nep-rmg New Economics Papers
on Risk Management
Issue of 2018‒10‒08
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. Robustness in the Optimization of Risk Measures By Paul Embrechts; Alexander Schied; Ruodu Wang
  2. Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model By Mengheng Li; Marcel Scharth
  3. Serially Dependent Extreme Events in Agricultural Commodity Futures Markets By Park, Eunchun; Maples, Joshua
  4. ExpectHill estimation, extreme risk and heavy tails By Daouia, Abdelaati; Girard, Stéphane; Stupfler, Gilles
  5. A Multi-Criteria Financial and Energy Portfolio Analysis of Hedge Fund Strategies By David E. Allen; Michael McAleer; Abhay K. Singh
  6. Pre-Harvest Risk Management for Kentucky Grain Farms By Davis, Todd D.; Schwenke, Eric
  7. Assessing the importance of the choice threshold in quantifying market risk under the POT method (EVT) By Sonia Benito Muela; Carmen López-Martín; Mª Ángeles Navarro
  8. Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations By Marius Pfeuffer; Goncalo dos Reis; Greig smith
  9. Firm-Level Political Risk and Asymmetric Volatility By Goodness C. Aye; Mehmet Balcilar; Riza Demirer; Rangan Gupta
  10. Measuring and trading volatility on the US stock market: A regime switching approach By José P. Dapena; Juan A. Serur; Julián R. Siri
  11. Mitigating counterparty risk By Gündüz, Yalin
  12. A Numerical Study of Carr and Lee's Correlation Immunization Strategy for Volatility Derivatives By Jimin Lin; Matthew Lorig
  13. Tail probabilities for short-term returns on stocks By Henrik O. Rasmussen; Paul Wilmott
  14. Measuring Network Systemic Risk Contributions: A Leave-one-out Approach By Sullivan HUE; Yannick LUCOTTE; Sessi TOKPAVI
  15. Long Run Returns Predictability and Volatility with Moving Averages By Chia-Lin Chang; Jukka Ilomäki; Hannu Laurila; Michael McAleer
  16. Risk sharing for capital requirements with multidimensional security markets By Felix-Benedikt Liebrich; Gregor Svindland
  17. Inferring short-term volatility indicators from Bitcoin blockchain By Nino Antulov-Fantulin; Dijana Tolic; Matija Piskorec; Zhang Ce; Irena Vodenska
  18. Monotone Sharpe ratios and related measures of investment performance By Mikhail Zhitlukhin
  19. Climate and Crop Insurance: Agricultural Risk Management into the 21st Century By Crane-Droesch, Andrew; Marshall, Elizabeth; Riddle, Anne; Rosch, Stephanie D.; Cooper, Joseph C.; Wallander, Steven
  20. Optimal Reinsurance for Gerber-Shiu Functions in the Cramer-Lundberg Model By Michael Preischl; Stefan Thonhauser
  21. Time Diversification: Perspectives from the Economic Index of Riskiness By Lu, Richard; Yang, Chen-Chen; Wong, Wing-Keung
  22. Nonparametric Estimation and Inference of Production Risk with Categorical Variables By Li, Zheng; Rejesus, Roderick M.; Zheng, Xiaoyong

  1. By: Paul Embrechts; Alexander Schied; Ruodu Wang
    Abstract: In this paper, we study issues of robustness in the context of Quantitative Risk Management. Depending on the underlying objectives, we develop a general methodology for determining whether a given risk measurement related optimization problem is robust. Motivated by practical issues from financial regulation, we give special attention to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We discover that for many simple representative optimization problems, VaR generally leads to non-robust optimizers whereas ES generally leads to robust ones. Our results thus shed light from a new angle on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are discovered.
    Date: 2018–09
  2. By: Mengheng Li (Economics Discipline Group, University of Technology, Sydney); Marcel Scharth (School of Economics,University of Sydney, Sydney)
    Abstract: We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry and heavy tails across all systematic and idiosyncratic components of the model. This framework accounts for well-documented features of univariate financial time series, while introducing a flexible dependence structure that incorporates tail dependence and asymmetries such as stronger correlations following downturns. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior simulation based on the particle Gibbs, ancestor sampling, and particle efficient importance sampling methods. We build computationally efficient model selection into our estimation framework to obtain parsimonious specifications in practice. We validate the performance of our proposed estimation method via extensive simulation studies for univariate and multivariate simulated datasets. An empirical study shows that the model outperforms other multivariate models in terms of value-at-risk evaluation and portfolio selection performance for a sample of US and Australian stocks.
