nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒04‒25
25 papers chosen by
Stan Miles
Thompson Rivers University

  1. Risk Modelling and Management: An Overview By Chia-Lin Chang; David E. Allen; Michael McAleer; Teodosio Perez Amaral
  2. GFC-Robust Risk Management under the Basel Accord using Extreme Value Methodologies By Juan-Angel Jimenez-Martin; Michael McAleer; Teodosio Perez Amaral; Paulo Araujo Santos
  3. Time-consistency of risk measures with GARCH volatilities and their estimation By Claudia Kl\"uppelberg; Jianing Zhang
  4. Crowded Trades: An Overlooked Systemic Risk for Central Clearing Counterparties By Albert J. Menkveld
  5. Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics By Andre Lucas; Bernd Schwaab; Xin Zhang
  6. Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation By Lukasz Gatarek; Lennart Hoogerheide; Koen Hooning; Herman K. van Dijk
  7. A Capital Adequacy Buffer Model By David Allen; Michael McAleer
  8. Asymmetric Realized Volatility Risk By David E. Allen; Michael McAleer; Marcel Scharth
  9. Short-Selling, Leverage and Systemic Risk By Amelia Pais; Philip A. Stork
  10. Realized Volatility Risk By David E. Allen; Michael McAleer; Marcel Scharth
  11. A New Bootstrap Test for the Validity of a Set of Marginal Models for Multiple Dependent Time Series: An Application to Risk Analysis By David Ardia; Lukasz Gatarek; Lennart F. Hoogerheide
  12. Assessing coherent Value-at-Risk and expected shortfall with extreme expectiles By Daouia, Abdelaati; Girard, Stéphane; Stupfler, Gilles
  13. Advances in Financial Risk Management and Economic Policy Uncertainty: An Overview By Shawkat Hammoudeh; Michael McAleer
  14. Risk Measurement and Risk Modelling using Applications of Vine Copulas By David E. Allen; Michael McAleer; Abhay K. Singh
  15. Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities By Anne Opschoor; Dick van Dijk; Michel van der Wel
  16. A Test for the Portion of Bivariate Dependence in Multivariate Tail Risk By Carsten Bormann; Melanie Schienle; Julia Schaumburg
  17. Downside Variance Risk Premium By Feunou, Bruno; Jahan-Parvar, Mohammad; Okou, Cedric
  18. Score Driven exponentially Weighted Moving Average and Value-at-Risk Forecasting By André Lucas; Xin Zhang
  19. Global Credit Risk: World, Country and Industry Factors By Bernd Schwaab; Siem Jan Koopman; André Lucas
  20. Expected Utility and Catastrophic Risk By Masako Ikefuji; Roger Laeven; Jan Magnus; Chris Muris
  21. Comonotonic Approximations of Risk Measures for Variable Annuity Guaranteed Benefits with Dynamic Policyholder Behavior By Runhuan Feng; Xiaochen Jing; Jan Dhaene
  22. Econometric Analysis of Financial Derivatives: An Overview By Chia-Lin Chang; Michael McAleer
  23. Optimal Hedging with the Vector Autoregressive Model By Lukasz Gatarek; Søren Johansen
  24. Forecasting Value-at-Risk using Block Structure Multivariate Stochastic Volatility Models By Manabu Asai; Massimiliano Caporin; Michael McAleer
  25. Tail Mutual Exclusivity and Tail-Var Lower Bounds By Ka Chun Cheung; Michel Denuit; Jan Dhaene

  1. By: Chia-Lin Chang (National Chung Hsing University, Taiwan); David E. Allen (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Spain, and Kyoto University); Teodosio Perez Amaral (Complutense University of Madrid, Spain)
    Abstract: The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on “Risk Modelling and Management” (RMM2011). The papers cover the following topics: currency hedging strategies using dynamic multivariate GARCH, risk management of risk under the Basel Accord: A Bayesian approach to forecasting value-at-risk of VIX futures, fast clustering of GARCH processes via Gaussian mixture models, GFC-robust risk management under the Basel Accord using extreme value methodologies, volatility spillovers from the Chinese stock market to economic neighbours, a detailed comparison of Value-at-Risk estimates, the dynamics of BRICS's country risk ratings and domestic stock markets, U.S. stock market and oil price, forecasting value-at-risk with a duration-based POT method, and extreme market risk and extreme value theory.
