nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒09‒30
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures By Denisa Banulescu; Christophe Hurlin; Jeremy Leymarie; O. Scaillet
  2. Relevance of loan characteristics in probability of default prediction for commercial mortgage loans By Nicole Lux
  3. A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies By Fantazzini, Dean; Zimin, Stephan
  4. The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades By Fantazzini, Dean; Shangina, Tamara
  5. Meta-model of a large credit risk portfolio in the Gaussian copula model By Florian Bourgey; Emmanuel Gobet; Clément Rey
  6. A Volatility Smile-Based Uncertainty Index By José Valentim Machado Vicente; Jaqueline Terra Moura Marins
  7. Affective Portfolio Analysis: Risk, Ambiguity and (IR)rationality By Donald J. Brown
  8. The Nexus between Loan Portfolio Size and Volatility: Does Banking Regulation Matter? By Franziska Bremus; Melina Ludolph
  9. Risk weighting, private lending and macroeconomic dynamics By Donadelli, Michael; Jüppner, Marcus; Prosperi, Lorenzo
  10. Loss Aversion and Search for Yield in Emerging Markets Sovereign Debt By Ricardo Sabbadini
  11. Corruption Risk in Contracting Markets: A Network Science Perspective By Johannes Wachs; Mih\'aly Fazekas; J\'anos Kert\'esz
  12. The value of knowing the market price of risk By Katia Colaneri; Stefano Herzel; Marco Nicolosi
  13. The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment By Xiao, Tim
  14. Implications of Default Recovery Rates for Aggregate Fluctuations By Giacomo Candian; Mikhail Dmitriev

  1. By: Denisa Banulescu (University of Orleans; Maastricht School of Business and Economics); Christophe Hurlin (University of Orleans); Jeremy Leymarie (University of Orleans); O. Scaillet (University of Geneva GSEM and GFRI; Swiss Finance Institute; University of Geneva - Research Center for Statistics)
    Abstract: This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as SES, SRISK, or ∆CoVaR. We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application is then carried out to check the validity of the MES, SRISK, and ∆CoVaR forecasts issued from a GARCH-DCC model for a panel of U.S. financial institutions. Our results show that this model is able to produce valid forecasts for the MES and SRISK when considering a medium-term horizon. Finally, we propose an original early warning system indicator for future systemic crises deduced from these backtests. We then define an adjusted systemic risk measure that takes into account the potential misspecification of the risk model.
    Date: 2019–09
  2. By: Nicole Lux
    Abstract: The current papers examines the sensitivity of loan characteristics on mortgage default probability for UK commercial mortgages. Commercial real estate (CRE) mortgages are major asset holdings for commercial banks, life insurance companies and thrift institutions. The slumping market for real estate threatened to drag down regional banks and other smaller financial institutions in the 08/09 financial crisis and led to the collapse of some financial institutions. Despite the prominence of CRE mortgages, modeling and analysing credit risk of CRE mortgages has been lagging behind those of non-CRE commercial loans. Modelling the probability of default for commercial real estate mortgages is more complicated than that for non-commercial real estate loans. Many distressed loans passed traditional underwriting standards suggesting that, in addition to LTV and DSCR ratios, other characteristics should be taken into consideration such as the inclusion property characteristics. The accuracy of default prediction is tested comparing two traditional statistical methods a) logistic regression (logit) and b) multiple discriminant analysis (MDA) using a unique dataset of defaulted commercial loan portfolios from 60 financial institutions lending in the UK between 2005 – 2017. Overall, both models show that the inclusion of property characteristics such as geography and asset type have been significant factors in determining default probability and improve model accuracy, while LTV shows no clear significance.
    Keywords: commercial mortgage risk; Credit risk modelling; linear discriminant analysis; Logistic Regression; Probability of default (PD)
    JEL: R3
    Date: 2019–01–01
  3. By: Fantazzini, Dean; Zimin, Stephan
    Abstract: This paper proposes a set of models which can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously to estimate also their credit risk using the Zero Price Probability (ZPP) model by Fantazzini et al (2008), which is a methodology to compute the probabilities of default using only market prices. For this purpose, both univariate and multivariate models with different specifications are employed. Two special cases of the ZPP with closed-form formulas in case of normally distributed errors are also developed using recent results from barrier option theory. A backtesting exercise using two datasets of 5 and 15 coins for market risk forecasting and a dataset of 42 coins for credit risk forecasting was performed. The Value-at-Risk and the Expected Shortfall for single coins and for an equally weighted portfolio were calculated and evaluated with several tests. The ZPP approach was used for the estimation of the probability of default/death of the single coins and compared to classical credit scoring models (logit and probit) and to a machine learning algorithm (Random Forest). Our results reveal the superiority of the t-copula/skewed-t GARCH model for market risk, and the ZPP-based models for credit risk.
