Risk Management
http://lists.repec.orgmailman/listinfo/nep-rmg
Risk Management
2017-02-19
Estimation of Risk Contributions with MCMC
http://d.repec.org/n?u=RePEc:arx:papers:1702.03098&r=rmg
Determining risk contributions by unit exposures to portfolio-wide economic capital is an important task in financial risk management. Despite its practical demands, computation of risk contributions is challenging for most risk models because it often requires rare-event simulation. In this paper, we address the problem of estimating risk contributions when the total risk is measured by Value-at-Risk (VaR). We propose a new estimator of VaR contributions, that utilizes Markov chain Monte Carlo (MCMC) method. Unlike the existing estimators, our MCMC-based estimator is computed by samples of conditional loss distribution given the rare event of our interest. MCMC method enables to generate such samples without evaluating the density of total risk. Thanks to these features, our estimator has improved sample-efficiency compared with the crude Monte Carlo method. Moreover, our method is widely applicable to various risk models specified by joint portfolio loss density. In this paper, we show that our MCMC-based estimator has several attractive properties, such as consistency and asymptotic normality. Our numerical experiment also demonstrates that, in various risk models used in practice, our MCMC estimator has smaller bias and MSE compared with these of existing estimators.
Takaaki Koike
Mihoko Minami
2017-02
Estimating VaR in credit risk: Aggregate vs single loss distribution
http://d.repec.org/n?u=RePEc:arx:papers:1702.04388&r=rmg
Using Monte Carlo simulation to calculate the Value at Risk (VaR) as a possible risk measure requires adequate techniques. One of these techniques is the application of a compound distribution for the aggregates in a portfolio. In this paper, we consider the aggregated loss of Gamma distributed severities and estimate the VaR by introducing a new approach to calculate the quantile function of the Gamma distribution at high confidence levels. We then compare the VaR obtained from the aggregation process with the VaR obtained from a single loss distribution where the severities are drawn first from an exponential and then from a truncated exponential distribution. We observe that the truncated exponential distribution as a model for the severities yields results closer to those obtained from the aggregation process. The deviations depend strongly on the number of obligors in the portfolio, but also on the amount of gross loss which truncates the exponential distribution.
M. Assadsolimani
D. Chetalova
2017-02
Essays on tail risk in macroeconomics and finance: measurement and forecasting
http://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/242122&r=rmg
This thesis is composed of three chapters that propose some novel approaches on tail risk for financial market and forecasting in finance and macroeconomics. The first part of this dissertation focuses on financial market correlations and introduces a simple measure of tail correlation, TailCoR, while the second contribution addresses the issue of identification of non- normal structural shocks in Vector Autoregression which is common on finance. The third part belongs to the vast literature on predictions of economic growth; the problem is tackled using a Bayesian Dynamic Factor model to predict Norwegian GDP.Chapter I: TailCoRThe first chapter introduces a simple measure of tail correlation, TailCoR, which disentangles linear and non linear correlation. The aim is to capture all features of financial market co- movement when extreme events (i.e. financial crises) occur. Indeed, tail correlations may arise because asset prices are either linearly correlated (i.e. the Pearson correlations are different from zero) or non-linearly correlated, meaning that asset prices are dependent at the tail of the distribution.Since it is based on quantiles, TailCoR has three main advantages: i) it is not based on asymptotic arguments, ii) it is very general as it applies with no specific distributional assumption, and iii) it is simple to use. We show that TailCoR also disentangles easily between linear and non-linear correlations. The measure has been successfully tested on simulated data. Several extensions, useful for practitioners, are presented like downside and upside tail correlations.In our empirical analysis, we apply this measure to eight major US banks for the period 2003-2012. For comparison purposes, we compute the upper and lower exceedance correlations and the parametric and non-parametric tail dependence coefficients. On