nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒07‒25
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Multivariate Shortfall Risk Allocation By Yannick Armenti; Stephane Crepey; Samuel Drapeau; Antonis Papapantoleon
  2. An Empirical Study of the Dynamic Correlation of Japanese Stock Returns. By Takashi Isogai
  3. Factorisable Sparse Tail Event Curves By Shih-Kang Chao; Wolfgang K. Härdle; Ming Yuan;
  4. Taming the Basel Leverage Cycle By Christoph Aymanns; Fabio Caccioli; J. Doyne Farmer; Vincent W. C. Tan
  5. "Cholesky Realized Stochastic Volatility Model" By Shinichiro Shirota; Yasuhiro Omori; Hedibert. F. Lopes; Haixiang Piao
  6. Endogenous derivation and forecast of lifetime PDs By Perederiy, Volodymyr
  7. Brent nefti opsiyonlarından neytral riskli ehtimal paylanmasının əldə olunması By Ahmadov, Vugar; Huseynov, Salman; Mammadov, Fuad; Karimli, Tural
  8. Financial Stock Market Co-movement and Correlation: Evidence in the European Union (EU) Area Before and After the October 2008 Financial Crisis By Serdar Neslihanoglu
  9. Strategic Framework for the Financial Management of Disaster Risk By World Bank Group
  10. Evaluating Insurer's Risk embedded in the Korean Reverse Mortgage Program Using Concurrent Simulation Method By Keunock Lew; Seungryul Ma
  11. Fiscal Disaster Risk Assessment Options for Consideration By World Bank Group; Global Facility for Disaster Reduction and Recovery
  12. Monitoring Vulnerability and Impact Diffusion in Financial Networks By Thiago Christiano Silva; Sergio Rubens Stancato de Souza; Benjamin Miranda Tabak
  13. Asymmetries and Portfolio Choice By Dahlquist, Magnus; Farago, Adam; Tédongap, Roméo
  14. Shape Regressions By Samantha Leorato; Franco Peracchi

  1. By: Yannick Armenti; Stephane Crepey; Samuel Drapeau; Antonis Papapantoleon
    Abstract: The ongoing concern about systemic risk since the outburst of the global financial crisis has highlighted the need for risk measures at the level of sets of interconnected financial components, such as portfolios, institutions or members of clearinghouses. The two main issues in systemic risk are the computation of an overall reserve level and its allocation to the different components of the system according to their importance. We develop here a pragmatic approach to systemic risk measurement and allocation based on multivariate shortfall risk measures, where acceptable allocations are first computed and then aggregated so as to minimize costs. We emphasize the analysis of the risk allocation and of its sensitivity as an indicator of the systemic risk. Moreover, we provide efficient numerical schemes to assess the risk allocation in high dimensions.
    Date: 2015–07
  2. By: Takashi Isogai (Bank of Japan)
    Abstract: We focus on the pairwise correlations of Japanese stock returns to study their correlation dynamics empirically. Two types of reduced size sample portfolios are created to observe the changes in conditional correlation: a set of individual stock portfolios created by using a network-based clustering algorithm and a single portfolio created from the mean return indexes of the individual sample portfolios. A multivariate GARCH model with dynamic conditional correlation (DCC) is then fitted to the return data of these sample portfolios independently. The estimation results show that the correlation matrices change over time in a way that depends on the sample portfolios; further, the DCC parameters are significantly different between them. Then, the time series of the maximum eigenvalues of the correlation matrices are calculated to observe the changes in correlation intensity. A higher level of correlation intensity is observed during crisis periods, namely after both the Lehman shock and the Great East Japan Earthquake. We also examine the impact of correlation changes on the risk of sample portfolios by using a numerical simulation, with the results showing non-negligible positive impacts. The comparative VaR backtesting simulation also suggests that DCC performs better than CCC.
    Keywords: Stock returns, dynamic correlation, DCC-GARCH, clustering, portfolio risk
    JEL: C1 C5 G1
    Date: 2015–07–13
  3. By: Shih-Kang Chao; Wolfgang K. Härdle; Ming Yuan;
    Abstract: In this paper, we propose a multivariate quantile regression method which enables localized analysis on conditional quantiles and global comovement analysis on conditional ranges for high-dimensional data. The proposed method, hereafter referred to as FActorisable Sparse Tail Event Curves, or FASTEC for short, exploits the potential factor structure of multivariate conditional quantiles through nuclear norm regularization and is particularly suitable for dealing with extreme quantiles. We study both theoretical properties and computational aspects of the estimating procedure for FASTEC. In particular, we derive nonasymptotic oracle bounds for the estimation error, and develope an efficient proximal gradient algorithm for the non-smooth optimization problem incurred in our estimating procedure. Merits of the proposed methodology are further demonstrated through applications to Conditional Autoregressive Value-at-Risk (CAViaR) (Engle and Manganelli; 2004), and a Chinese temperature dataset.