    Keywords: cryptocurrencies; market risk; credit risk; ZPP
    JEL: C32 C5 C51 C53 C58 G12 G17 G32 G33
    Date: 2019
  4. By: Fantazzini, Dean; Shangina, Tamara
    Abstract: This paper focuses on the forecasting of market risk measures for the Russian RTS index future, and examines whether augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the estimated risk measures. We considered a time sample of daily data from 2006 till 2019, which includes several episodes of large-scale turbulence in the Russian future market. We found that the predictive power of several models did not increase if these two variables were added, but actually decreased. The worst results were obtained when these two variables were added jointly and during periods of high volatility, when parameters estimates became very unstable. Moreover, several models augmented with these variables did not reach numerical convergence. Our empirical evidence shows that, in the case of Russian future markets, T-GARCH models with implied volatility and student’s t errors are better choices if robust market risk measures are of concern.
    Keywords: Forecasting; Value-at-Risk; Realized Volatility; Google Trends; Implied Volatility; GARCH; ARFIMA; HAR; Realized-GARCH
    JEL: C22 C51 C53 G17 G32
    Date: 2019
  5. By: Florian Bourgey (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Clément Rey (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We design a meta-model for the loss distribution of a large credit portfolio in the Gaussian copula model. Using both the Wiener chaos expansion on the systemic economic factor and a Gaussian approximation on the associated truncated loss, we significantly reduce the computational time needed for sampling the loss and therefore estimating risk measures on the loss distribution. The accuracy of our method is confirmed by many numerical examples.
    Keywords: Monte Carlo simulation,portfolio credit risk,polynomial chaos expansion,meta-model
    Date: 2019–09–19
  6. By: José Valentim Machado Vicente; Jaqueline Terra Moura Marins
    Abstract: We propose a new uncertainty index based on the discrepancy of the smile of FX options. We show that our index spikes near turbulent periods, forecasts economic activity and its innovations hold a significant and negative equity premium. Unlike other uncertainty indexes, our index is supported by equilibrium models, which relate the difference of options prices across moneyness to uncertainty. Moreover, our index is based on investment decisions, can be easily and continuously updated and is comparable across countries.
    Date: 2019–09
  7. By: Donald J. Brown (Dept. of Economics, Yale University)
    Abstract: Ambiguous assets are characterized as assets where objective and subjective probabilities of tomorrow’s asset-returns are ill-deï¬ ned or may not exist, e.g., bitcoin, volatility indices or any IPO. Investors may choose to diversify their portfolios of ï¬ at money, stocks and bonds by investing in ambiguous assets, a fourth asset class, to hedge the uncertainties of future returns that are not risks. (IR)rational probabilities are computable alternative descriptions of the distribution of returns for ambiguous assets. (IR)rational probabilities can be used to deï¬ ne an investor’s (IR)rational expected utility function in the class of non-expected utilities. Investment advisors use revealed preference analysis to elicit the investor’s composite preferences for risk tolerance, ambiguity aversion and optimism. Investors rationalize (IR)rational expected utilities over portfolios of ï¬ at money, stocks, bonds and ambiguous assets by choosing their optimal portfolio investments with (IR)rational expected utilities. Subsequently, investors can hedge future losses of their optimal portfolios by purchasing minimum-cost portfolio insurance.
    Keywords: Behavioral Finance, Prospect Theory, Afriat Inequalities
    JEL: B31 C91 D9
    Date: 2019–09
  8. By: Franziska Bremus; Melina Ludolph
    Abstract: Since the global financial crisis and the related restructuring of banking systems, bank concentration is on the rise in many countries. Consequently, bank size and its role for macroeconomic volatility (or: stability) is the subject of intense debate. This paper analyzes the effects of financial regulations on the link between bank size, as measured by the volume of the loan portfolio, and volatility. Using bank-level data for 1999 to 2014, we estimate a power law that relates bank size to the volatility of loan growth. The effect of regulation on the power law coefficient indicates whether regulation weakens or strengthens the size-volatility nexus. Our analysis reveals that more stringent capital regulation and the introduction of bank levies weaken the size-volatility nexus; in countries with more stringent capital regulation or levies in place, large banks show, ceteris paribus, lower loan portfolio volatility. Moreover, we find weak evidence that diversification guidelines weaken the link between size and volatility.