the overall sample, results show that both the linear and non-linear contributions are relevant. The results suggest that co-movement increases during the financial crisis because of both the linear and non- linear correlations. Furthermore, the increase of TailCoR at the end of 2012 is mostly driven by the non-linearity, reflecting the risks of tail events and their spillovers associated with the European sovereign debt crisis. Chapter II: On the identification of non-normal shocks in structural VARThe second chapter deals with the structural interpretation of the VAR using the statistical properties of the innovation terms. In general, financial markets are characterized by non- normal shocks. Under non-Gaussianity, we introduce a methodology based on the reduction of tail dependency to identify the non-normal structural shocks.Borrowing from statistics, the methodology can be summarized in two main steps: i) decor- relate the estimated residuals and ii) the uncorrelated residuals are rotated in order to get a vector of independent shocks using a tail dependency matrix. We do not label the shocks a priori, but post-estimate on the basis of economic judgement.Furthermore, we show how our approach allows to identify all the shocks using a Monte Carlo study. In some cases, the method can turn out to be more significant when the amount of tail events are relevant. Therefore, the frequency of the series and the degree of non-normality are relevant to achieve accurate identification.Finally, we apply our method to two different VAR, all estimated on US data: i) a monthly trivariate model which studies the effects of oil market shocks, and finally ii) a VAR that focuses on the interaction between monetary policy and the stock market. In the first case, we validate the results obtained in the economic literature. In the second case, we cannot confirm the validity of an identification scheme based on combination of short and long run restrictions which is used in part of the empirical literature.Chapter III :Nowcasting NorwayThe third chapter consists in predictions of Norwegian Mainland GDP. Policy institutions have to decide to set their policies without knowledge of the current economic conditions. We estimate a Bayesian dynamic factor model (BDFM) on a panel of macroeconomic variables (all followed by market operators) from 1990 until 2011.First, the BDFM is an extension to the Bayesian framework of the dynamic factor model (DFM). The difference is that, compared with a DFM, there is more dynamics in the BDFM introduced in order to accommodate the dynamic heterogeneity of different variables. How- ever, in order to introduce more dynamics, the BDFM requires to estimate a large number of parameters, which can easily lead to volatile predictions due to estimation uncertainty. This is why the model is estimated with Bayesian methods, which, by shrinking the factor model toward a simple naive prior model, are able to limit estimation uncertainty.The second aspect is the use of a small dataset. A common feature of the literature on DFM is the use of large datasets. However, there is a literature that has shown how, for the purpose of forecasting, DFMs can be estimated on a small number of appropriately selected variables.Finally, through a pseudo real-time exercise, we show that the BDFM performs well both in terms of point forecast, and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg Survey, and that it outperforms the predictions published by the Norges Bank in its monetary policy report.
Lorenzo Ricci
Tail correlation, tail risk, quantile, ellipticity, crises. JEL classification: C32, C51, G01.; Identification, Independent Component Analysis, Impulse Response Function, Vector Autoregression.; Real-Time Forecasting, Bayesian Factor model, Nowcasting. JEL classification: C32, C53, E37.
2017-02-13
Invariance properties in the dynamic gaussian copula model *
http://d.repec.org/n?u=RePEc:arx:papers:1702.03232&r=rmg
We prove that the default times (or any of their minima) in the dynamic Gaussian copula model of Cr{\'e}pey, Jeanblanc, and Wu (2013) are invariance times in the sense of Cr{\'e}pey and Song (2017), with related invariance probability measures different from the pricing measure. This reflects a departure from the immersion property, whereby the default intensities of the surviving names and therefore the value of credit protection spike at default times. These properties are in line with the wrong-way risk feature of counterparty risk embedded in credit derivatives, i.e. the adverse dependence between the default risk of a counterparty and an underlying credit derivative exposure.
