSE than the benchmarks in the case of large bias. We consider additional scenarios where the number of subsamples grows with the total sample size and e↵ective subsample sizes can be low. We extend our methodology to handle serial dependence and the presence of covariates. Simulations confirm the statistical inferential theory of our pooled estimators. Two applications to real weather and insurance data are showcased.
    Keywords: Extreme values ; Heavy tails ; Distributed inference ; Pooling ; Testing
    Date: 2022–03–21
  14. By: Sebastián Fanelli (CEMFI, Centro de Estudios Monetarios y Financieros); Martín Gonzalez-Eiras (University of Copenhagen)
    Abstract: A financial crisis creates substantial wealth losses. How these losses are allocated determines the magnitude of the crisis and the path to recovery. We study how institutions and technological factors that shape default and debt restructuring decisions affect the amplification of aggregate shocks. For sufficiently large shocks, agents renegotiate. This limits the losses borne by borrowers, shutting the amplification mechanism via asset prices. The range of shocks that trigger renegotiation is decreasing in repossession costs and increasing in default costs, if the latter are public information. Private information may induce equilibrium default but, by allowing agents with high default costs to extract a larger haircut, facilitates the recovery. The model is consistent with evidence from real estate markets in the U.S. during the Great Recession; and rationalizes recent changes in U.S. Bankruptcy Code in the wake of the COVID-19 crisis.
    Keywords: Financial crises, balance sheet recessions, default, renegotiation
    JEL: E32 E44 G01
    Date: 2021–12
  15. By: Chetan Dave (Economics Department, University of Alberta); Scott J. Dressler (Department of Economics, Villanova School of Business, Villanova University); Samreen Malik (NYU Abu Dhabi, Division of Social Sciences)
    Abstract: Several macroeconomic time series exhibit excess kurtosis or "Fat Tails" possibly due to rare but large shocks (i.e., tail events). We document the extent to which tail events are attributable to long-run growth shocks. We show that excess kurtosis is not a uniform characteristic of postwar US data, but attributable to episodes containing well-documented growth shocks. A general equilibrium model captures these observations assuming Gaussian business-cycle shocks and a single growth shock from various sources. The model matches the data best with a growth shock to labor productivity while investment-specific technology shocks drive cycles.
    Keywords: fat tails, growth shocks, real business cycles
    JEL: E0 E3
    Date: 2022–03
  16. By: Kathrin Hellmuth; Christian Klingenberg
    Abstract: In financial mathematics, it is a typical approach to approximate financial markets operating in discrete time by continuous-time models such as the Black Scholes model. Fitting this model gives rise to difficulties due to the discrete nature of market data. We thus model the pricing process of financial derivatives by the Black Scholes equation, where the volatility is a function of a finite number of random variables. This reflects an influence of uncertain factors when determining volatility. The aim is to quantify the effect of this uncertainty when computing the price of derivatives. Our underlying method is the generalized Polynomial Chaos (gPC) method in order to numerically compute the uncertainty of the solution by the stochastic Galerkin approach and a finite difference method. We present an efficient numerical variation of this method, which is based on a machine learning technique, the so-called Bi-Fidelity approach. This is illustrated with numerical examples.
    Date: 2022–02
  17. By: Bu, R.; Li, D.; Linton, O.; Wang, H.
    Abstract: In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For the highfrequency data contaminated by the microstructure noise, we introduce a localised pre-averaging estimation method in the high-dimensional setting which first pre-whitens data via a kernel filter and then uses the estimation tool developed in the noise-free scenario, and further derive the uniform convergence rates for the developed spot volatility matrix estimator. In addition, we also combine the kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector, and establish the relevant uniform consistency result. Numerical studies are provided to examine performance of the proposed estimation methods in finite samples.
    Keywords: Brownian semi-martingale, Kernel smoothing, Microstructure noise, Sparsity, Spot volatility matrix, Uniform consistency
    JEL: C10 C14 C22
    Date: 2022–03–16
  18. By: Horn, Sebastian; Reinhart, Carmen M.; Trebesch, Christoph
    Abstract: China's lending boom to developing countries is morphing into defaults and debt distress. Given the secrecy surrounding China's loans, also the associated defaults remain 'hidden', as missed payments and restructuring details are not disclosed. We construct an encompassing dataset of sovereign debt restructurings with Chinese lenders and find that these credit events are surprisingly frequent, exceeding the number of sovereign bond or Paris Club restructurings. Chinese lenders follow a resolution approach reminiscent of 1980s Western lenders; they seldom provide deep debt relief with face value reduction. If history is any guide, multi-year debt workouts with serial restructurings lie in store.
    Keywords: China,external debt,default,crisis resolution,official lending,hidden debts,sovereign risk,Belt and Road initiative
    JEL: F21 F34 F42 F6 G15 H63 N25
    Date: 2022

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