
on Risk Management 
By:  Carbajal De Nova, Carolina 
Abstract:  The proposed method attempts to contribute towards the econometric and simulation applied risk management literature. It consists on an algorithm to construct synthetic data and risk simulation econometric models, supported by a set of behavioral assumptions. This algorithm has the advantage of replicating natural phenomena and uncertainty events in a short period of time. These features convey economically low costs besides computational efficiency. An application for wheat farmers is developed. The efficiency of this method is confirmed when its results and statistical inference converge with those generated from experimental data. Convergence is demonstrated specifically by means of information convergence and diminishing scaling variance. Modifications on the proposed algorithm regarding risk distribution parameters are not onerous. These modifications can generate diverse risk scenarios seeking to minimize and manage risk. Hence, risk sources could be anticipated, identified as well as quantified. The algorithm flexibility makes risk testing accessible to an ample variety of entrepreneurial problems i.e., public health systems, farmers associations, hedge funds, insurance companies; etcetera. This method could provide grounded criteria for decisionmaking in order to improve management practices. 
Keywords:  behavioral assumptions, risk scenarios, simulation econometric models, synthetic data 
JEL:  G02 
Date:  2017–03–28 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:77978&r=rmg 
By:  Sergey Nadtochiy; Mykhaylo Shkolnikov 
Abstract:  We propose an interacting particle system to model the evolution of a system of banks with mutual exposures. In this model, a bank defaults when its normalized asset value hits a lower threshold, and its default causes instantaneous losses to other banks, possibly triggering a cascade of defaults. The strength of this interaction is determined by the level of the socalled noncore exposure. We show that, when the size of the system becomes large, the cumulative loss process of a bank resulting from the defaults of other banks exhibits discontinuities. These discontinuities are naturally interpreted as systemic events, and we characterize them explicitly in terms of the level of noncore exposure and the fraction of banks that are "about to default". The main mathematical challenges of our work stem from the very singular nature of the interaction between the particles, which is inherited by the limiting system. A similar particle system is analyzed in [DIRT15a] and [DIRT15b], and we build on and extend their results. In particular, we characterize the largepopulation limit of the system and analyze the jump times, the regularity between jumps, and the local uniqueness of the limiting process. 
Date:  2017–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1705.00691&r=rmg 
By:  ZhiFu Mi; YiMing Wei (Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology); BaoJun Tang; RongGang Cong; Hao Yu; Hong Cao; Dabo Guan 
Abstract:  The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive reestimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the Value at Risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of Generalized Error Distribution (GED). The results show that EVT is a powerful approach to capture the risk in the oil markets. On the contrary, the traditional VarianceCovariance and Monte Carlo approaches tend to overestimate risk when the confidence level is 95%, but underestimate risk at the confidence level of 99%. The VaR of WTI returns is larger than that of Brent returns at identical confidence levels. Moreover, the GEDGARCH model can estimate the downside dynamic VaR accurately for the WTI and Brent oil returns. 
Keywords:  Value at risk; GEDGARCH; Extreme value theory; Risk quantification; oil markets 
JEL:  Q54 Q40 
Date:  2017–04–01 
URL:  http://d.repec.org/n?u=RePEc:biw:wpaper:102&r=rmg 
By:  Tetsuya Takaishi 
Abstract:  We study stock market instability by using crosscorrelations constructed from the return time series of 366 stocks traded on the Tokyo Stock Exchange from January 5, 1998 to December 30, 2013. To investigate the dynamical evolution of the crosscorrelations, crosscorrelation matrices are calculated with a rolling window of 400 days. To quantify the volatile market stages where the potential risk is high, we apply the principal components analysis and measure the cumulative risk fraction (CRF), which is the system variance associated with the first few principal components. From the CRF, we detected three volatile market stages corresponding to the bankruptcy of Lehman Brothers, the 2011 Tohoku Region Pacific Coast Earthquake, and the FRB QE3 reduction observation in the study period. We further apply the random matrix theory for the risk analysis and find that the first eigenvector is more equally delocalized when the market is volatile. 
Date:  2017–04 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1704.08612&r=rmg 
By:  Umberto Cherubini; Paolo Neri 
Abstract:  We consider the problem of risk diversification of $\alpha$stable heavy tailed risks. We study the behaviour of the aggregated ValueatRisk, with particular reference to the impact of different tail dependence structures on the limits to diversification. We confirm the large evidence of subadditivity violations, particularly for risks with low tail index values and positive dependence. So, reinsurance strategies are not allowed to exploit diversification gains, or only a very limited amount of them. Concerning the impact of tail dependence, we find the peculiar results that for high tail dependence levels the limits to diversification are uniformly lower for all the levels of dependence, and for all levels of $\alpha 
Date:  2017–04 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1704.07235&r=rmg 
By:  Hyeongwoo Kim; Kyunghwan Ko 
Abstract:  We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in outofsample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in shortterm forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets. 
Keywords:  Partial Least Squares; Principal Component Analysis; Financial Stress Index; OutofSample Forecast; RRMSPE 
JEL:  C38 C53 C55 E44 E47 G01 G17 
Date:  2017–05 
URL:  http://d.repec.org/n?u=RePEc:abn:wpaper:auwp201703&r=rmg 