    Keywords: Generalised hyperbolic skew Student’s t-distribution; Metropolis-Hastings algorithm; Importance sampling; Particle filter; Particle Gibbs; State space model; Time-varying covariance matrix; Factor model
    JEL: C11 C32 C53 G32
    Date: 2018–08–24
  3. By: Park, Eunchun; Maples, Joshua
    Abstract: Extreme price changes have become increasingly common in agricultural commodity futures markets. Many empirical studies have shown that agricultural commodity futures returns are not normally distributed and are heavy-tailed. However, most of the studies do not allow for stochastic dependence of extreme events over time. Statistical tools based on Extreme Value Theory can be utilized to model tail risk in agricultural markets. In this paper, we employ a Bayesian hierarchical model for serially-dependent extreme commodity futures price changes. The model assumes that the distribution of marginal price returns follows the generalized Pareto distribution (GPD), and reflects a serial dependence structure in tail distribution. The model proposed here allows both the parameters in the serial dependence function and the marginal GPD to vary over time. Thus, the model provides important information on changes in the shape of the heavy-tailed distribution. For empirical analysis, we use daily futures prices for corn. Based on our preliminary results, recent years have seen considerable increases in the probability of an extreme price decline in several commodity markets. These results have implications for risk management strategies as well as the design and effectiveness of federal insurance programs.
    Keywords: Agricultural Finance, Research Methods/ Statistical Methods, Risk and Uncertainty
    Date: 2018–01–17
  4. By: Daouia, Abdelaati; Girard, Stéphane; Stupfler, Gilles
    Abstract: Risk measures of a financial position are traditionally based on quantiles. Replacing quantiles with their least squares analogues, called expectiles, has recently received increasing attention. The novel expectile-based risk measures satisfy all coherence requirements. We revisit their extreme value estimation for heavy-tailed distributions. First, we estimate the underlying tail index via weighted combinations of top order statistics and asymmetric least squares estimates. The resulting expectHill estimators are then used as the basis for estimating tail expectiles and Expected Shortfall. The asymptotic theory of the proposed estimators is provided, along with numerical simulations and applications to actuarial and financial data.
    Keywords: Asymmetric least squares; Coherent risk measures; Expected shortfall; Expectile; Extrapolation; Extremes; Heavy tails; Tail index
    JEL: C13 C14
    Date: 2018–09
  5. By: David E. Allen (School of Mathematics and Statistics, University of Sydney, Australia, Department of Finance, Asia University, Taiwan, and School of Business and Law, Edith Cowan University, Western Australia.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.); Abhay K. Singh (Department of Applied Finance, Macquarie University, Australia.)
    Abstract: The paper is concerned with a multi-criteria portfolio analysis of hedge fund strategies that are concerned with financial commodities, including the possibility of energy spot, futures and exchange traded funds (ETF). It features a tri-criteria analysis of the Eurekahedge fund data strategy index data. We use nine Eurekahedge equally weighted main strategy indices for the portfolio analysis. The tri-criteria analysis features three objectives: return, risk and dispersion of risk objectives in a Multi-Criteria Optimisation (MCO) portfolio analysis. We vary the MCO return and risk targets, and contrast the results with four more standard portfolio optimisation criteria, namely tangency portfolio (MSR), most diversified portfolio (MDP), global minimum variance portfolio (GMW), and portfolios based on minimising expected shortfall (ERC). Backtests of the chosen portfolios for this hedge fund data set indicate that the use of MCO is accompanied by uncertainty about the a priori choice of optimal parameter settings for the decision criteria. The empirical results do not appear to outperform more standard bi-criteria portfolio analyses in the backtests undertaken on the hedge fund index data.
