Semiparametric estimation of multi-asset portfolio tail risk |
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| Institution: | 1. The Wang Yanan Institute for Studies in Economics, Xiamen University, Fujian 361005, China;2. Department of Economics, Cornell University, Ithaca, NY 14853, USA;1. Department of Accounting, Finance, and Economics, College of Business Administration, Winthrop University, 423 Thurmond Building, Rock Hill, SC 29733, United States;2. Department of Finance, Belk College of Business, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, United States;1. College of Management, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taiwan;2. School of Accounting, Economics, and Finance, Deakin University, Burwood, Victoria, Australia;3. Department of Finance, National University of Kaohsiung, Kaohsiung, Taiwan;1. UTS Business School, University of Technology Sydney, NSW 2007, Australia;2. Department of Economics, University of Leicester, LE17RH, UK;1. Humboldt University, Institute of Corporate Finance, Dorotheenstr. 1, 10099 Berlin, Germany;2. Ulm University, Institute of Strategic Management and Finance, Helmholtzstraße 22, 89081 Ulm, Germany |
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| Abstract: | When correlations between assets turn positive, multi-asset portfolios can become riskier than single assets. This article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets. The estimator captures simultaneously the information contained in each individual asset return that composes the portfolio, and the interrelation between assets. Noticeably, the accuracy of the estimates does not deteriorate when the number of assets in the portfolio increases. The implementation is as easy for a large number of assets as it is for a small number. We estimate the probability distribution of large losses for the American stock market considering portfolios with ten, fifty and one hundred assets of stocks with different market capitalization. In either case, the approximation for the portfolio tail risk is very accurate. We compare our results with well known benchmark models. |
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| Keywords: | Multi-asset portfolios Risk management Tail probability Tail risk Multivariate extreme value theory Value-at-Risk |
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