    Keywords: Currency hedging strategies, Basel Accord, risk management, forecasting, VIX futures, fast clustering, mixture models, extreme value methodologies, volatility spillovers, Value-at-Risk, country risk ratings, BRICS, extreme market risk
    JEL: C14 C32 C53 C58 G11 G32
    Date: 2013–06–25
  2. By: Juan-Angel Jimenez-Martin (Complutense University of Madrid, Spain); Michael McAleer (Complutense University of Madrid, Spain, Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands, and Kyoto University, Japan); Teodosio Perez Amaral (Complutense University of Madrid, Spain); Paulo Araujo Santos (University of Lisbon, Portugal)
    Abstract: See the publication in <I>Mathematics and Computers in Simulation (MATCOM)</I> (2013). Volume 94(C), pages 223-237.<P> In this paper we provide further evidence on the suitability of the median of the point VaR forecasts of a set of models as a GFC-robust strategy by using an additional set of new extreme value forecasting models and by extending the sample period for comparison. These extreme value models include DPOT and Conditional EVT. Such models might be expected to be useful in explaining financial data, especially in the presence of extreme shocks that arise during a GFC. Our empirical results confirm that the median remains GFC-robust even in the presence of these new extreme value models. This is illustrated by using the S&P500 index before, during and after the 2008-09 GFC. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria, including several tests for independence of the violations. The strategy based on the median, or more generally, on combined forecasts of single models, is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions.
    Keywords: Value-at-Risk (VaR), DPOT, daily capital charges, robust forecasts, violation penalties, optimizing strategy, aggressive risk management, conservative risk management, Basel, global financial crisis
    JEL: G32 G11 G17 C53 C22
    Date: 2013–05–21
  3. By: Claudia Kl\"uppelberg; Jianing Zhang
    Abstract: In this paper we study time-consistent risk measures for returns that are given by a GARCH$(1,1)$ model. We present a construction of risk measures based on their static counterparts that overcomes the lack of time-consistency. We then study in detail our construction for the risk measures Value-at-Risk (VaR) and Average Value-at-Risk (AVaR). While in the VaR case we can derive an analytical formula for its time-consistent counterpart, in the AVaR case we derive lower and upper bounds to its time-consistent version. Furthermore, we incorporate techniques from Extreme Value Theory (EVT) to allow for a more tail-geared analysis of the corresponding risk measures. We conclude with an application of our results to stock prices to investigate the applicability of our results.
    Date: 2015–04
  4. By: Albert J. Menkveld (VU University Amsterdam, the Netherlands)
    Abstract: Counterparty default risk might hamper trade and trigger a financial crisis. The introduction of a central clearing counterparty (CCP) benefits trading but pushes systemic risk into CCP default. Standard risk management strategies at CCPs currently overlook a risk associated with crowded trades. This paper identifies it, measures it, and proposes a margin methodology that accounts for it. The application to actual CCP data illustrates that this hidden risk can become large, in particular at times of high CCP risk.
    Keywords: Financial economics
    JEL: G00
    Date: 2014–06–02
  5. By: Andre Lucas (VU University Amsterdam); Bernd Schwaab (European Central Bank, Financial Markets Research); Xin Zhang (VU University Amsterdam, and Sveriges Riksbank, Research Division)
    Abstract: We develop a novel high-dimensional non-Gaussian modeling framework to infer conditional and joint risk measures for many financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence parameters that naturally accommodates asymmetries, heavy tails, as well as non-linear and time-varying default dependence. We demonstrate how to apply a conditional law of large numbers in this setting to define risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple financial firm defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks during 2011-12, as well as their steep decline after subsequent policy actions.
