nep-rmg New Economics Papers
on Risk Management
Issue of 2014‒07‒28
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. Carrying the (Paper) Burden: A Portfolio View of Systemic Risk and Optimal Bank Size By JAAP W.B. BOS; MARTIEN LAMERS; VICTORIA PURICE
  2. The Collateral Risk of ETFs By Christophe Hurlin; Gregoire Iseli; Christophe Pérignon; Stanley Yeung
  3. A wavelet-based copula approach for modeling market risk in agricultural commodity markets By RIADH ALOUI; MOHAMED SAFOUANE BEN AISSA; DUC KHUONG NGUYEN
  4. Structured portfolio analysis under SharpeOmega ratio By Rania Hentati-KAFFEL; Jean-Luc Prigent
  5. Do competitive disadvantages really arise from „over complying“?: proposed Basel III Leverage and Supplementary Leverage Ratios re-visited By Ojo, Marianne
  6. The dynamics of the leverage cycle By Christoph Aymanns; J. Doyne Farmer
  7. Design of Financial Derivatives: Statistical Power does not Ensure Risk Management Power By Bell, Peter Newton
  8. A Multi-Asset Option Approximation for General Stochastic Processes By Juan Arismendi;
  9. Arbitrage-free prediction of the implied volatility smile By Petros Dellaportas; Aleksandar Mijatovi\'c
  10. Predicting and Capitalizing on Stock Market Bears in the U.S. By Bertrand Candelon; Jameel Ahmed; Stefan Straetmans
  11. Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction By Diep Duong; Norman Swanson
  12. Does oil price uncertainty transmit to the Thai stock market? By Jiranyakul, Komain

    Abstract: We examine the relationship between bank size and financial stability by viewing the supervisor of a banking system as an ‘investor’ holding a portfolio of banks. Based on this view, we investigate the role of large banks in determining the systemic risk in this portfolio. Our results, based on book data of U.S. banks and Bank Holding Companies, indicate that the largest banks are consistently overrepresented in the current portfolio compared with the minimum variance portfolio. Moreover, the risk level of the portfolio can be reduced by limiting concentration without sacrificing returns.
    Keywords: Systemic risk; Modern Portfolio Theory; U.S. banking
    Date: 2014–05
  2. By: Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Gregoire Iseli (Université de Genève - Université de Genève); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959); Stanley Yeung (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)
    Abstract: As most Exchange-Traded Funds (ETFs) engage in securities lending or are based on total return swaps, they expose their investors to counterparty risk. To mitigate the funds' exposure, their counterparties must pledge collateral. In this paper, we present a framework to study collateral risk and provide empirical estimates for the $40.9 billion collateral portfolios of 164 funds managed by a leading ETF issuer. Overall, our findings contradict the allegations made by international agencies about the high collateral risk of ETFs. Finally, we theoretically show how to construct an optimal collateral portfolio for an ETF.
    Keywords: Asset management; passive investment ;derivatives ; systemic risk
    Date: 2014–07–05
    Abstract: We consider the problem of accurate market risk modeling for agricultural commodity products over heterogeneous investment horizons using copulas and wavelet methods. Our results indicate that the degree and structure of the dependence of daily commodity returns on the three market risk factors (federal funds rate, USD/Euro exchange rate, and world stock market fluctuations) vary according to the time scale. Changes in the USD/EUR exchange rate and the stock market index are the dominant risks for agricultural commodity markets. Moreover, the tail dependence on the daily returns of the three market risk factors is also scale-dependent, and frequently asymmetric. Finally, there is evidence to suggest that the application of the wavelet-copula model improves the accuracy of VaR estimates, compared to traditional approaches.
    Keywords: Agricultural commodities, Extreme-value copula, Wavelet, VaR, CVaR
    JEL: Q14 C52 C58 G11 G17
    Date: 2014–07–15
  4. By: Rania Hentati-KAFFEL; Jean-Luc Prigent
    Abstract: This paper deals with performance measurement of financial struc- tured products. For this purpose, we introduce the SharpeOmega ratio, based on put as downside risk measure. This allows to take account of the asymmetry of the return probability distribution. We provide gen- eral results about the optimization of some standard structured portfolios with respect to the SharpeOmega ratio. We determine in particular the optimal combination of risk free, stock and calljput instruments with re- spect to this performance measure. We show that, contrary to Sharpe ratio maximization (Goetzmann et al., 2002), the payoff of the optimal structured portfolio is not necessarily increasing and concave. We also discuss about the interest of the asset management industry to reward high Sharpe Omega ratios.
