New Economics Papers
on Risk Management
Issue of 2013‒01‒26
twelve papers chosen by

  1. Dynamic Prudential Regulation By Afrasiab Mirza
  2. Has the Basel Accord Improved Risk Management During the Global Financial Crisis? By Michael McAleer; Juan-Ángel Jiménez-Martín; Teodosio Pérez Amaral
  3. The Foster-Hart Measure of Riskiness for General Gambles By Frank Riedel; Tobias Hellmann
  4. Measuring Model Risk By Thomas Breuer; Imre Csiszar
  5. Bayesian Non-Parametric Portfolio Decisions with Financial Time Series By Audrone Virbickaite; M. Concepci\'on Aus\'in; Pedro Galeano
  6. Recent Developments in Financial Economics and Econometrics: An Overview By Chia-Lin Chang; David Allen; Michael McAleer
  7. Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression By David E. Allen; Abhay K. Singh; Robert J. Powell; Michael McAleer; James Taylor; Lyn Thomas
  8. A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns By René Garcia; Daniel Mantilla-Garcia; Lionel Martellini
  9. Financial Dependence Analysis: Applications of Vine Copulae By David.E. Allen; Mohammad.A. Ashraf; Michael. McAleer; Robert.J. Powell; Abhay K. Singh
  10. Financial Frictions and the Credit Transmission Channel: Capital Requirements and Bank Capital By Lucyna Gornicka; Sweder van Wijnbergen
  11. The Implications of VaR and Short-Selling Restrictions on the Portfolio Manager Performance By Fulbert, Tchana Tchana; Georges, Tsafack
  12. The best estimation for high-dimensional Markowitz mean-variance optimization By Bai, Zhidong; Li, Hua; Wong, Wing-Keung

  1. By: Afrasiab Mirza
    Abstract: This paper investigates regulations for banks that covered by deposit insurance in a dynamic setting where bankruptcy entails social costs. Regulatory policy operates through rules governing the bank's capital structure and asset allocation that may be adjusted each period. Throughout, the regulator must take into account that the bank is better informed about the inherent risks of its assets (adverse selection) and may forgo unobservable and costly actions to improve asset quality (moral hazard). Under the optimal regulatory policy under banks face risk-adjusted capital requirements but also hard-caps on size and leverage. In addition, the optimal policy counteracts pro-cyclical bank behaviour through the use of capital buffers.
    Keywords: Capital Regulation, Deposit Insurance, Risk-shifting
    JEL: G2 G3 G21 G28 G32
    Date: 2012–12
  2. By: Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam.); Juan-Ángel Jiménez-Martín (Departamento de Economía Cuantitativa (Department of Quantitative Economics), Facultad de Ciencias Económicas y Empresariales (Faculty of Economics and Business), Universidad Complutense de Madrid); Teodosio Pérez Amaral (Departamento de Economía Cuantitativa (Department of Quantitative Economics), Facultad de Ciencias Económicas y Empresariales (Faculty of Economics and Business), Universidad Complutense de Madrid)
    Abstract: The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing from a variety of risk models, and discuss the selection of optimal risk models. A new approach to model selection for predicting VaR is proposed, consisting of combining alternative risk models, and we compare conservative and aggressive strategies for choosing between VaR models. We then examine how different risk management strategies performed during the 2008-09 global financial crisis. These issues are illustrated using Standard and Poor’s 500 Composite Index.
    Keywords: Value-at-Risk (VaR), daily capital charges, violation penalties, optimizing strategy, risk forecasts, aggressive or conservative risk management strategies, Basel Accord, global financial crisis.
    JEL: G32 G11 G17 C53 C22
    Date: 2012–10
  3. By: Frank Riedel (Bielefeld University); Tobias Hellmann (Bielefeld University)
    Abstract: Foster and Hart proposed an operational measure of riskiness for discrete random variables. We show that their dening equation has no solution for many common continuous distributions including many uniform distributions, e.g. We show how to extend consistently the denition of riskiness to continuous random variables. For many continuous random variables, the risk measure is equal to the worst case risk measure, i.e. the maximal possible loss incurred by that gamble. We also extend the Foster{Hart risk measure to dynamic environments for general distributions and probability spaces, and we show that the extended measure avoids bankruptcy in innitely repeated gambles.