    Keywords: systemic risk; dynamic equicorrelation model; generalized hyperbolic distribution; Law of Large Numbers
    JEL: G21 C32
    Date: 2013–05–13
  6. By: Lukasz Gatarek (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam); Lennart Hoogerheide (VU University Amsterdam); Koen Hooning (Delft University of Technology); Herman K. van Dijk (Econometric Institute, Erasmus University Rotterdam, and VU University Amsterdam)
    Abstract: Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a posterior in which the likelihood is replaced by the censored likelihood; and the censored predictive likelihood, which is used for Bayesian Model Averaging. We perform extensive experiments involving simulated and empirical data. Our results show the ability of these new approaches to outperform the standard posterior and traditional Bayesian Model Averaging techniques in applications of Value-at-Risk prediction in GARCH models.
    Keywords: censored likelihood, censored posterior, censored predictive likelihood, Bayesian Model Averaging, Value at Risk, Metropolis-Hastings algorithm.
    JEL: C11 C15 C22 C51 C53 C58 G17
    Date: 2013–04–15
  7. By: David Allen (University of South Australia, and University of Sydney, Australia); Michael McAleer (National Tsing Hua University Taiwan,)
    Abstract: In this paper, we develop a new capital adequacy buffer model (CABM) which is sensitive to dynamic economic circumstances. The model, which measures additional bank capital required to compensate for fluctuating credit risk, is a novel combination of the Merton structural model which measures distance to default and the timeless capital asset pricing model (CAPM) which measures additional returns to compensate for additional share price risk.
    Keywords: Credit risk, Capital buffer, Distance to default, Conditional value at risk, Capital adequacy buffer model
    JEL: G01 G21 G28
    Date: 2013–10–15
  8. By: David E. Allen (University of Sydney, and University of South Australia, Australia); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, Tinbergen Institute, the Netherlands; Complutense University Madrid, Spain); Marcel Scharth (University of New South Wales, Australia)
    Abstract: In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility, volatility of volatility, volatility risk, value-at-risk, forecasting, conditional heteroskedasticity
    JEL: C58 G12
    Date: 2014–06–23
  9. By: Amelia Pais (Massey University, College of Business, School of Economics and Finance, Auckland, New Zealand); Philip A. Stork (VU University Amsterdam, and Duisenberg School of Finance)
    Abstract: During the Global Financial Crisis, regulators imposed short-selling bans to protect financial institutions. The rationale behind the bans was that “bear raids”, driven by short-sellers, would increase the individual and systemic risk of financial institutions, especially for institutions with high leverage. This study uses Extreme Value Theory to estimate the effect of short-selling on financial institutions’ individual and systemic risks in France, Italy and Spain; it also analyses the relationship between financial institutions’ leverage and short-selling. The results show that short-sellers appear to specifically target institutions with lower capital levels. Furthermore, institutions’ risk-levels and changes in short-selling positions tend to move in tandem.
    Keywords: bear raids, short-selling bans, financial institutions’ risk, systemic risk, leverage capital requirements, Extreme Value Theory
    JEL: C14 G01 G15 G21
    Date: 2013–11–15
  10. By: David E. Allen (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, and Complutense University of Madrid); Marcel Scharth (University of New South Wales, Australia)
    Abstract: This discussion paper led to an article in the <I>Journal of Risk and Financial Management</I> (2014). Volume 7(2), pages 80-109.<P> In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility, volatility of volatility, volatility risk, value-at-risk, forecasting,
    JEL: C14 C22 C58 G15
    Date: 2013–07–16
  11. By: David Ardia (Laval University, Quebec, Canada); Lukasz Gatarek (Erasmus University Rotterdam); Lennart F. Hoogerheide (VU University Amsterdam)
    Abstract: A novel simulation-based methodology is proposed to test the validity of a set of marginal time series models, where the dependence structure between the time series is taken ‘directly’ from the observed data. The procedure is useful when one wants to summarize the test results for several time series in one joint test statistic and p-value. The proposed test method can have higher power than a test for a univariate time series, especially for short time series. Therefore our test for multiple time series is particularly useful if one wants to assess Value-at-Risk (or Expected Shortfall) predictions over a small time frame (e.g., a crisis period). We apply our method to test GARCH model specifications for a large panel data set of stock returns.
