nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒07‒18
eleven papers chosen by
Stan Miles
Thompson Rivers University

  1. Modelling Dependence in High Dimensions with Factor Copulas By Oh, Dong Hwan; Patton, Andrew J.
  2. Banks' Risk Exposures By Juliane Begenau; Monika Piazzesi; Martin Schneider
  3. Measuring spot variance spillovers when (co)variances are time-varying – the case of multivariate GARCH models By Fengler, Matthias R.; Herwartz, Helmut
  4. Switching to non-affine stochastic volatility: A closed-form expansion for the Inverse Gamma model By Nicolas Langren\'e; Geoffrey Lee; Zili Zhu
  5. Shape Regressions By Franco Peracchi; Samantha Leorato
  6. A conceptual foundation for the theory of risk aversion By Yonatan Aumann
  7. Corporate Fraction and the Equilibrium Term-Structure of Equity Risk By Roberto Marfè
  8. Rare Shocks vs. Non-linearities: What Drives Extreme Events in the Economy? Some Empirical Evidence By Michal Franta
  9. Commodity Price Crash: Risks to Exports and Economic Growth in Asia-Pacific LDCs and LLDCs By Aman Saggu; Witada Anukoonwattaka
  10. Bitcoin Price: Is it really that New Round of Volatility can be on way? By Bouoiyour, Jamal; Selmi, Refk
  11. On the Origins of Risk-Taking By Black, Sandra E.; Devereux, Paul J.; Lundborg, Petter; Majlesi, Kaveh

  1. 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 closed-form density, but we obtain analytical results for the implied tail dependence using extreme value theory, and we verify that simulation-based 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
  2. 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
  3. 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 second-order dynamics of the popular BEKK model. In an empirical application to a four-dimensional system of US asset classes - equity, fixed income, foreign exchange and commodities - we illustrate the second-order transmissions at various levels of (dis)aggregation. Moreover, we demonstrate that the proposed spillover indices are informative on the value-at-risk violations of portfolios composed of the considered asset classes.
    Keywords: Multivariate GARCH, spillover index, value-at-risk, variance spillovers, variance decomposition
    JEL: C32 C58 F3 G1
    Date: 2015–07
  4. By: Nicolas Langren\'e; Geoffrey Lee; Zili Zhu
    Abstract: This paper introduces the Inverse Gamma (IGa) stochastic volatility model with time-dependent parameters, defined by the volatility dynamics $dV_{t}=\kappa_{t}\left(\theta_{t}-V_{t}\right)dt+\lambda_{t}V_{t}dB_{t}$. This non-affine 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 closed-form volatility-of-volatility expansion. Specifically, the price of a European put option with IGa volatility is approximated by a Black-Scholes price plus a weighted combination of Black-Scholes greeks, where the weights depend only on the four time-dependent parameters of the model. This closed-form 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 real-world 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 transform-based affine models. We therefore hope that the present work will foster further research on non-affine 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
  5. 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 curse-of-dimensionality 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 fills-in 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 delta-method; non-separable models; influence function
    JEL: C1 C21 C25
    Date: 2015–07–10
  6. 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 neo-classical 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 risk-aversion. We then show that when cast in quantitative form these ordinal definitions coincide with the classical Arrow-Pratt 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
  7. 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 continuous-time long-run 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 short-run than at medium horizons but also preserves the role of long-run 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
  8. By: Michal Franta
    Abstract: A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and non-linearities 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 1984–2013. Extreme shocks are accounted for by assuming t-distributed reduced-form shocks. Non-linearity 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 non-linearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if non-linearities and shock distributions with fat tails are considered.
    Keywords: Bayesian VAR, density forecasting, fat tails, non-linearity
    JEL: C11 C32 E44
    Date: 2015–06
  9. 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 Asia-Pacific LDCs and LLDCs with export-portfolios and economies which are at greatest risk from the recent collapse in global commodity prices. Asia-Pacific LDCs and LLDCs account for less than 2% of global commodity exports and just 7% of Asia-Pacific commodity exports; however many these economies have export-portfolios 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
  10. 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 2010-June 2015] and [January 2015-June 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 (Exponential-GARCH) 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
  11. By: Black, Sandra E. (University of Texas at Austin); Devereux, Paul J. (University College Dublin); Lundborg, Petter (Lund University); Majlesi, Kaveh (Lund University)
    Abstract: Risk-taking 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 pre-birth and post-birth factors are important. However, once we condition on having positive financial wealth, we find that nurture has a much stronger influence on risk-taking by children, and the evidence of a relationship between stock-holding 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 risk-attitudes and behavior is environmentally determined.
    Keywords: intergenerational mobility, nature versus nurture, portfolio allocation
    JEL: G11 J01
    Date: 2015–07

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