    Abstract: El documento se refiere a un análisis de cartera de criterios múltiples de estrategias de fondos de cobertura que se ocupan de los productos financieros, incluida la posibilidad de fondos spot, futuros y fondos cotizados en bolsa (ETF). Presenta un análisis de tres criterios de los datos del índice de estrategia de datos del fondo Eurekahedge. Utilizamos nueve índices de estrategia principales igualmente ponderados de Eurekahedge para el análisis de cartera. El análisis de tres criterios presenta tres objetivos: retorno, riesgo y dispersión de los objetivos de riesgo en un análisis de cartera de Optimización Multi-Criterios (MCO). Variamos los de rentabilidad y riesgo objetivos MCO, y contrastar los resultados con otros cuatro criterios de optimización de la cartera estándar, a saber cartera de tangencia (MSR), la cartera más diversificada (MDP), portafolio de mínima varianza mundial (SMG), y carteras basados ​​en la minimización de que exista un déficit (ERC) Las pruebas retrospectivas de las carteras elegidas para este conjunto de datos de fondos de cobertura indican que el uso de MCO va acompañado de incertidumbre acerca de la elección a priori de ajustes de parámetros óptimos para los criterios de decisión. Los resultados empíricos no parecen superar el rendimiento de análisis de cartera bi-criterio más estándar en los backtest realizados sobre los datos del índice de fondos de cobertura.
    Keywords: MCO; Portfolio Analysis; Hedge Fund Strategies; Multi-Criteria Optimisation; Genetic Algorithms; Spot prices; Futures pricees; Exchange Traded Funds (ETF).
    JEL: G15 G17 G32 C58 D53
    Date: 2018–06
  6. By: Davis, Todd D.; Schwenke, Eric
    Abstract: Corn, soybean, and wheat pre-harvest price risk management strategies are evaluated using data from 2001 to 2017 crop years. A comparison of seasonality of the December corn, November soybeans, and July wheat futures contracts is used to determine potential months to implement hedges. The value of hedges is compared to use of cash forward contracts or cash sales at harvest.
    Keywords: Farm Management, Risk and Uncertainty
    Date: 2018–01–17
  7. By: Sonia Benito Muela (Department of Economic Analysis Faculty of Economics and Business Administration National Distance Education University (UNED).); Carmen López-Martín (Department of Business and Accounting Faculty of Economics and Business Administration National Distance Education University (UNED).); Mª Ángeles Navarro (PhD. Student of the Faculty of Economics and Business Administration National Distance Education University (UNED).)
    Abstract: The conditional extreme value theory has been proven to be one of the most successful in estimating market risk. The implementation of this method in the framework of the Peaks Over Threshold (POT) model requires one to choose a threshold for fitting the generalized Pareto distribution (GPD). In this paper, we investigate whether the selection of the threshold is important for the quantification of market risk. For measuring risk, we use the value at risk (VaR) measure and the expected shortfall (ES) measure. The study has been done for a large set of assets. The results obtained indicate that the quantification of the market risk through the VaR and ES measures does not depend on the threshold selected. This result is also found in a smaller sample.
    Keywords: Extreme Value Theory; Peaks over Threshold; Value at Risk; Expected Shortfall; Generalized Pareto Distribution.
    JEL: G19 G29
    Date: 2018–09
  8. By: Marius Pfeuffer; Goncalo dos Reis; Greig smith
    Abstract: This paper focuses on estimating, in Markov and non-Markov setups, rating transition probabilities crucial in financial regulation. We first deal with the estimation of a continuous time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computation time of Wald confidence intervals to less than half of the current standard. We provide an efficient procedure to transfer such uncertainties to the rating migrations and probabilities of default, which is of usefulness for practitioners. When a full data set is available, we propose a tractable and parsimonious model based on self-exciting marked point processes that captures the non-Markovian effect of rating momentum. Compared to the Markov model, the non-Markov model yields higher probabilities of default in the investment grades, but also lower default probabilities in some speculative grades. This agrees with empirical observations and has clear practical implications. We illustrate all methods using data from Moody's proprietary corporate credit ratings data set. Implementations are available in the R package ctmcd.