    Keywords: Bootstrap test, GARCH, marginal models, multiple time series, Value-at-Risk
    JEL: C1 C12 C22 C44
    Date: 2014–02–28
  12. By: Daouia, Abdelaati; Girard, Stéphane; Stupfler, Gilles
    Abstract: The class of quantiles lies at the heart of extreme-value theory and is one of the basic tools in risk management. The alternative family of expectiles is based on squared rather than absolute error loss minimization. The exibility and virtues of these least squares analogues of quantiles are now well established in actuarial science, econo- metrics and statistical finance. Both quantiles and expectiles were embedded in the more general class of M-quantiles as the minimizers of a generic asymmetric convex loss function. It has been proved very recently that the only M-quantiles that are coherent risk measures are the expectiles. Also, in contrast to the quantile-based ex- pected shortfall, expectiles benefit from the important property of elicitability that corresponds to the existence of a natural backtesting methodology. Least asymmetri- cally weighted squares estimation of expectiles did not, however, receive yet as much attention as quantile-based risk measures from the perspective of extreme values. In this article, we develop new methods for estimating the Value-at-Risk and expected shortfall measures via high expectiles. We focus on the challenging domain of attrac- tion of heavy-tailed distributions that better describe the tail structure and sparseness of most actuarial and financial data. We first estimate the intermediate large expec- tiles and then extrapolate these estimates to the very far tails. We establish the limit distributions of the proposed estimators when they are located in the range of the data or near and even beyond the maximum observed loss. Monte Carlo experiments and a concrete application are given to illustrate the utility of extremal expectiles as an efficient instrument of risk protection.
    Keywords: Asymmetric squared loss; Coherent Value-at-Risk; Expected shortfall; Expectiles; Extrapolation; Extreme value theory; Heavy tails.
    Date: 2015–04
  13. By: Shawkat Hammoudeh (Drexel University, Philadelphia, United States); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Italy)
    Abstract: Financial risk management is difficult at the best of times, but especially so in the presence of economic uncertainty and financial crises. The purpose of this special issue on “Advances in Financial Risk Management and Economic Policy Uncertainty” is to highlight some areas of research in which novel econometric, financial econometric and empirical finance methods have contributed significantly to the analysis of financial risk management when there is economic uncertainty, especiallythe power of print: uncertainty shocks, markets, and the economy, determinants of the banking spread in the Brazilian economy: the role of micro and macroeconomic factors, forecasting value-at-risk using block structure multivariate stochastic volatility models, the time-varying causality between spot and futures crude oil prices: a regime switching approach, a regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates, a practical approach to constructing price-based funding liquidity factors, realized range volatility forecasting: dynamic features and predictive variables, modelling a latent daily tourism financial conditions index, bank ownership, financial segments and the measurement of systemic risk: an application of CoVaR, model-free volatility indexes in the financial literature: a review, robust hedging performance and volatility risk in option markets: application to Standard and Poor’s 500 and Taiwan index options, price cointegration between sovereign CDS and currency option markets in the global financial crisis, whether zombie lending should always be prevented, preferences of risk-averse and risk-seeking investors for oil spot and futures before, during and after the global financial crisis, managing financial risk in Chinese stock markets: option pricing and modeling under a multivariate threshold autoregression, managing systemic risk in The Netherlands, mean-variance portfolio methods for energy policy risk management, on robust properties of the SIML estimation of volatility under micro-market noise and random sampling, asymmetric large-scale (I)GARCH with hetero-tails, the economic fundamentals and economic policy uncertainty of Mainland China and their impacts on Taiwan and Hong Kong, prediction and simulation using simple models characterized by nonstationarity and seasonality, and volatility forecast of stock indexes by model averaging using high frequency data.
