
on Risk Management 
Issue of 2015‒07‒18
eleven papers chosen by 
By:  Oh, Dong Hwan (Board of Governors of the Federal Reserve System (U.S.)); Patton, Andrew J. (Duke University) 
Abstract:  his paper presents flexible new models for the dependence structure, or copula, of economic variables based on a latent factor structure. The proposed models are particularly attractive for relatively high dimensional applications, involving fifty or more variables, and can be combined with semiparametric marginal distributions to obtain flexible multivariate distributions. Factor copulas generally lack a closedform density, but we obtain analytical results for the implied tail dependence using extreme value theory, and we verify that simulationbased estimation using rank statistics is reliable even in high dimensions. We consider "scree" plots to aid the choice of the number of factors in the model. The model is applied to daily returns on all 100 constituents of the S&P 100 index, and we find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that factor copula models provide superior estimates of some measures of systemic risk. 
Keywords:  Copulas; correlation; dependence; systemic risk; tail dependence 
JEL:  C31 C32 C51 
Date:  2015–05–18 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:201551&r=rmg 
By:  Juliane Begenau; Monika Piazzesi; Martin Schneider 
Abstract:  This paper studies U.S. banks' exposure to interest rate and credit risk. We exploit the factor structure in interest rates to represent many bank positions in terms of simple factor portfolios. This approach delivers time varying measures of exposure that are comparable across banks as well as across the business segments of an individual bank. We also propose a strategy to estimate exposure due to interest rate derivatives from regulatory data on notional and fair values together with the history of interest rates. We use the approach to document stylized facts about the recent evolution of bank risk taking. 
JEL:  E4 E43 E58 G0 G2 G21 
Date:  2015–07 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:21334&r=rmg 
By:  Fengler, Matthias R.; Herwartz, Helmut 
Abstract:  In highly integrated markets, news spreads at a fast pace and bedevils risk monitoring and optimal asset allocation. We therefore propose global and disaggregated measures of variance transmission that allow one to assess spillovers locally in time. Key to our approach is the vector ARMA representation of the secondorder dynamics of the popular BEKK model. In an empirical application to a fourdimensional system of US asset classes  equity, fixed income, foreign exchange and commodities  we illustrate the secondorder transmissions at various levels of (dis)aggregation. Moreover, we demonstrate that the proposed spillover indices are informative on the valueatrisk violations of portfolios composed of the considered asset classes. 
Keywords:  Multivariate GARCH, spillover index, valueatrisk, variance spillovers, variance decomposition 
JEL:  C32 C58 F3 G1 
Date:  2015–07 
URL:  http://d.repec.org/n?u=RePEc:usg:econwp:2015:17&r=rmg 
By:  Nicolas Langren\'e; Geoffrey Lee; Zili Zhu 
Abstract:  This paper introduces the Inverse Gamma (IGa) stochastic volatility model with timedependent parameters, defined by the volatility dynamics $dV_{t}=\kappa_{t}\left(\theta_{t}V_{t}\right)dt+\lambda_{t}V_{t}dB_{t}$. This nonaffine model is much more realistic than classical affine models like the Heston stochastic volatility model, even though both are as parsimonious (only four stochastic parameters). Indeed, it provides more realistic volatility distribution and volatility paths, which translate in practice into more robust calibration and better hedging accuracy, explaining its popularity among practitioners. In order to price vanilla options with IGa volatility, we propose a closedform volatilityofvolatility expansion. Specifically, the price of a European put option with IGa volatility is approximated by a BlackScholes price plus a weighted combination of BlackScholes greeks, where the weights depend only on the four timedependent parameters of the model. This closedform pricing method allows for very fast pricing and calibration to market data. The overall quality of the approximation is very good, as shown by several calibration tests on realworld market data where expansion prices are compared favorably with Monte Carlo simulation results. This paper shows that the IGa model is as simple, more realistic, easier to implement and faster to calibrate than classical transformbased affine models. We therefore hope that the present work will foster further research on nonaffine models like the Inverse Gamma stochastic volatility model, all the more so as this robust model is of great interest to the industry. 
Date:  2015–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1507.02847&r=rmg 
By:  Franco Peracchi (Department of Economics, Georgetown University); Samantha Leorato (Department of Economics and Finance, Tor Vergata University) 
Abstract:  Learning about the shape of a probability distribution, not just about its location or dispersion, is often an important goal of empirical analysis. Given a continuous random variable Y and a random vector X defined on the same probability space, the conditional distribution function (CDF) and the conditional quantile function (CQF) offer two equivalent ways of describing the shape of the conditional distribution of Y given X. To these equivalent representations correspond two alternative approaches to shape regression. One approach  distribution regression  is based on direct estimation of the conditional distribution function (CDF); the other approach  quantile regression  is instead based on direct estimation of the conditional quantile function (CQF). Since the CDF and the CQF are generalized inverses of each other, indirect estimates of the CQF and the CDF may be obtained by taking the generalized inverse of the direct estimates obtained from either approach, possibly after rearranging to guarantee monotonicity of estimated CDFs and CQFs. The equivalence between the two approaches holds for standard nonparametric estimators in the unconditional case. In the conditional case, when modeling assumptions are introduced to avoid curseofdimensionality problems, this equivalence is generally lost as a convenient parametric model for the CDF need not imply a convenient parametric model for the CQF, and vice versa. Despite the vast literature on the quantile regression approach, and the recent attention to the distribution regression approach, no systematic comparison of the two has been carried out yet. Our paper fillsin this gap by comparing the asymptotic properties of estimators obtained from the two approaches, both when the assumed parametric models on which they are based are correctly specified and when they are not. 
