报告题目 Simulating Risk Measures with Estimated Relative Errors
摘要:Risk measures, such as value-at-risk and expected shortfall, are widely used in stochastic models. With the necessary sample size being computed using asymptotic expansions of relative errors for a wide class of dependent samples, we propose a general framework to simulate these risk measures via a sorting algorithm. The asymptotic expansions appear to be new even for independent and identical samples. An extensive numerical study is conducted to compare the proposed algorithm against existing algorithms, showing that the new algorithm is easy to implement, fast and accurate, even at the 0.001 quantile level. Applications to the estimation of intra-horizon risk and to a comparison of the relative errors of value-at-risk and expected shortfall are also given. This is a joint work with Wei Jiang.
报告人简介：Prof. Steven Kou is the Director of the Risk Management Institute (RMI). He is also a Class of ’62 Professor of Mathematics at National University of Singapore (NUS). Formerly from Columbia University’s Department of Industrial Engineering and Operations Research, Professor Kou joined NUS as a Professor in Mathematics and Provost’s Chair in 2013.
Recognized internationally as one of the best in the research areas of Financial Mathematics/Engineering, Professor Kou’s most important works are on modelling jumps in asset pricing for equity, credit and fixed income markets and pricing of path-dependent options. His scholarly achievements have been recognized by the scientific community, as demonstrated by his receipt of the Erlang Prize (2002) from INFORMS, the leading association for professionals in the fields of analytics, management science and operations research. Professor Kou is currently also serving in the editorial boards of several top journals in his field, including Operations Research, Mathematical Finance, Mathematics of Operations Research, and Journal of Business and Economic Statistics.