    Keywords: Bank size, regulation, volatility, diversification, moral hazard, power law
    JEL: G21 G28 E32
    Date: 2019
  9. By: Donadelli, Michael; Jüppner, Marcus; Prosperi, Lorenzo
    Abstract: According to current regulation, European banks can apply zero risk weights to sovereign exposures in their balance sheet, irrespective of the assigned rating. We show that a zero risk weighting of sovereign bonds has implications by distorting banks' asset allocation decisions. Due to the lower regulatory cost of sovereign bonds, banks invest more in those bonds at the expense of lending to the real sector. To quantify the effect of this distortion, we build a standard RBC model featuring financial intermediation and a government sector calibrated to the euro area economy. Financial regulation is introduced via a penalty function that punishes banks if they deviate from the target capital ratio. We study the zero risk weight policy during normal times when there is no sovereign default risk and find that a policy introducing positive risk weights on government bonds has both long-run effects and stabilising properties with respect to the business cycle. This policy makes the steady state lending spread on loans to firms decline, stimulating investment and output. Also, it stabilises the lending spread, leading to a lower volatility of investment and output.
    Keywords: sovereign bonds,risk weighting,RBC,lending
    JEL: E44 E32 G21 G32
    Date: 2019
  10. By: Ricardo Sabbadini
    Abstract: Empirical evidence indicates that a decline in international risk-free interest rates decreases emerging markets (EM) sovereign spreads. A standard quantitative model of sovereign default, calibrated to match average levels of debt and spread, does not replicate this feature even if the risk aversion of lenders moves with international interest rates. In this paper, I show that a model with lenders that are loss-averse and have reference dependence, traits suggested by the behavioral finance literature, replicates the noticed stylized fact. In this framework, when international interest rates fall, EM sovereign spreads decline despite increases in debt and default risk. This happens because investors search for yield in risky EM bonds when the risk-free rate is lower than their return of reference. I find that larger spread reductions occur for i) riskier countries; ii) greater declines in the risk-free rate; and iii) higher degrees of loss aversion.
    Date: 2019–09
  11. By: Johannes Wachs; Mih\'aly Fazekas; J\'anos Kert\'esz
    Abstract: We use methods from network science to analyze corruption risk in a large administrative dataset of over 4 million public procurement contracts from European Union member states covering the years 2008-2016. By mapping procurement markets as bipartite networks of issuers and winners of contracts we can visualize and describe the distribution of corruption risk. We study the structure of these networks in each member state, identify their cores and find that highly centralized markets tend to have higher corruption risk. In all EU countries we analyze, corruption risk is significantly clustered. However, these risks are sometimes more prevalent in the core and sometimes in the periphery of the market, depending on the country. This suggests that the same level of corruption risk may have entirely different distributions. Our framework is both diagnostic and prescriptive: it roots out where corruption is likely to be prevalent in different markets and suggests that different anti-corruption policies are needed in different countries.
    Date: 2019–09
  12. By: Katia Colaneri; Stefano Herzel; Marco Nicolosi
    Abstract: This paper presents an optimal allocation problem in a financial market with one risk-free and one risky asset, when the market is driven by a stochastic market price of risk. We solve the problem in continuous time, for an investor with a Constant Relative Risk Aversion (CRRA) utility, under two scenarios: when the market price of risk is observable (the {\em full information case}), and when it is not (the {\em partial information case}). The corresponding market models are complete in the partial information case and incomplete in the other case, hence the two scenarios exhibit rather different features. We study how the access to more accurate information on the market price of risk affects the optimal strategies and we determine the maximal price that the investor would be willing to pay to get such information. In particular, we examine two cases of additional information, when an exact observation of the market price of risk is available either at time $0$ only (the {\em initial information case}), or during the whole investment period (the {\em dynamic information case}).
    Date: 2019–09
  13. By: Xiao, Tim
    Abstract: This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To correct a common mistake in the literature, we emphasize that the market value of a defaultable derivative is actually a risky value rather than a risk-free value. Credit value adjustment (CVA) is also elaborated. A practical framework is developed for pricing defaultable derivatives and calculating their CVAs at a portfolio level.
    Keywords: credit value adjustment (CVA),credit risk modeling,financial derivative valuation,collateralization,margin and netting
    JEL: E44 G21 G12 G24 G32 G33 G18 G28
    Date: 2019
  14. By: Giacomo Candian (HEC Montréal); Mikhail Dmitriev (Florida State University)
    Abstract: We document that default recovery rates in the United States are highly volatile and strongly pro-cyclical. These facts are hard to reconcile with the existing financial friction literature. Indeed, models with limited enforceability a la Kiyotaki and Moore (1997) do not have defaults and recovery rates, while agency costs models following Bernanke, Gertler, and Gilchrist (1999) underestimate the volatility of recovery rates by one order of magnitude. We extend the standard agency costs model allowing liquidation costs for creditors to depend on the tightness of the market for physical capital. Creditors do not have expertise in selling entrepreneurial assets, but when buyers are plentiful, this disadvantage is minimal. Instead when sellers are abundant, the disadvantage of being an outsider is higher. Following a negative shock, entrepreneurs sell capital and liquidation costs for creditors increase. Creditors cut lending and cause entrepreneurs to sell more capital. This liquidity channel works independently from standard balance sheet effects and amplifies the impact of financial shocks on output by up to 50 percent.
    Date: 2019

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