Keywords:  Distribution regression; quantile regression; functional deltamethod; nonseparable models; influence function 
JEL:  C1 C21 C25 
Date:  2015–07–10 
URL:  http://d.repec.org/n?u=RePEc:geo:guwopa:gueconwpa~151506&r=rmg 
By:  Yonatan Aumann 
Abstract:  Classically, risk aversion is equated with concavity of the utility function. In this work we explore the conceptual foundations of this definition. In accordance with neoclassical economics, we seek an ordinal definition, based on the decisions maker’s preference order, independent of numerical values. We present two such definitions, based on simple, conceptually appealing interpretations of the notion of riskaversion. We then show that when cast in quantitative form these ordinal definitions coincide with the classical ArrowPratt definition (once the latter is defined with respect to the appropriate units), thus providing a conceptual foundation for the classical definition. The implications of the theory are discussed, including, in particular, to the understanding of insurance. The entire study is within the expected utility framework. 
Keywords:  Risk aversion, Utility theory, Ordinal preferences, Multiple objectives decision making 
Date:  2015–06 
URL:  http://d.repec.org/n?u=RePEc:huj:dispap:dp686&r=rmg 
By:  Roberto Marfè 
Abstract:  The recent empirical evidence of a downward sloping term structure of equity risk is viewed as a challenge to many leading asset pricing models. This paper analytically characterizes conditions under which a continuoustime longrun risk model can accommodate the stylized facts about dividend and equity risk, when dividends are a stationary stochastic fraction of aggregate consumption. Such a cointegrating relation makes dividends riskier in the shortrun than at medium horizons but also preserves the role of longrun risk: consequently, the model captures both the traditional puzzles, like the high equity premium, as well as the new evidence about the term structure of equity risk. 
JEL:  C62 D51 D53 G12 G13 
Date:  2015 
URL:  http://d.repec.org/n?u=RePEc:cca:wpaper:409&r=rmg 
By:  Michal Franta 
Abstract:  A smallscale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and nonlinearities during stress events observed in the economy. The model focuses on the link between credit/financial markets and the real economy and is estimated on US quarterly data for the period 1984â€“2013. Extreme shocks are accounted for by assuming tdistributed reducedform shocks. Nonlinearity is allowed by the possibility of regime switch in the shock propagation mechanism. Strong evidence for fat tails in error distributions is found. Moreover, the results suggest that accounting for extreme shocks rather than explicit modeling of nonlinearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if nonlinearities and shock distributions with fat tails are considered. 
Keywords:  Bayesian VAR, density forecasting, fat tails, nonlinearity 
JEL:  C11 C32 E44 
Date:  2015–06 
URL:  http://d.repec.org/n?u=RePEc:cnb:wpaper:2015/04&r=rmg 
By:  Aman Saggu (United Nations Economic and Social Commission for Asia and the Pacific (ESCAP)); Witada Anukoonwattaka (United Nations Economic and Social Commission for Asia and the Pacific (ESCAP)) 
Abstract:  This issue of the Trade Insights series identifies AsiaPacific LDCs and LLDCs with exportportfolios and economies which are at greatest risk from the recent collapse in global commodity prices. AsiaPacific LDCs and LLDCs account for less than 2% of global commodity exports and just 7% of AsiaPacific commodity exports; however many these economies have exportportfolios which are highly concentrated in one or two major commodities: mainly crude oil, natural, gas, aluminum, iron ore/steel, cotton and copper. 
Keywords:  Commodity price, revenue, export, economies 
JEL:  F1 
Date:  2015–03 
URL:  http://d.repec.org/n?u=RePEc:unt:esctis:tis6&r=rmg 
By:  Bouoiyour, Jamal; Selmi, Refk 
Abstract:  To the mass public, Bitcoin is well known since its creation by its extreme volatility. However, Bitcoin’s declining fluctuations since the start 2015 has revived our attention to assess whether there is a coming Bitcoin market phase. Using an optimal GARCH model on daily data, we show that the volatility of Bitcoin price decreases notably when comparing the periods [December 2010June 2015] and [January 2015June 2015]. During the first interval, the Threshold GARCH estimates reveal that there is a great duration of persistence and thus tends to follow a long memory process. For the second period, the chosen specification (ExponentialGARCH) displays less volatility persistence. Despite this remarkable volatility’s decrease, we cannot argue that Bitcoin market is mature, since the degree of asymmetry remains strong; Specifically, Bitcoin is likely to be driven by negative rather than positive shocks. 
Keywords:  Bitcoin; volatility; optimal GARCH model. 
JEL:  E3 E30 F3 
Date:  2015–07–13 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:65580&r=rmg 
By:  Black, Sandra E. (University of Texas at Austin); Devereux, Paul J. (University College Dublin); Lundborg, Petter (Lund University); Majlesi, Kaveh (Lund University) 
Abstract:  Risktaking behavior is highly correlated between parents and their children; however, little is known about the extent to which these relationships are genetic or determined by environmental factors. We use data on stock market participation of Swedish adoptees and relate this to the investment behavior of both their biological and adoptive parents. We find that stock market participation of parents increases that of children by about 34% and that both prebirth and postbirth factors are important. However, once we condition on having positive financial wealth, we find that nurture has a much stronger influence on risktaking by children, and the evidence of a relationship between stockholding of biological parents and their adoptive children becomes very weak. We find similar results when we study the share of financial wealth that is invested in stocks. This suggests that a substantial proportion of riskattitudes and behavior is environmentally determined. 
Keywords:  intergenerational mobility, nature versus nurture, portfolio allocation 
JEL:  G11 J01 
Date:  2015–07 
URL:  http://d.repec.org/n?u=RePEc:iza:izadps:dp9178&r=rmg 