    Keywords: Financial risk management, Economic policy uncertainty, Financial econometrics, Empirical finance
    JEL: C58 D81 E60 G32
    Date: 2014–06–23
  14. By: David E. Allen (School of Mathematics and Statistics, Sydney University, NSW, and Centre for Applied Financial Studies, University of South Australia, Adelaide, SA, Australia); Michael McAleer (National Tsing Hua University, Taiwan, and Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, and Tinbergen Institute, the Netherlands, and Department of Quantitative Economics, Complutense University of Madrid, Spain); Abhay K. Singh (School of Business, Edith Cowan University, Joondalup, WA, Australia)
    Abstract: This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite nancial risk. Copula-based dependence modelling is a popular tool in nancial applications, but is usually applied to pairs of securities. By contrast, Vine copulas provide greater exibility and permit the modelling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. The paper features the use of Regular Vine copulas in an analysis of the co-dependencies of 10 major European Stock Markets, as represented by individual market indices and the composite STOXX 50 index. The sample runs from 2005 to the end of 2011 to permit an exploration of how correlations change indierent economic circumstances using three dierent sample periods: pre-GFC pre-GFC (Jan 2005- July 2007), GFC (July 2007-Sep 2009), and post-GFC periods (Sep 2009 - Dec 2011). The empirical results suggest that the dependencies change in a complex manner, and are subject to change in dierent economic circumstances. One of the attractions of this approach to risk modelling is the exibility in the choice of distributions used to model co-dependencies. The practical application of Regular Vine metrics is demonstrated via an example of the calculation of the VaR of a portfolio made up of the indices.
    Keywords: Regular Vine Copulas, Tree structures, Co-dependence modelling, European stock markets
    JEL: G11 C02
    Date: 2014–05–08
  15. By: Anne Opschoor (VU University Amsterdam); Dick van Dijk (Erasmus University Rotterdam); Michel van der Wel (Erasmus University Rotterdam)
    Abstract: We investigate the added value of combining density forecasts for asset return prediction in a specific region of support. We develop a new technique that takes into account model uncertainty by assigning weights to individual predictive densities using a scoring rule based on the censored likelihood. We apply this approach in the context of recently developed univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and Nikkei stock market indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates of the individual models.
    Keywords: Density forecast evaluation, Volatility modeling, Censored likelihood, Value-at-Risk
    JEL: C53 C58 G17
    Date: 2014–07–21
  16. By: Carsten Bormann; Melanie Schienle (Leibniz Universität Hannover, Germany); Julia Schaumburg (VU University Amsterdam)
    Abstract: In practice, multivariate dependencies between extreme risks are often only assessed in a pairwise way. We propose a test to detect when tail dependence is truly high-dimensional and bivariate simplifications would produce misleading results. This occurs when a significant portion of the multivariate dependence structure in the tails is of higher dimension than two. Our test statistic is based on a decomposition of the stable tail dependence function, which is standard in extreme value theory for describing multivariate tail dependence. The asymptotic properties of the test are provided and a bootstrap based finite sample version of the test is suggested. A simulation study documents the good performance of the test for standard sample sizes. In an application to international government bonds, we detect a high tail{risk and low return situation during the last decade which can essentially be attributed to increased higher{order tail risk. We also illustrate the empirical consequences from ignoring higher-dimensional tail risk.
    Keywords: decomposition of tail dependence, multivariate extreme values, stable tail dependence function, subsample bootstrap, tail correlation
    JEL: C12 C19
    Date: 2014–02–25
  17. By: Feunou, Bruno (Bank of Canada); Jahan-Parvar, Mohammad (Board of Governors of the Federal Reserve System (U.S.)); Okou, Cedric (UQAM)
    Abstract: We propose a new decomposition of the variance risk premium in terms of upside and downside variance risk premia. The difference between upside and downside variance risk premia is a measure of skewness risk premium. We establish that the downside variance risk premium is the main component of the variance risk premium, and that the skewness risk premium is a priced factor with significant prediction power for aggregate excess returns. Our empirical investigation highlights the positive and significant link between the downside variance risk premium and the equity premium, as well as a positive and significant relation between the skewness risk premium and the equity premium. Finally, we document the fact that the skewness risk premium fills the time gap between one quarter ahead predictability, delivered by the variance risk premium as a short term predictor of excess returns and traditional long term predictors such as price-dividend or price-earning ratios. Our resul ts are supported by a simple equilibrium consumption-based asset pricing model.
