nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒07‒15
fifteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Elicitability and Identifiability of Systemic Risk Measures and other Set-Valued Functionals By Tobias Fissler; Jana Hlavinov\'a; Birgit Rudloff
  2. Modelling Extremal Dependence for Operational Risk by a Bipartite Graph By Oliver Kley; Claudia Klüppelberg; Sandra Paterlini
  3. Risk Management in Financial Institutions By Rampini, Adriano A.; Viswanathan, S.; Vuillemey, Guillaume
  4. A simple approach to dual representations of systemic risk measures By Maria Arduca; Pablo Koch-Medina; Cosimo Munari
  5. A Triptych Approach for Reverse Stress Testing of Complex Portfolios By Pascal Traccucci; Luc Dumontier; Guillaume Garchery; Benjamin Jacot
  6. Stress testing effects on portfolio similarities among large US Banks By Bräuning, Falk; Fillat, Jose
  7. Relative Bound and Asymptotic Comparison of Expectile with Respect to Expected Shortfall By Samuel Drapeau; Mekonnen Tadese
  8. Assessing the vulnerability to price spikes in agricultural commodity markets By Triantafyllou, Athanasios; Dotsis, George; Sarris, Alexandros
  9. Can Investors Benefit from Hedge Fund Strategies? Utility-Based, Out-of-Sample Evidence By Massimo Guidolin; Alexei Orlov
  10. Has Regulatory Capital Made Banks Safer? Skin in the Game vs Moral Hazard By Ernest Dautovic
  11. An improved approach for estimating large losses in insurance analytics and operational risk using the g-and-h distribution By Marco Bee; Julien Hambuckers; Luca Trapin
  12. In Search of Systematic Risk and the Idiosyncratic Volatility Puzzle in the Corporate Bond Market By Jennie Bai; Turan G. Bali; Quan Wen
  13. Realized Volatility Forecasting: Robustness to Measurement Errors By Fabrizio Cipollini; Giampiero M. Gallo; Edoardo Otranto
  14. Small-time and large-time smile behaviour for the Rough Heston model By Martin Forde; Stefan Gerhold; Benjamin Smith
  15. Dynamic time series clustering via volatility change-points By Nick Whiteley

  1. By: Tobias Fissler; Jana Hlavinov\'a; Birgit Rudloff
    Abstract: This paper is concerned with a two-fold objective. Firstly, we establish elicitability and identifiability results for systemic risk measures introduced in Feinstein, Rudloff and Weber (2017). Specifying the entire set of capital allocations adequate to render a financial system acceptable, these systemic risk measures are examples of set-valued functionals. A functional is elicitable (identifiable) if it is the unique minimiser (zero) of an expected scoring function (identification function). Elicitability and identifiability are essential for forecast ranking and validation, $M$- and $Z$-estimation, both possibly in a regression framework. To account for the set-valued nature of the systemic risk measures mentioned above, we secondly introduce a theoretical framework of elicitability and identifiability of set-valued functionals. It distinguishes between exhaustive forecasts, being set-valued and aiming at correctly specifying the entire functional, and selective forecasts, content with solely specifying a single point in the correct functional. Uncovering the structural relation between the two corresponding notions of elicitability and identifiability, we establish that a set-valued functional can be either selectively elicitable or exhaustively elicitable. Notably, selections of quantiles such as the lower quantile turn out not to be elicitable in general. Applying these structural results to systemic risk measures, we construct oriented selective identification functions, which induce a family of strictly consistent exhaustive elementary scoring functions. We discuss equivariance properties of these scores. We demonstrate their applicability in a simulation study considering comparative backtests of Diebold-Mariano type with a pointwise traffic-light illustration of Murphy diagrams.
    Date: 2019–07
  2. By: Oliver Kley; Claudia Klüppelberg; Sandra Paterlini
    Abstract: We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto tails of losses and the heterogeneous dependence structures between them. We then derive estimators for individual as well as aggregated tail risk, measured in terms of Value-at-Risk and Conditional-Tail-Expectation for very high confidence levels, and provide also an asymptotically full capital allocation method. Estimation methods for such tail risk measures and capital allocations are also proposed and tested on simulated data. Finally, by having access to real-world operational risk losses from the Italian banking system, we show that even with a small number of observations, the proposed estimation methods produce reliable estimates, and that quantifying dependence by means of the empirical network has a big impact on estimates at both individual and aggregate level, as well as for capital allocations.
    Keywords: Bipartite graph, Estimation, Expected Shortfall, Extremal dependence, Operational Risk, Quantile risk measure, Value-at-Risk
    Date: 2019
  3. By: Rampini, Adriano A.; Viswanathan, S.; Vuillemey, Guillaume
    Abstract: We study risk management in financial institutions using data on hedging of interest rate and foreign exchange risk. We find strong evidence that institutions with higher net worth hedge more, controlling for risk exposures, both across institutions and within institutions over time. For identification, we exploit net worth shocks resulting from loan losses due to drops in house prices. Institutions that sustain such shocks reduce hedging significantly relative to otherwise similar institutions. The reduction in hedging is differentially larger among institutions with high real estate exposure. The evidence is consistent with the theory that financial constraints impede both financing and hedging.
