Varying the VaR for unconditional and conditional environments

Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from 12 European bourses, this paper presents VaR measures based on their unconditional and conditional distributions for single and multi-period settings. These measures underpinned by extreme value theory are statistically robust explicitly allowing for fat-tailed densities. Conditional tail estimates accounting for volatility clustering are obtained by adjusting the unconditional extreme value procedure with GARCH filtered returns. The conditional modelling results in iid returns allowing for the use of a simple and efficient multi-period extreme value scaling law. The paper examines the properties of these distinct conditional and unconditional trading models. The paper finds that the biases inherent in unconditional single and multi-period estimates assuming normality extend to the conditional setting.     

中国股票市场风险度量的模型选择与比较研究

分别采用等权移动平均方法、指教加权移动平均方法、GARCH(1,1)方法、GARCH(1,1)-t方法和Pareto型极值分布方法计算上海和深圳股票日收益率的VaR.向后检验表明,Pareto型极值分布方法比其他方法更能准确地反映我国股市的风险.     

Tail dependence and indicators of systemic risk for large US depositories

In this study, we investigate the extreme loss tail dependence between stock returns of large US depository institutions. We find that stock returns exhibit strong loss dependence even in their limiting joint extremes. Motivated by this result, we derive extremal dependence-based systemic risk indicators. The proposed systemic risk indicators reflect downturns in the US financial industry very well. We also develop a set of firm-level average extremal dependence measures. We show that these firm-level measures could have been used to identify the firms that were more vulnerable to the 2007–2008 financial crisis. Additionally, we explore the performance of selected systemic risk indicators in predicting the crisis performance of large US depository institutions and find that the average stock return correlations are also good predictors of crisis period returns. Finally, we identify factors predictive of extremal dependence for the US depository institutions in a panel regression setting. Strength of extremal dependence increases with asset size and similarity of financial fundamentals. On the other hand, strength of extremal dependence decreases with capitalization, liquidity, funding stability and asset quality. We believe the proposed indicators have the potential to inform the prudential supervision of systemic risk.     

基于风险价值法和压力测试法的企业风险管理方法研究

本文提出风险价值法和压力测试法的企业风险管理方法,克服了传统方法只给出风险相对严重程度的不足。建立风险量化评估、预警和控制体系,采用优化组合方法,实施一体化风险管理,规避重大风险事件的发生。     

Learning about Risk: Some Lessons from Insurance

This paper argues that in the fundamental subject of financialrisk analysis, some valuable lessons may be drawn from insurance.The probability of ruin, defined as a first passage time, carriesa dynamic element whose absence in Value at Risk is one liability,among others. Extreme value theory, which has been successfullyapplied to insurance shortly after it was introduced in probability,may offer a coherent framework for analyzing the extreme movessuch as the ones observed in recent foreign exchange and financialcrises. Lastly, we show that the genuine hazards generated byglobal capital markets and illustrated by the events of summer1998, generate a market incompleteness that existing modelsof defaultable bonds do not fully address. In contrast, thelong experience of risk premium analysis in the insurance andreinsurance industry, as well as the existence of historicaldata on natural disasters, render the valuation of catastrophebonds less perilous than that of defaultable bonds.     

Computing value at risk with high frequency data

We compare the computation of value at risk with daily and with high frequency data for the Deutsche mark–US dollar exchange rate. Among the main points considered in the paper are: (a) the comparison of measures of value at risk on the basis of multi-step volatility forecasts; (b) the computation of the degree of fractional differencing for high frequency data in the context of a Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) model; and (c) the comparison between deterministic and stochastic models for the filtering of high frequency returns.     

An options-based approach to evaluating the risk of Fannie Mae and Freddie Mac

Fannie Mae and Freddie Mac assume a significant amount of interest and prepayment risk and all of the credit risk for about half of the $8 trillion U.S. residential mortgage market. Their hybrid government-private status, and the perception that they are too big to fail, make them a potentially large, but largely unaccounted for, risk to the federal government. Measuring the size and risk of this liability is technically difficult, but important for the debate over the appropriate regulation of these institutions. Here we take an options pricing approach to evaluating these costs and risks. Under the base case assumptions, the estimated value of the guarantees is $7.9 billion over 10 years, with a combined .5 percent value at risk of $122 billion. We evaluate the sensitivity of these estimates to various modeling assumptions, and also to the regulatory regime, including forbearance policies and capital requirements. The analysis highlights the benefits, but also the challenges, of taking an options-based approach to evaluating the value of federal credit guarantees.