    Date: 2014–07–15
  5. By: Ojo, Marianne
    Abstract: The Basel III Leverage Ratio, as originally agreed upon in December 2010, has recently undergone revisions and updates – both in relation to those proposed by the Basel Committee on Banking Supervision – as well as proposals introduced in the United States. Whilst recent proposals have been introduced by the Basel Committee to improve, particularly, the denominator component of the Leverage Ratio, new requirements have been introduced in the U.S to upgrade and increase these ratios, and it is those updates which relate to the Basel III Supplementary Leverage Ratio that have primarily generated a lot of interests. This is attributed not only to concerns that many subsidiaries of US Bank Holding Companies (BHCs) will find it cumbersome to meet such requirements, but also to potential or possible increases in regulatory capital arbitrage: a phenomenon which plagued the era of the original 1988 Basel Capital Accord and which also partially provided impetus for the introduction of Basel II. This paper is aimed at providing an analysis of the most recent updates which have taken place in respect of the Basel III Leverage Ratio and the Basel III Supplementary Leverage Ratio – both in respect of recent amendments introduced by the Basel Committee and revisions introduced in the United States. Amongst these notable developments, the Final or rather nearly finalised Standard issued by the Basel Committee in January 2014, as well as the 2014 U.S Enhanced Supplementary Leverage Ratios are worth mentioning. Sometimes the competitive disadvantages resulting from over compliance or stringent measures may generate costs which are actually minimal when compared to those costs which could potential arise in a scenario where economic disruptions and crises do occur where such „over compliance“ measures are not implemented. So when do measures become over-compliant? What may be regarded as over-compliance for a particular jurisdiction may not necessarily be the case for another. Conversely what may be required for minimal compliance purposes in certain jurisdictions may prove inadequate for certain major economies.
    Keywords: credit risk; global systemically important banks (G-SIBs); leverage ratios; harmonisation; accounting rules; capital arbitrage; disclosure; stress testing techniques; U.S Basel III Final Rule
    JEL: E3 E32 G2 G28 K2
    Date: 2014–07–21
  6. By: Christoph Aymanns; J. Doyne Farmer
    Abstract: We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high and vice versa, a phenomenon that has been dubbed pro-cyclical leverage. We show that this leads to endogenous irregular oscillations, in which gradual increases in stock prices and leverage are followed by drastic market collapses, i.e. a leverage cycle. This phenomenon is studied using simplified models that give a deeper understanding of the dynamics and the nature of the feedback loops and instabilities underlying the leverage cycle. We introduce a flexible leverage regulation policy in which it is possible to continuously tune from pro-cyclical to countercyclical leverage. When the policy is sufficiently countercyclical and bank risk is sufficiently low the endogenous oscillation disappears and prices go to a fixed point. While there is always a leverage ceiling above which the dynamics are unstable, countercyclical leverage can be used to raise the ceiling. Finally, we investigate fixed limits on leverage and show how they can control the leverage cycle.
    Date: 2014–07
  7. By: Bell, Peter Newton
    Abstract: This paper presents a modelling framework for analysis of financial derivatives. The framework analyzes the derivative from the perspective of a producer who has uncertain quantity of production. Quantity has a statistical relationship to an index number, or risk factor, and the producer can buy a derivative on the index number, which provides the producer with an indirect hedge against low quantity. A practical concern is how to create such an index number: one approach is to define the index as an estimated regression equation with maximal explanatory power across some set of possible equations. I use my framework to conduct a simulation experiment that shows picking an index with maximal explanatory power can lead to a financial derivative with suboptimal efficiency. In other words, I show that it is possible for one index to have lower statistical power than another but higher risk management power. This result is due to the fact that statistical power is measured over all values of quantity, whereas losses only occur for low quantity and it is sufficient (in some cases) for the index to have strong explanatory power for low values of quantity to serve as an effective risk management tool.