    Date: 2013–01
  4. By: Thomas Breuer; Imre Csiszar
    Abstract: We propose to interpret distribution model risk as sensitivity of expected loss to changes in the risk factor distribution, and to measure the distribution model risk of a portfolio by the maximum expected loss over a set of plausible distributions defined in terms of some divergence from an estimated distribution. The divergence may be relative entropy, a Bregman distance, or an $f$-divergence. We give formulas for the calculation of distribution model risk and explicitly determine the worst case distribution from the set of plausible distributions. We also give formulas for the evaluation of divergence preferences describing ambiguity averse decision makers.
    Date: 2013–01
  5. By: Audrone Virbickaite; M. Concepci\'on Aus\'in; Pedro Galeano
    Abstract: A Bayesian non-parametric approach for efficient risk management is proposed. A dynamic model is considered where optimal portfolio weights and hedging ratios are adjusted at each period. The covariance matrix of the returns is described using an asymmetric MGARCH model. Restrictive parametric assumptions for the errors are avoided by relying on Bayesian non-parametric methods, which allow for a better evaluation of the uncertainty in financial decisions. Illustrative risk management problems using real data are solved. Significant differences in posterior distributions of the optimal weights and ratios are obtained arising from different assumptions for the errors in the time series model.
    Date: 2013–01
  6. By: Chia-Lin Chang (National Chung Hsing University, Taiwan); David Allen (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Spain; Kyoto University, Japan)
    Abstract: Research papers in empirical finance and financial econometrics are among the most widely cited, downloaded and viewed articles in the discipline of Finance. The special issue presents several papers by leading scholars in the field on “Recent Developments in Financial Economics and Econometrics”. The breadth of coverage is substantial, and includes original research and comprehensive review papers on theoretical, empirical and numerical topics in Financial Economics and Econometrics by leading researchers in finance, financial economics, financial econometrics and financial statistics. The purpose of this special issue on “Recent Developments in Financial Economics and Econometrics” is to highlight several novel and significant developments in financial economics and financial econometrics, specifically dynamic price integration in the global gold market, a conditional single index model with local covariates for detecting and evaluating active management, whether the Basel Accord has improved risk management during the global financial crisis, the role of banking regulation in an economy under credit risk and liquidity shock, separating information maximum likelihood estimation of the integrated volatility and covariance with micro-market noise, stress testing correlation matrices for risk management, whether bank relationship matters for corporate risk taking, with evidence from listed firms in Taiwan, pricing options on stocks denominated in different currencies, with theory and illustrations, EVT and tail-risk modelling, with evidence from market indices and volatility series, the economics of data using simple model free volatility in a high frequency world, arbitrage-free implied volatility surfaces for options on single stock futures, the non-uniform pricing effect of employee stock options using quantile regression, nonlinear dynamics and recurrence plots for detecting financial crisis, how news sentiment impacts asset volatility, with evidence from long memory and regime-switching approaches, quantitative evaluation of contingent capital and its applications, high quantiles estimation with Quasi-PORT and DPOT, with an application to value-at-risk for financial variables, evaluating inflation targeting based on the distribution of inflation and inflation volatility, the size effects of volatility spillovers for firm performance and exchange rates in tourism, forecasting volatility with the realized range in the presence of noise and non-trading, using CARRX models to study factors affecting the volatilities of Asian equity markets, deciphering the Libor and Euribor spreads during the subprime crisis, information transmission between sovereign debt CDS and other financial factors for Latin America, time-varying mixture GARCH models and asymmetric volatility, and diagnostic checking for non-stationary ARMA models with an application to financial data.