St\'ephane Cr\'epey
Shiqi Song
2017-02
The impact of network connectivity on factor exposures, asset pricing and portfolio diversification
http://d.repec.org/n?u=RePEc:zbw:safewp:166&r=rmg
This paper extends the classic factor-based asset pricing model by including network linkages in linear factor models. We assume that the network linkages are exogenously provided. This extension of the model allows a better understanding of the causes of systematic risk and shows that (i) network exposures act as an inflating factor for systematic exposure to common factors and (ii) the power of diversification is reduced by the presence of network connections. Moreover, we show that in the presence of network links a misspecified traditional linear factor model presents residuals that are correlated and heteroskedastic. We support our claims with an extensive simulation experiment.
Billio, Monica
Caporin, Massimiliano
Panzica, Roberto Calogero
Pelizzon, Loriana
CAPM,volatility,network,interconnections,systematic risk
2017
Optimal capital growth with convex shortfall penalties
http://d.repec.org/n?u=RePEc:ehl:lserod:65486&r=rmg
The optimal capital growth strategy or Kelly strategy, has many desirable properties such as maximizing the asympotic long run growth of capital. However, it has considerable short run risk since the utility is logarithmic, with essentially zero Arrow-Pratt risk aversion. It is common to control risk with a Value-at-Risk constraint defined on the end of horizon wealth. A more effective approach is to impose a VaR constraint at each time on the wealth path. In this paper we provide a method to obtain the maximum growth while staying above an ex-ante discrete time wealth path with high probability, where shortfalls below the path are penalized with a convex function of the shortfall. The effect of the path VaR condition and shortfall penalties is less growth than the Kelly strategy, but the downside risk is under control. The asset price dynamics are defined by a model with Markov transitions between several market regimes and geometric Brownian motion for prices within regime. The stochastic investment model is reformulated as a deterministic program which allows the calculation of the optimal constrained growth wagers at discrete points in time.
Leonard C. MacLean
Yonggan Zhao
William T. Ziemba
portfolio selection; capital growth; regime switching; convex penalty; value at risk
2016-01
Estimating spatial basis risk in rainfall index insurance: Methodology and application to excess rainfall insurance in Uruguay:
http://d.repec.org/n?u=RePEc:fpr:ifprid:1595&r=rmg
This paper develops a novel methodology to estimate the degree of spatial basis risk for an arbitrary rainfall index insurance instrument. It relies on a widelyused stochastic rainfall generator, extendedto accommodate nontraditional dependence patternsâ€”in particular spatial upper-tail dependence in rainfallâ€”through a copula function. The methodology is applied to a recentlylaunched index product insuring against excess rainfall in Uruguay. The model is first calibrated using historical daily rainfall data from the national network of weather stations, complemented with a unique,high-resolution dataset from a dense network of 34 automatic weather stations around the study area. The degree of downside spatial basis risk is then estimated by Monte Carlo simulations and the results are linked to both a theoretical model of the demand for index insurance and to farmersâ€™ perceptions about the product.
Ceballos, Francisco
rain, rainfall patterns, insurance, weather, precipitation, risk management,
2016
Heterogeneous Market Structure and Systemic Risk: Evidence from Dual Banking Systems
http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0134&r=rmg
This paper investigates how banking system stability is affected when we combine Islamic and conventional finance under the same roof. We compare systemic resilience of three types of banks in 6 GCC countries with dual banking systems: fully-fledged Islamic banks (IB), purely conventional banks (CB) and conventional banks with Islamic windows (CBw). We employ market-based systemic risk measures such as MES, SRISK and DeltaCoVaR to identify which sector is more vulnerable to a systemic event, and use graphical network models to determine the banking sector that can more easily spread a systemic shock to the whole system. Using a sample of 2,608 observations on 79 publicly traded banks operating over the 2005-2014 period, we find that CBw is the least resilient sector to a systemic event and is more interconnected with other banks during crisis times.