    Keywords: Downside variance risk premium; realized volatility; risk-neutral volatility; skewness risk premium; upside variance risk premium
    JEL: G12
    Date: 2015–03–17
  18. By: André Lucas (VU University Amsterdam); Xin Zhang (Sveriges Riksbank, Sweden)
    Abstract: We present a simple new methodology to allow for time variation in volatilities using a recursive updating scheme similar to the familiar RiskMetrics approach. We update parameters using the score of the forecasting distribution rather than squared lagged observations. This allows the parameter dynamics to adapt automatically to any non-normal data features and robustifies the subsequent volatility estimates. Our new approach nests several extensions to the exponentially weighted moving average (EWMA) scheme as proposed earlier. Our approach also easily handles extensions to dynamic higher-order moments or other choices of the preferred forecasting distribution. We apply our method to Value-at-Risk forecasting with Student's t distributions and a time varying degrees of freedom parameter and show that the new method is competitive to or better than earlier methods for volatility forecasting of individual stock returns and exchange rates.
    Keywords: dynamic volatilities, time varying higher order moments, integrated generalized autoregressive score models, Exponential Weighted Moving Average (EWMA), Value-at-Risk (VaR)
    JEL: C51 C52 C53 G15
    Date: 2014–07–22
  19. By: Bernd Schwaab (European Central Bank, Financial Research, Germany); Siem Jan Koopman (VU University Amsterdam, the Netherlands); André Lucas (VU University Amsterdam, the Netherlands)
    Abstract: This paper investigates the dynamic properties of systematic default risk conditions for firms from different countries, industries, and rating groups. We use a high-dimensional nonlinear non-Gaussian state space model to estimate common components in corporate defaults in a 41 country sample between 1980Q1-2014Q4,covering both the global financial crisis and euro area sovereign debt crises. We find that macro and default-specific world factors are a primary source of default clustering across countries. Defaults cluster more than what is implied by shared exposures to macro factors, indicating that other factors are of high importance as well. For all firms, deviations of systematic default risk from macro fundamentals are correlated with net tightening bank lending standards, implying that bank credit supply and systematic default risk are inversely related.
    Keywords: systematic default risk, credit portfolio models, frailty-correlated defaults, international default risk cycles, state space methods
    JEL: G21 C33
    Date: 2015–02–26
  20. By: Masako Ikefuji (University of Southern Denmark, Denmark); Roger Laeven (University of Amsterdam, the Netherlands); Jan Magnus (VU University Amsterdam, the Netherlands); Chris Muris (Simon Fraser University, Canada)
    Abstract: An expected utility based cost-benefit analysis is in general fragile to its distributional assumptions. We derive necessary and sufficient conditions on the utility function of the expected utility model to avoid this. The conditions ensure that expected (marginal) utility remains finite also under heavy-tailed distributional assumptions. Our results are context-free and are relevant to many fields encountering catastrophic risk analysis, such as, perhaps most noticeably, insurance and risk management.
    Keywords: Expected utility, Catastrophe, Cost-benefit analysis, Risk management, Power utility, Exponential utility, Heavy tails
    JEL: D61 D81 G10 G20
    Date: 2014–10–14
  21. By: Runhuan Feng (University of Illinois at Urbana-Champaign, United States); Xiaochen Jing (University of Illinois at Urbana-Champaign, United States); Jan Dhaene (Katholieke Universiteit Leuven, Belgium)
    Abstract: The computation of various risk metrics is essential to the quantitative risk management of variable annuity guaranteed benets. The current market practice of Monte Carlo simulation often requires intensive computations, which can be very costly for insurance companies to implement and take so much time that they cannot obtain information and take actions in a timely manner. In an attempt to nd low-cost and ecient alternatives, we explore the techniques of comonotonic bounds to produce closed-form approximation of the risk measures for variable annuity guaranteed benets. The techniques are further developed in this paper to address in a systematic way risk measures for death benets with the consideration of dynamic policyholder behavior.