    Keywords: Dynamic price integration; local covariates; risk management; global financial crisis; credit risk; liquidity shock; micro-market noise; corporate risk taking; options; volatility; quantiles; news sentiment; contingent capital; value-at-risk (see paper)
    JEL: G11 G12 G13 G15 G18
    Date: 2013–01–21
  7. By: David E. Allen (Edith Cowan University, Australia); Abhay K. Singh (Edith Cowan University, Australia); Robert J. Powell (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Spain, and Kyoto University, Japan); James Taylor (University of Oxford, Oxford); Lyn Thomas (University of Southampton, Southampton)
    Abstract: The purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the volatility and market return as quantified by Ordinary Least Square (OLS) regression is not uniform across the distribution of the volatility-price return pairs using quantile regressions. We examine the bivariate relationship of six volatility-return pairs, viz. CBOE-VIX and S&P-500, FTSE-100 Volatility and FTSE-100, NASDAQ-100 Volatility (VXN) and NASDAQ, DAX Volatility (VDAX) and DAX-30, CAC Volatility (VCAC) and CAC-40 and STOXX Volatility (VSTOXX) and STOXX. The assumption of a normal distribution in the return series is not appropriate when the distribution is skewed and hence OLS does not capture the complete picture of the relationship. Quantile regression on the other hand can be set up with various loss functions, both parametric and non-parametric (linear case) and can be evaluated with skewed marginal based copulas (for the non linear case). Which is helpful in evaluating the non-normal and non-linear nature of the relationship between price and volatility. In the empirical analysis we compare the results from linear quantile regression (LQR) and copula based non linear quantile regression known as copula quantile regression (CQR). The discussion of the properties of the volatility series and empirical findings in this paper have significance for portfolio optimization, hedging strategies, trading strategies and risk management in general.
    Keywords: Return-Volatility relationship; quantile regression; copula; copula quantile regression; volatility index; tail dependence
    JEL: C14 C58 G11
    Date: 2013–01–18
  8. By: René Garcia; Daniel Mantilla-Garcia; Lionel Martellini
    Abstract: In this paper, we formally show that the cross-sectional variance of stock returns is a consistent and asymptotically efficient estimator for aggregate idiosyncratic volatility. This measure has two key advantages: it is model-free and observable at any frequency. Previous approaches have used monthly model based measures constructed from time series of daily returns. The newly proposed cross-sectional volatility measure is a strong predictor for future returns on the aggregate stock market at the daily frequency. Using the cross-section of size and book-to-market portfolios, we show that the portfolios’ exposures to the aggregate idiosyncratic volatility risk predict the cross-section of expected returns. <P>
    Keywords: Aggregate idiosyncratic volatility, cross-sectional dispersion, prediction of market returns,
    Date: 2013–01–01
  9. By: David.E. Allen (Edith Cowan University, Australia); Mohammad.A. Ashraf (Indian Institute of Technology, Kharagpur, India); Michael. McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Spain, and Kyoto University, Japan); Robert.J. Powell (Edith Cowan University, Australia); Abhay K. Singh (Edith Cowan University, Australia)
    Abstract: This paper features the application of a novel and recently developed method of statistical and mathematical analysis to the assessment of financial risk: namely Regular Vine copulas. Dependence modelling using copulas is a popular tool in financial applications, but is usually applied to pairs of securities. Vine copulas offer greater exibility and permit the modelling of complex dependency patterns using the rich variety of bivariate copulas which can be arranged and analysed in a tree structure to facilitate the analysis of multiple dependencies. We apply Regular Vine copula analysis to a sample of stocks comprising the Dow Jones Index to assess their interdependencies and to assess how their correlations change in different economic circumstances using three different sample periods: 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 there is evidence of greater reliance on the Student <I>t</I> copula in the copula choice within the tree structures for the GFC period, which is consistent with the existence of larger tails in the distributions of returns for this period. One of the attractions of this approach to risk modelling is the exibility in the choice of distributions used to model co-dependencies.
