A simple method for time scaling value-at-risk: Let the data speak for themselves
Abstract
New empirical and data-based scaling factors are introduced to scale the 1-day value-at-risk (VaR) to 5- and 10-day VaR. The method relies on the estimation of the ratio of the high quantiles of the aggregated data to the daily data. Using real data sets, the new scaling factors are compared with the commonly used benchmark of square-root-of-time scaling, and a more complex simulation-based estimation method. The results indicate that the empirical scaling factors outperform against the square-root-of-time scaling and are competitive with the simulation-based method.
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Author's Biography
Kamal Hamidieh is a National Science Foundation Vertical Integration of Research and Education (NSF VIGRE) Post-Doctoral Instructor at Rice University Statistics Department. He holds a PhD in statistics from the University of Michigan, and his research focuses on extreme value theory and risk management.
Katherine Bennett Ensor is professor and chair of the Department of Statistics and founding director of the Center of Computational Finance and Economic Systems at Rice University. She works in the area of quantitative risk management and the analysis of dependent financial time series. Katherine is an elected Fellow of the American Statistical Association and a recipient of the H. O. Hartley Award, given to outstanding alumni, from Texas A&M University. This work is supported through her National Science Foundation Award DMS 07939420.
Citation
Hamidieh, Kamal and Ensor, Katherine Bennett (2010, September 1). A simple method for time scaling value-at-risk: Let the data speak for themselves. In the Journal of Risk Management in Financial Institutions, Volume 3, Issue 4. https://doi.org/10.69554/NMKI3872.
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