    Keywords: High-dimensional data analysis, multivariate quantile regression, quantile regression, value-at-risk, nuclear norm, multi-task learning
    JEL: C38 C63 G17 G20
    Date: 2015–07
  4. By: Christoph Aymanns; Fabio Caccioli; J. Doyne Farmer; Vincent W. C. Tan
    Abstract: Effective risk control must make a tradeoff between the microprudential risk of exogenous shocks to individual institutions and the macroprudential risks caused by their systemic interactions. We investigate a simple dynamical model for understanding this tradeoff, consisting of a bank with a leverage target and an unleveraged fundamental investor subject to exogenous noise with clustered volatility. The parameter space has three regions: (i) a stable region, where the system always reaches a fixed point equilibrium; (ii) a locally unstable region, characterized by cycles and chaotic behavior; and (iii) a globally unstable region. A crude calibration of parameters to data puts the model in region (ii). In this region there is a slowly building price bubble, resembling a "Great Moderation", followed by a crash, with a period of approximately 10-15 years, which we dub the "Basel leverage cycle". We propose a criterion for rating macroprudential policies based on their ability to minimize risk for a given average leverage. We construct a one parameter family of leverage policies that allows us to vary from the procyclical policies of Basel II or III, in which leverage decreases when volatility increases, to countercyclical policies in which leverage increases when volatility increases. We find the best policy depends critically on three parameters: The average leverage used by the bank; the relative size of the bank and the fundamentalist, and the amplitude of the exogenous noise. Basel II is optimal when the exogenous noise is high, the bank is small and leverage is low; in the opposite limit where the bank is large or leverage is high the optimal policy is closer to constant leverage. We also find that systemic risk can be dramatically decreased by lowering the leverage target adjustment speed of the banks.
    Date: 2015–07
  5. By: Shinichiro Shirota (Department of Statistical Science, Duke University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo); Hedibert. F. Lopes (Insper Institute of Education and Research); Haixiang Piao (Nippon Life Insurance Company)
    Abstract: Multivariate stochastic volatility models are expected to play important roles in financial applications such as asset allocation and risk management. However, these models suffer from two major difficulties: (1) there are too many parameters to estimate using only daily asset returns and (2) estimated covariance matrices are not guaranteed to be positive denite. Our approach takes advantage of realized covariances to attain the efficient estimation of parameters by incorporating additional information for the co-volatilities, and considers Cholesky decomposition to guarantee the positive definiteness of the covariance matrices. In this framework, we propose a exible modeling for stylized facts of financial markets such as dynamic correlations and leverage effects among volatilities. Taking a Bayesian approach, we describe Markov Chain Monte Carlo implementation with a simple but efficient sampling scheme. Our model is applied to nine U.S. stock returns data, and the model comparison is conducted based on portfolio performances. --
    Date: 2015–07
  6. By: Perederiy, Volodymyr
    Abstract: This paper proposes a simple technical approach for the derivation of future (forward) point-in-time PD forecasts, with minimal data requirements. The inputs required are the current and future through-the-cycle PDs of the obligors, their last known default rates, and a measure for the systematic dependence of the obligors. Technically, the forecasts are made from within a classical asset-based credit portfolio model, just with the assumption of a suitable autoregressive process for the systematic factor. The paper discusses in detail the practical issues of implementation, in particular the parametrization alternatives. The paper also shows how the approach can be naturally extended to low-default portfolios with volatile default rates, using Bayesian methodology. Furthermore, the expert judgments about the current macroeconomic state, although not necessary for the forecasts, can be embedded using the Bayesian technique. The presented forward PDs can be used for the derivation of lifetime credit losses required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective.
    Keywords: Prediction, Probability of Default, PD, Default Rates, Through-the-Cycle, TTC, Point-in-Time, PIT, Credit Portfolio Model, Systematic Factor, Macroeconomic Factor, Time Series, Autoregression, Bayesian Analysis, IFRS 9, Accounting, Financial Instruments, Lifetime, Expected Credit Losses
    JEL: C11 C13 C22 C51 C53 E32 E37 G33 M41
    Date: 2015–07–14
  7. By: Ahmadov, Vugar; Huseynov, Salman; Mammadov, Fuad; Karimli, Tural
    Abstract: In this study, we estimate a risk-neutral implied probability distribution using American call (put) options on Brent oil futures. For this purpose, we apply three different methodologies: non-parametric approach (kernel density estimation), semi-parametric approach by Shimko (1997), Datta and others (2014) and parametric approach by Bahra (1997), Melik and Tomas (1997). One advantage of probability distribution estimation is that besides providing us with average market expectation, it also helps to calculate different moments and attach probabilities to oil price expectations. This study intends to develop a necessary toolbox for policymakers to undertake different case study analysis that will facilitate decision-making process and helps them to promptly address the global shocks. As examples for the case studies, we examine impacts of Yemen - Saudi Arabia (airstrike) conflict and 166th OPEC meeting decisions on oil price expectations. We show that the methodologies employed for the estimations of implied probability distribution and case study analysis deliver plausible and convincing results. Note that, albeit study covers estimations of oil price expectations, employed methodology can be easily applied to other financial markets, i.e., international exchange market or LIBOR to asses expectations.