    Keywords: Regular Vine Copulas; Tree structures; Co-dependence modelling
    JEL: G11 C02
    Date: 2013–01–22
  10. By: Lucyna Gornicka (University of Amsterdam); Sweder van Wijnbergen (University of Amsterdam)
    Abstract: We investigate actual capital chosen by banks in presence of capital minimum requirements and ex-post penalties for violating them. The model yields excess capital that is always positive and increases during times of distress in the economy, which is in line with empirical evidence. Next, we show that in presence of ex-post violation penalties the introduction of the conservation buffer under Basel III will not contribute to lowering the pro-cyclicality of capital regulations. The countercyclical buffer proposed under Basel III is then even more desirable as it significantly attenuates fluctuations of actual capital also when the penalties are accounted for.
    Keywords: capital requirements; Basel regulatory framework; excess capital; countercyclical buffer; market discipline
    JEL: G21 G28 E32 E44
    Date: 2013–01–14
  11. By: Fulbert, Tchana Tchana; Georges, Tsafack
    Abstract: The ability of a portfolio manager to deliver higher returns with relatively low risk is a fundamental issue in finance. We analyze here the performance of a portfolio manager under two different types of constraints. For a manager with private information, we compare the effect of value at risk (VaR) and short-selling constraints on the relation between the expected portfolio return and the market return. We find that in more volatile market, the VaR restriction will have a stronger effect on the manager performance compared to the short-selling restriction effect. The VaR constraint also strongly affects a manager with good quality of information while the short-selling restriction moderately affects manager with any level of information quality. For the manager attitude toward the risk, a too aggressive manager will find his overall performance more affected by the VaR constraint. Therefore, financial institutions such as large investment banks and hedge-funds with a strong ability to obtain superior information could be more affected by a very strong VaR restriction than by a short-selling restriction.
    Keywords: Performance valuation; Asymmetric information; Financial regulation; VaR restriction; Short-Selling restriction
    JEL: G11 G28 G32
    Date: 2013–05–17
  12. By: Bai, Zhidong; Li, Hua; Wong, Wing-Keung
    Abstract: The traditional(plug-in) return for the Markowitz mean-variance (MV) optimization has been demonstrated to seriously overestimate the theoretical optimal return, especially when the dimension to sample size ratio $p/n$ is large. The newly developed bootstrap-corrected estimator corrects the overestimation, but it incurs the "under-prediction problem," it does not do well on the estimation of the corresponding allocation, and it has bigger risk. To circumvent these limitations and to improve the optimal return estimation further, this paper develops the theory of spectral-corrected estimation. We first establish a theorem to explain why the plug-in return greatly overestimates the theoretical optimal return. We prove that under some situations the plug-in return is $\sqrt{\gamma}\ $\ times bigger than the theoretical optimal return, while under other situations, the plug-in return is bigger than but may not be $\sqrt{\gamma}\ $\ times larger than its theoretic counterpart where $\gamma = \frac 1{1-y}$ with $y$ being the limit of the ratio $p/n$. Thereafter, we develop the spectral-corrected estimation for the Markowitz MV model which performs much better than both the plug-in estimation and the bootstrap-corrected estimation not only in terms of the return but also in terms of the allocation and the risk. We further develop properties for our proposed estimation and conduct a simulation to examine the performance of our proposed estimation. Our simulation shows that our proposed estimation not only overcomes the problem of "over-prediction," but also circumvents the "under-prediction," "allocation estimation," and "risk" problems. Our simulation also shows that our proposed spectral-corrected estimation is stable for different values of sample size $n$, dimension $p$, and their ratio $p/n$. In addition, we relax the normality assumption in our proposed estimation so that our proposed spectral-corrected estimators could be obtained when the returns of the assets being studied could follow any distribution under the condition of the existence of the fourth moments.
    Keywords: Markowitz mean-variance optimization; Optimal Return; Optimal Portfolio Allocation; Large Random Matrix; Bootstrap Method
    JEL: G11 C3
    Date: 2013–01–10

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.