    Date: 2018–09
  9. By: Goodness C. Aye (Department of Economics, University of Pretoria, Pretoria, South Africa); Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, Northern Cyprus, Turkey; Department of Economics, University of Pretoria, Pretoria, South Africa and Montpellier Business School, Montpellier, France.); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper examines whether proxies of political risk exposure at the firm-level can predict the aggregate stock market volatility. Utilizing a nonparametric causality-in-quantiles test which not only guards against misspecification due to nonlinearity, but also tests for causality over the entire conditional distribution of the realized volatilities, we show that political risk exposure can serve as a strong predictor of bad realized volatility, while the causal effects are non-existent in the case of overall and good realized volatilities. Our findings provide novel insight to the well documented asymmetric volatility puzzle and the effect of political uncertainty on stock market fluctuations via the investor attention channel. The results also suggest that political risk exposure could be a contributing factor to jump risk in the cross-section of returns.
    Keywords: Aggregate Realized Volatility; Firm-Level Political Risk, Quantile Causality, S&P 500.
    JEL: C22 G1
    Date: 2018–09
  10. By: José P. Dapena; Juan A. Serur; Julián R. Siri
    Abstract: The volatility premium is a well-documented phenomenon, which can be approximated by the difference between the previous month level of the VIX Index and the rolling 30-day close-to-close volatility. Along with the literature, we show evidence that VIX is generally above the 30-day rolling volatility giving rise to the volatility premium, so selling volatility can become a profitable trading strategy as long as proper risk management is under place. As a contribution, we introduced the implementation of a Hidden Markov Model (HMM), identifying two states of the nature and showing that the volatility premium undergoes temporal breaks in its behavior. Based on this, we formulate a trading strategy by selling volatility and switching to medium-term U.S. Treasury Bills when appropriated. We test the performance of the strategy using the conventional Carhart four-factor model showing a positive and statistically significant alpha.
    Keywords: Realized volatility, expected volatility, volatility premium, regime switching, excess returns, hidden Markov model, VIX.
    JEL: C1 C3 N2 G11
    Date: 2018–09
  11. By: Gündüz, Yalin
    Abstract: This paper provides initial evidence on counterparty risk-mitigation activities of financial institutions on the basis of Depository Trust and Clearing Corporation's (DTCC) proprietary bilateral credit default swap transactions and positions. We show that financial institutions that are active buyers of protection from a specific counterparty undertake successive contracts and purchase protection written on them, even avoiding wrong-way risk mitigation. Higher stock return and CDS price volatility, lower past stock returns, and higher CDS prices of the counterparty are shown to have an increasing effect on the hedging behaviour against the counterparty. As the current regulatory frameworks explicitly formulate any protection purchase on the counterparty would diminish the required capital, this type of risk mitigation could follow regulatory capital relief motives and provides a viable hedging instrument beyond receiving coverage through collateral.
    Keywords: credit default swaps,DTCC,OTC markets,hedging,Basel III,CRR
    JEL: G11 G21 G23
    Date: 2018
  12. By: Jimin Lin; Matthew Lorig
    Abstract: In their seminal work `Robust Replication of Volatility Derivatives,' Carr and Lee show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price. Additionally, they propose a correlation immunization method that minimizes the pricing and hedging error that results when the correlation between the risky asset's price and volatility is nonzero. In this paper, we perform a number of Monte Carlo experiments to test the effectiveness of Carr and Lee's immunization strategy. Our results indicate that the correlation immunization method is an effective means of reducing pricing and hedging errors that result from nonzero correlation.
    Date: 2018–09
  13. By: Henrik O. Rasmussen; Paul Wilmott
    Abstract: We consider the tail probabilities of stock returns for a general class of stochastic volatility models. In these models, the stochastic differential equation for volatility is autonomous, time-homogeneous and dependent on only a finite number of dimensional parameters. Three bounds on the high-volatility limits of the drift and diffusion coefficients of volatility ensure that volatility is mean-reverting, has long memory and is as volatile as the stock price. Dimensional analysis then provides leading-order approximations to the drift and diffusion coefficients of volatility for the high-volatility limit. Thereby, using the Kolmogorov forward equation for the transition probability of volatility, we find that the tail probability for short-term returns falls off like an inverse cubic. Our analysis then provides a possible explanation for the inverse cubic fall off that Gopikrishnan et al. (1998) report for returns over 5-120 minutes intervals. We find, moreover, that the tail probability scales like the length of the interval, over which the return is measured, to the power 3/2. There do not seem to be any empirical results in the literature with which to compare this last prediction.