    Keywords: Derivatives; Financial constraints; financial institutions; Foreign exchange risk; interest rate risk; Risk management
    JEL: D92 E44 G21 G32
    Date: 2019–06
  4. By: Maria Arduca; Pablo Koch-Medina; Cosimo Munari
    Abstract: We describe a general approach to obtain dual representations for systemic risk measures of the "allocate first, then aggregate"-type, which have recently received significant attention in the literature. Our method is based on the possibility to express this type of multivariate risk measures as special cases of risk measures with multiple eligible assets. This allows us to apply standard Fenchel-Moreau techniques to tackle duality also for systemic risk measures. The same approach can be also successfully employed to obtain an elementary proof of the dual representation of "first aggregate, then allocate"-type systemic risk measures. As a final application, we apply our results to derive a simple proof of the dual representation of univariate utility-based risk measures.
    Date: 2019–06
  5. By: Pascal Traccucci; Luc Dumontier; Guillaume Garchery; Benjamin Jacot
    Abstract: The quest for diversification has led to an increasing number of complex funds with a high number of strategies and non-linear payoffs. The new generation of Alternative Risk Premia (ARP) funds are an example that has been very popular in recent years. For complex funds like these, a Reverse Stress Test (RST) is regarded by the industry and regulators as a better forward-looking risk measure than a Value-at-Risk (VaR). We present an Extended RST (ERST) triptych approach with three variables: level of plausibility, level of loss and scenario. In our approach, any two of these variables can be derived by providing the third as the input. We advocate and demonstrate that ERST is a powerful tool for both simple linear and complex portfolios and for both risk management as well as day-to-day portfolio management decisions. An updated new version of the Levenberg - Marquardt optimization algorithm is introduced to derive ERST in certain complex cases.
    Date: 2019–06
  6. By: Bräuning, Falk (Federal Reserve Bank of Boston); Fillat, Jose (Federal Reserve Bank of Boston)
    Abstract: We use an expansive regulatory loan-level dataset to analyze how the portfolios of the largest US banks have evolved since 2011. In particular, we analyze how the commercial and industrial and commercial real estate loan portfolios have changed in response to stress-testing requirements stipulated in the 2010 Dodd-Frank Act. We find that the largest US banks, which are subject to stress testing, have become more similar since the current form of the stress testing was implemented in 2011. We also find that banks with poor stress test results tend to adjust their portfolios in a way that makes them more similar to the portfolios of banks that performed well in the stress testing. In general, stress testing has resulted in more diversified bank portfolios in terms of sectoral and regional distributions. However, we also find that all the large US banks diversified in a similar way, creating a more concentrated systemic portfolio in the aggregate.
    Keywords: bank correlations; concentration; portfolio similarity; stress tests; systemic risk
    JEL: G21 G28
    Date: 2019–04–01
  7. By: Samuel Drapeau; Mekonnen Tadese
    Abstract: Expectile bears some interesting properties in comparison to the industry wide expected shortfall in terms of assessment of tail risk. We study the relationship between expectile and expected shortfall using duality results and the link to optimized certainty equivalent. Lower and upper bounds of expectile are derived in terms of expected shortfall as well as a characterization of expectile in terms of expected shortfall. Further, we study the asymptotic behavior of expectile with respect to expected shortfall as the risk level goes to $0$ in terms of extreme value distributions. Illustrating the formulation of expectile in terms of expected shortfall, we also provide explicit or semi-explicit expressions of expectile for some classical distributions.
    Date: 2019–06
  8. By: Triantafyllou, Athanasios; Dotsis, George; Sarris, Alexandros
    Abstract: We empirically examine the predictability of the conditions which are associated with a higher probability of a price spike in agricultural commodity markets. We find that the forward spread is the most significant indicator of probable price jumps in maize, wheat and soybeans futures markets, a result which is in line with the “Theory of Storage”. We additionally show that some option-implied variables add significant predictive power when added to the more standard information variable set. Overall, the estimated probabilities of large price increases from our probit models exhibit significant correlations with the historical sudden market upheavals in agricultural markets.
    Keywords: Agricultural price spikes, Tail Risk Measure, Extreme Value Theory, Risk neutral moments, Agricultural Commodities, Basis, Theory of Storage
    Date: 2019–07–02
  9. By: Massimo Guidolin; Alexei Orlov
    Abstract: We report systematic, out-of-sample evidence on the benefits to an already welldiversified investor that may derive from further diversification into various hedge fund strategies. We investigate dynamic strategic asset allocation decisions that take into account investors’ preferences as well as return predictability. Our results suggest that not all hedge fund strategies benefit a long-term investor who is already well diversified across stocks, government and corporate bonds, and REITs. Only strategies whose payoffs are highly nonlinear (e.g., fixed income relative value and convertible arbitrage), and therefore not easily replicable, constitute viable options. Most of the realized economic value fails to result from a mean-variance type of improvement but comes instead from an improvement in realized higher-moment properties of optimal portfolios. Medium to highly risk-averse investors benefit the most from this alternative asset class.