    Keywords: Production, uncertainty, financial derivative, index number, statistical power, risk management
    JEL: C43 D29 D81 G23 G32 M11
    Date: 2014–07–19
  8. By: Juan Arismendi (ICMA Centre, Henley Business School, University of Reading);
    Abstract: We derived a model-free analytical approximation of the price of a multi-asset option defined over an arbitrary multivariate process, applying a semi-parametric expansion of the unknown risk-neutral density with the moments. The analytical expansion termed as the Multivariate Generalised Edgeworth Expansion (MGEE) is an infinite series over the derivatives of the known continuous time density. The expected value of the density expansion is calculated to approximate the option price. The expansion could be used to enhance a Monte Carlo pricing methodology incorporating the information about moments of the risk-neutral distribution. The numerical efficiency of the approximation is tested over a jump diffusion density. For the known density, we tested the multivariate lognormal (MVLN), even though arbitrary densities could be used, and we provided its derivatives until the fourth-order. The MGEE relates two densities and isolates the effects of multivariate moments over the opt ion prices. Results show that a calibrated approximation provides a good fit when the difference between the moments of the risk-neutral density and the auxiliary density are small relative to the density function of the former, and the uncalibrated approximation has immediate implications over risk management and hedging theory. The possibility to select the auxiliary density provides an advantage over classical Gram-Charlier A, B and C series approximations. The density approximation and the methodology can be applied to other fields of finance like asset pricing, econometrics, and areas of statistical nature
    Keywords: Multi-asset option pricing, Derivatives, Risk Management
    Date: 2014–04
  9. By: Petros Dellaportas; Aleksandar Mijatovi\'c
    Abstract: This paper gives an arbitrage-free prediction for future prices of an arbitrary co-terminal set of options with a given maturity, based on the observed time series of these option prices. The statistical analysis of such a multi-dimensional time series of option prices corresponding to $n$ strikes (with $n$ large, e.g. $n\geq 40$) and the same maturity, is a difficult task due to the fact that option prices at any moment in time satisfy non-linear and non-explicit no-arbitrage restrictions. Hence any $n$-dimensional time series model also has to satisfy these implicit restrictions at each time step, a condition that is impossible to meet since the model innovations can take arbitrary values. We solve this problem for any $n\in\NN$ in the context of Foreign Exchange (FX) by first encoding the option prices at each time step in terms of the parameters of the corresponding risk-neutral measure and then performing the time series analysis in the parameter space. The option price predictions are obtained from the predicted risk-neutral measure by effectively integrating it against the corresponding option payoffs. The non-linear transformation between option prices and the risk-neutral parameters applied here is \textit{not} arbitrary: it is the standard mapping used by market makers in the FX option markets (the SABR parameterisation) and is given explicitly in closed form. Our method is not restricted to the FX asset class nor does it depend on the type of parameterisation used. Statistical analysis of FX market data illustrates that our arbitrage-free predictions outperform the naive random walk forecasts, suggesting a potential for building management strategies for portfolios of derivative products, akin to the ones widely used in the underlying equity and futures markets.
    Date: 2014–07
  10. By: Bertrand Candelon; Jameel Ahmed; Stefan Straetmans
    Abstract: his paper attempts to predict the bear conditions on the US stock market. To this aim we elaborate simple predictive regressions, static and dynamic binary choice (BCM) as well as Markov-switching models. The in- and out-of-sample prediction ability is evaluated and we compare the forecasting performance of various specifications across as well as within models. It turns out that various dynamic extensions of static versions of probit and logit models reveal additional predictive information for both in- and out-of-sample fit. We also find that binary models outperform the Markov-switching model. With respect to the macro-financial variables, terms spreads, inflation and money supply turn out to be useful predictors. The results lead to useful implications for investors practicing active portfolio and risk management and for policy makers as tools to get early warning signals.
    Keywords: Bear stock market, S&P 500 Index, Macro-financial variables, Dynamic Binary Response models, Markov-switching model, Bry-Boschan algorithm, Active Trading Strategies.
    JEL: C22 C25 C53 G11 G17
    Date: 2014–07–15
  11. By: Diep Duong (Rutgers University); Norman Swanson (Rutgers University)
    Abstract: Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen, Bollerslev and Diebold (2007), Corsi, Pirino and Reno (2008), Barndorff, Kinnebrock, and Shephard (2010), Patton and Shephard (2011), and the references cited therein. In this paper, we review the extant literature and then present new empirical evidence on the predictive content of realized measures of jump power variations (including upside and downside risk, jump asymmetry, and truncated jump variables), constructed using instantaneous returns, i.e., |r_{t}|^{q}, 0≤q≤6, in the spirit of Ding, Granger and Engle (1993) and Ding and Granger (1996). Our prediction experiments use high frequency price returns constructed using S&P500 futures data as well as stocks in the Dow 30; and our empirical implementation involves estimating linear and nonlinear heterogeneous autoregressive realized volatility (HAR-RV) type models. We find that past "large" jump power variations help less in the prediction of future realized volatility, than past "small" jump power variations. Additionally, we find evidence that past realized signed jump power variations, which have not previously been examined in this literature, are strongly correlated with future volatility, and that past downside jump variations matter in prediction. Finally, incorporation of downside and upside jump power variations does improve predictability, albeit to a limited extent.
    Keywords: realized volatility, jumps, jump power variations, forecasting, jump test
    JEL: C58 C53 C22
    Date: 2013–07–27
  12. By: Jiranyakul, Komain
    Abstract: This study investigates the impact of oil price volatility (uncertainty) on the Stock Exchange of Thailand. Monthly data from May 1987 to December 2013 are applied to the two-stage procedure. In the first step, a bivariate generalized autoregressive conditional heteroskedastic (GARCH) model is estimated to obtain the volatility series of stock market index and oil price. In the second step, the pairwise Granger causality tests are performed to .determine the direction of volatility transmission between oil to stock markets. It this found that movement in real oil price does not adversely affect real stock market return, but stock price volatility does affect real stock return. In addition, there exists a positive one-directional volatility transmission running from oil to stock market. It is also found that oil price movement and its uncertainty adversely affect two main sub-index returns. These important findings give some implications for risk management and policy measures.
    Keywords: Real stock price, real oil price, volatility transmission, emerging markets
    JEL: C22 G15 Q40
    Date: 2014–06

This nep-rmg issue is ©2014 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.