    Date: 2018–09
  14. By: Sullivan HUE; Yannick LUCOTTE; Sessi TOKPAVI
    Date: 2018
  15. By: Chia-Lin Chang (Department of Applied Economics Department of Finance National Chung Hsing University, Taiwan.); Jukka Ilomäki (Faculty of Management University of Tampere, Finland.); Hannu Laurila (Faculty of Management University of Tampere, Finland.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)
    Abstract: The paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affect financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.
    Keywords: Trading strategies; Risk; Moving average; Market timing; Returns predictability; Volatility; Rolling window; Data frequency.
    JEL: C22 C32 C58 G32
    Date: 2018–09
  16. By: Felix-Benedikt Liebrich; Gregor Svindland
    Abstract: We consider the risk sharing problem for capital requirements induced by capital adequacy tests and security markets. The agents involved in the sharing procedure may be heterogeneous in that they apply varying capital adequacy tests and have access to different security markets. We discuss conditions under which there exists a representative agent. Thereafter, we study two frameworks of capital adequacy more closely, polyhedral constraints and distribution based constraints. We prove existence of optimal risk allocations and equilibria within these frameworks and elaborate on their robustness.
    Date: 2018–09
  17. By: Nino Antulov-Fantulin; Dijana Tolic; Matija Piskorec; Zhang Ce; Irena Vodenska
    Abstract: In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume.
    Date: 2018–09
  18. By: Mikhail Zhitlukhin
    Abstract: We introduce a new measure of performance of investment strategies, the monotone Sharpe ratio. We study its properties, establish a connection with coherent risk measures, and obtain an efficient representation for using in applications.
    Date: 2018–09
  19. By: Crane-Droesch, Andrew; Marshall, Elizabeth; Riddle, Anne; Rosch, Stephanie D.; Cooper, Joseph C.; Wallander, Steven
    Keywords: Natural Resource Economics, Risk and Uncertainty, Resource and Environmental Policy Analysis
    Date: 2018–06–20
  20. By: Michael Preischl; Stefan Thonhauser
    Abstract: Complementing existing results on minimal ruin probabilities, we minimize expected discounted penalty functions (or Gerber-Shiu functions) in a Cramer-Lundberg model by choosing optimal reinsurance. Reinsurance strategies are modelled as time dependant control functions, which leads to a setting from the theory of optimal stochastic control and ultimately to the problem's Hamilton-Jacobi-Bellman equation. We show existence and uniqueness of the solution found by this method and provide numerical examples involving light and heavy tailed claims and also give a remark on the asymptotics.
    Date: 2018–09
  21. By: Lu, Richard; Yang, Chen-Chen; Wong, Wing-Keung
    Abstract: Time diversification which is the idea of there being less riskiness over longer investment horizons is examined in this paper. Different from previous studies, this paper contributes to the literature by using the Aumann and Serrano index as a risk measure to examine whether there is any time diversification in stock investment by using the daily return of the S&P 500, the S&P 400, and the NASDAQ with both short and long holding periods and by using the block bootstrapping technique in the simulation. From returns of short (long) holding periods, we conclude that, in general, the riskiness of the shorter (longer) period is statistically greater than that of the longer (shorter) period. Our findings reject the hypothesis of no time diversification effect and reject the geometric Brownie motion process for the returns of different holding periods. The results could be due to short- and medium-term momentum and long-term contrarian. Our findings are useful to academics, investors, and policy makers in their decision making related to time diversification.
    Keywords: Time diversification, Economic index of riskiness, Investment horizon
    JEL: G11
    Date: 2018–10–01
  22. By: Li, Zheng; Rejesus, Roderick M.; Zheng, Xiaoyong
    Keywords: Research Methods/Econometrics/Stats, Risk and Uncertainty, Productivity Analysis and Emerging Technologies
    Date: 2018–06–20

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