    Keywords: Variable annuity guaranteed benefit, risk measures, value at risk, conditional tail expectation, geometric Brownian motion, comonotonicity, dynamic policyholder behavior
    JEL: G19 C63
    Date: 2015–01–16
  22. By: Chia-Lin Chang (National Chung Hsing University, Taiwan); Michael McAleer (National Tsing Hua University, Taiwan, Erasmus University Rotterdam, the Netherlands, Complutense University of Madrid, Spain)
    Abstract: One of the fastest growing areas in empirical finance, and also one of the least rigorously analyzed, especially from a financial econometrics perspective, is the econometric analysis of financial derivatives, which are typically complicated and difficult to analyze. The purpose of this special issue of the journal on “Econometric Analysis of Financial Derivatives” is to highlight several areas of research by leading academics in which novel econometric, financial econometric, mathematical finance and empirical finance methods have contributed significantly to the econometric analysis of financial derivatives, including market-based estimation of stochastic volatility models, the fine structure of equity-index option dynamics, leverage and feedback effects in multifactor Wishart stochastic volatility for option pricing, option pricing with non-Gaussian scaling and infinite-state switching volatility, stock return and cash flow predictability: the role of volatility risk, the long and the short of the risk-return trade-off, What’s beneath the surface? option pricing with multifrequency latent states, bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets, a stochastic dominance approach to financial risk management strategies, empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction, non-linear dynamic model of the variance risk premium, pricing with finite dimensional dependence, quanto option pricing in the presence of fat tails and asymmetric dependence, smile from the past: a general option pricing framework with multiple volatility and leverage components, COMFORT: A common market factor non-Gaussian returns model, divided governments and futures prices, and model-based pricing for financial derivatives.
    Keywords: Stochastic volatility, switching volatility, volatility risk, option pricing dynamics, futures prices, fractional integration, stochastic dominance, variance risk premium, fat tails, leverage and asymmetry, divided governments
    JEL: C58 G23 G32
    Date: 2014–12–16
  23. By: Lukasz Gatarek (Erasmus University Rotterdam); Søren Johansen (University of Copenhagen, CREATES, Denmark)
    Abstract: We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the hedged asset and among themselves. We nd that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is dened as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the innite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average of correlation and cointegration parameters. The results are general and can be applied for any portfolio of assets that can be modeled by the CVAR of any rank and order.
    Keywords: hedging, cointegration, minimum variance portfolio
    JEL: C22 C58 G11
    Date: 2014–02–18
  24. By: Manabu Asai (Soka University, Japan); Massimiliano Caporin (University of Padova, Italy); Michael McAleer (Erasmus University Rotterdam, The Netherlands, Complutense University of Madrid, Spain, and Kyoto University, Japan)
    Abstract: Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose is to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets can be very large. We contribute to this strand of the literature by proposing a block-type parameterization for multivariate stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value-at-Risk thresholds based on one-step-ahead forecasts of covariances by the new specification are satisfactory for the period including the Global Financial Crisis.
    Keywords: block structures; multivariate stochastic volatility; curse of dimensionality; leverage effects; multi-factors; heavy-tailed distribution
    JEL: C32 C51 C10
    Date: 2013–05–27
  25. By: Ka Chun Cheung (The University of Hong Kong, Hong Kong, PR of China); Michel Denuit (Université Catholique de Louvain, Belgium); Jan Dhaene (Katholieke Universiteit Leuven, Belgium, University of the Free State, Bloemfontein, South Africa)
    Abstract: In this paper, we extend the concept of mutual exclusivity proposed by Dhaene and Denuit (1999) to its tail counterpart and baptise this new dependency structure as tail mutual exclusivity. Probability levels are first specified for each component of the random vector. Under this dependency structure, at most one exceedance over the corresponding VaRs is possible, the other components being zero in such a case. No condition is imposed when all components stay below the VaRs. Several properties of this new negative dependence concept are derived. We show that this dependence structure gives rise to the smallest value of Tail-VaR of a sum of risks within a given Fréchet space, provided that the probability level of the Tail-VaR is close enough to one.
    Keywords: Mutual exclusivity, stop-loss transform, tail convex order, risk measures
    JEL: G19 C63
    Date: 2015–02–12

This nep-rmg issue is ©2015 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.