    Keywords: Strategic asset allocation, hedge fund strategies, predictive regressions, out-ofsample performance, certainty equivalent return.
    JEL: G11 G17 G12 C53
    Date: 2018
  10. By: Ernest Dautovic
    Abstract: The paper evaluates the impact of macroprudential capital regulation on bank capital, risk taking behaviour, and solvency. The identication relies on an exogenous policy change in bank-level capital requirements across systemically important banks in Europe. A one percentage point hike in capital requirements leads to anaverage CET1 capital level increase of 13 percent improving their loss absorption capacity.
    Keywords: capital requirements, risk-taking, moral hazard, macroprudential policy
    JEL: E51 G21 O52
    Date: 2019–06
  11. By: Marco Bee; Julien Hambuckers; Luca Trapin
    Abstract: In this paper, we study the estimation of parameters for g-and-h distributions. These distributions find applications in modeling highly skewed and fat-tailed data, like extreme losses in the banking and insurance sector. We first introduce two estimation methods: a numerical maximum likelihood technique, and an indirect inference approach with a bootstrap weighting scheme. In a realistic simulation study, we show that indirect inference is computationally more efficient and provides better estimates in case of extreme features of the data. Empirical illustrations on insurance and operational losses illustrate these findings.
    Keywords: Intractable likelihood, indirect inference, skewed distribution, tail modeling, bootstrap
    JEL: C15 C46 C51 G22
    Date: 2019
  12. By: Jennie Bai; Turan G. Bali; Quan Wen
    Abstract: We propose a comprehensive measure of systematic risk for corporate bonds as a nonlinear function of robust risk factors and find a significantly positive link between systematic risk and the time-series and cross-section of future bond returns. We also find a positive but insignificant relation between idiosyncratic risk and future bond returns, suggesting that institutional investors dominating the bond market hold well-diversified portfolios with a negligible exposure to bond-specific risk. The composite measure of systematic risk also predicts the distribution of future market returns, and the systematic risk factor earns a positive price of risk, consistent with Merton's (1973) ICAPM.
    JEL: C13 G10 G11 G12
    Date: 2019–06
  13. By: Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Giampiero M. Gallo (Corte dei Conti and NYU in Florence, Italy); Edoardo Otranto (Università di Messina, Italy)
    Abstract: In this paper, we reconsider the issue of measurement errors affecting the estimates of a dynamic model for the conditional expectation of realized variance arguing that heteroskedasticity of such errors may be adequately represented with a multiplicative error model. Empirically we show that the significance of quarticity/quadratic terms capturing attenuation bias is very important within an HAR model, but is greatly diminished within an AMEM, and more so when regime specific dynamics account for a faster mean reversion when volatility is high. Model Confidence Sets confirm such robustness both in– and out–of–sample.
    Keywords: Realized volatility, Forecasting, Measurement errors, HAR, AMEM, Markov switching, Volatility of volatility
    JEL: C22 C51 C53 C58
    Date: 2019–07
  14. By: Martin Forde; Stefan Gerhold; Benjamin Smith
    Abstract: We characterize the asymptotic small-time and large-time implied volatility smile for the rough Heston model introduced by El Euch, Jaisson and Rosenbaum. We show that the asymptotic short-maturity smile scales in qualitatively the same way as a general rough stochastic volatility model, and is characterized by the Fenchel-Legendre transform of the solution a Volterra integral equation (VIE). The solution of this VIE satisfies a space-time scaling property which simplifies its computation. We corroborate our results numerically with Monte Carlo simulations. We also compute a power series in the log-moneyness variable for the asymptotic implied volatility, which yields tractable expressions for the vol skew and convexity, thus being useful for calibration purposes. We also derive formal asymptotics for the small-time moderate deviations regime and a formal saddlepoint approximation for call options in the large deviations regime. This goes to higher order than previous works for rough models, and in particular captures the effect of the mean reversion term. In the large maturity case, the limiting asymptotic smile turns out to be the same as for the standard Heston model, for which there is a well known closed-form formula in terms of the SVI parametrization.
    Date: 2019–06
  15. By: Nick Whiteley
    Abstract: This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clustering is performed using a probability metric evaluated between posterior distributions of the most recent change-point associated with each series. This implies series are grouped together at a given time if there is evidence the most recent shifts in their respective volatilities were coincident or closely timed. The clustering method is dynamic, in that groupings may be updated in an online manner as data arrive. Numerical results are given analyzing daily returns of constituents of the S&P 500.
    Date: 2019–06

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