    Keywords: oil option prices, risk-neutral probability distribution, case study analysis
    JEL: C14 G13 G14 G17
    Date: 2015–07–14
  8. By: Serdar Neslihanoglu (Eskisehir Osmangazi University)
    Abstract: This paper investigates the effect that the financial stock market had on co-movement and correlation when modeling and forecasting individual financial stock market. According to both the Capital Asset Pricing Model (CAPM) (Sharpe-Linter-Mossin, 1960’s) and portfolio theory (Markowitz, 1952), the likely presence of correlations between various financial stock markets is an important issue for stock market portfolio managers; for example, in terms of portfolio diversification, it could reduce overall portfolio risk. Hence, we propose a multivariate extension of the conditional CAPM with a time-varying beta using the state space model; this, in turn, would allow the correlation between financial stock markets to be utilized during the estimation process. This paper presents evidence based on monthly data generated by several EU area financial stock markets before and after the October 2008 financial crisis and forecasting 1 year into the future. The empirical results overwhelmingly support one’s considering the financial stock market co-movement and correlation structure when modeling and forecasting individual EU area financial stock markets.
    Keywords: CAPM, Co-movement and Correlation, EU Area Financial Stock Markets, October 2008 Financial Crisis, Multivariate State Space Model, Systematic Risk.
    JEL: C52 C58 C19
  9. By: World Bank Group
    Keywords: Insurance and Risk Mitigation Urban Development - Hazard Risk Management Social Protections and Labor - Labor Policies Environment - Natural Disasters Finance and Financial Sector Development - Non Bank Financial Institutions
    Date: 2015–01
  10. By: Keunock Lew (Seoul National University of Science & Technology); Seungryul Ma (Government Employees Pension Foundation)
    Abstract: The paper conducted a concurrent simulation analysis to evaluate guarantor's risk in the reverse mortgage annuity program, considering that key variables of the program change simultaneously with their own stochastic processes. From the analysis with the data covering a period from September 2004 to December 2014, it was revealed that the probability of the guarantor having net liability (or net loss) turned out almost nothing (ie. merely 3.65%). Therefore, it is interpreted that the current program was designed very safely for the interest of guarantor and has room to increase monthly payment for annuitants. We also evaluated the effect of individual variable’s volatility on the magnitude of guarantor's total risk. From the analysis, it was confirmed that the current reverse mortgage program was designed to offset longevity risk which may increase with the period mortality rate of 2013 life table by market risk which can decrease with assuming low growth rate of housing price and high level of loan rates. The concurrent simulation is viewed as a more realistic way for evaluating guarantor’s risk because it assumes key variables to change simultaneously with their interdependency in the analysis. Therefore, the concurrent simulation results could give more rational implications to the government's policy makers as well as the reverse mortgage annuity market.
    Keywords: Reverse Mortgage Annuity, Stochastic Process, Guarantor Risk Evaluation, Concurrent Simulation
    JEL: G20 G22
  11. By: World Bank Group; Global Facility for Disaster Reduction and Recovery
    Keywords: Insurance Risk Mitigation Urban Development - Hazard Risk Management Environment - Natural Disasters Conflict and Development - Disaster Management Finance and Financial Sector Development - Debt Markets
    Date: 2015–03
  12. By: Thiago Christiano Silva; Sergio Rubens Stancato de Souza; Benjamin Miranda Tabak
    Abstract: In this paper, we propose novel risk-related network measurements to identify the roles that financial institutions play as potential targets or sources of contagion. We derive theoretical properties and provide a clear systemic risk interpretation for the proposed measures. Devised upon the notion of communicability in networks, we introduce the impact susceptibility index, which indicates whether market participants are locally or remotely vulnerable. We show that this index can be used as a financial stability monitoring tool and apply it to analyze the Brazilian financial market. We find that non-banking institutions are potentially remote vulnerable in certain periods, while banking institutions are not susceptible to indirect impacts. To address the perspective of market participants as sources of contagion, we propose the impact diffusion influence index, which captures the potential influence of financial institutions on propagating impacts in the network. We unveil the presence of a portion of non-large banking institutions that is consistently more influential than large banks in potentially diffusing impacts throughout the network. Regarding financial system stability, regulators should identify the entities that play these two roles, as they can render the system more risky
    Date: 2015–07
  13. By: Dahlquist, Magnus; Farago, Adam; Tédongap, Roméo
    Abstract: We examine the portfolio choice of an investor with generalized disappointment aversion preferences who faces returns described by a normal-exponential model. We derive a three-fund separation strategy: the investor allocates wealth to a risk-free asset, a standard mean-variance efficient fund, and an additional fund reflecting return asymmetries. The optimal portfolio is characterized by the investor's endogenous effective risk aversion and implicit asymmetry aversion. We find that disappointment aversion is associated with much larger asymmetry aversion than are standard preferences. Our model explains patterns in popular portfolio advice and provides a reason for shifting from bonds to stocks as the investment horizon increases.
    Keywords: Asset allocation; Downside risk
    JEL: G11
    Date: 2015–07
  14. By: Samantha Leorato (University of Rome "Tor Vergata"); Franco Peracchi (Georgetown University, University of Rome "Tor Vergata" and EIEF)
    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.
    Date: 2015

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