Pejman Abedifar
Paolo Giudici
Shatha Hashem
graphical network models, Islamic banking, partial correlations, systemic risk measures
2017-02
What Broker Charges Reveal about Mortgage Credit Risk
http://d.repec.org/n?u=RePEc:hhs:rbnkwp:0336&r=rmg
Prior to the subprime crisis, mortgage brokers charged higher percentage fees for loans that turned out to be riskier ex post, even when conditioning on other risk characteristics. High conditional fees reveal borrower attributes that are associated with high borrower risk, such as suboptimal shopping behavior, high valuation for the loan or high borrower-specific broker costs. Borrowers who pay high conditional fees are inherently more risky, not just because they pay high fees. We find a stronger association between conditional fees and delinquency risk when lenders have fewer incentives to screen bor- rowers, for purchase rather than refinance loans, and for loans originated by brokers who have less frequent interactions with the lender. Our findings shed light on the pro- posed QRM exemption criteria for risk retention requirements for residential mortgage securitizations.
Berndt, Antje
Hollifield, Burton
Sandås, Patrik
Mortgage brokers; Loan performance; Subprime crisis; Credit risk retention; Qualied residential mortgages
2017-02-01
Risk-sharing benefits and the capital structure of insurance companies
http://d.repec.org/n?u=RePEc:ete:afiper:571404&r=rmg
Providing risk-sharing benefits to risk-averse policy holders is a primary function of insurance companies. We model that policy holders are paying a fee over the present value of indemnifications (i.e., technical provisions) to enjoy these risksharing benefits. This fee implies that a capital structure largely consisting of technical provisions is optimal for insurance firms, making the traditional Modigliani-Miller logic inappropriate for them. To support the issuance of technical provisions with socially desirable properties, insurance firms hold a surplus to absorb losses. We show that the Modigliani-Miller logic applies to the composition of this loss-absorption capacity. This explains why insurance companies may use, next to equity and technical provisions, financial debt in supporting their activities.
Cynthia Van Hulle
Hans Degryse
Kristien Smedts
2017
Agricultural Risk Management and Land Tenure
http://d.repec.org/n?u=RePEc:zbw:vfsc16:145792&r=rmg
Farmers under a sharecropping contract have been shown to exert less effort than farmers renting land due to lower incentives. They do not only choose their effort level, however, but also make investment decisions between projects of different risk-return profiles. We develop a small theoretical model that integrates the effort effect of sharecropping as well as the risk-reducing aspect of sharecropping which allows analyzing the implications for production, risk-management and risk-coping. In the empirical analysis, we combine a household survey taken in eleven African countries with data on climate risk to test the theoretical predictions. We find that sharecropping is endogenous to climate: it is more frequent in regions with low rainfall and higher weather variability. In a second step we test whether sharecropping can function as a substitute to other risk adaptation strategies. We find that sharecropping farmers are less likely to own livestock and more likely to use fertilizer. In economies where formal kinds of insurance are unavailable, sharecropping thus functions as a form of insurance and reduces the need for potentially harmful risk management strategies.
Schwerhoff, Gregor
Kalkuhl, Matthias
Waha, Katharina
2016
Do House Prices Hedge Inflation in the US? A Quantile Cointegration Approach
http://d.repec.org/n?u=RePEc:pre:wpaper:201707&r=rmg
This study analyses the long-run relationship between U.S house prices and non-housing Consumer Price Index (CPI) over the monthly period 1953 to 2016 using a quantile cointegration analysis. Our findings show evidence of instability in standard cointegration models, suggesting possibility of structural breaks and nonlinearity in the relationship between house prices and non-housing CPI. This motivates the use of a time-varying approach, namely, a quantile cointegration analysis, which allows the cointegrating coefficient to vary over the conditional distribution of house prices and simultaneously test for the existence of cointegration at each quantile. Our results suggest that the U.S non-housing CPI and house price index series are cointegrated at lower quantiles only, with house prices over-hedging inflation at these quantiles.
Christina Christou
Rangan Gupta
Wendy Nyakabawo
Mark E. Wohar
house prices, inflation, hedging, quantile cointegration
2017-02