caa a22 4500 605519641 CHVBK 20210128100731.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11009-014-9396-5 doi (NATIONALLICENCE)springer-10.1007/s11009-014-9396-5 Distributional Bounds for Portfolio Risk with Tail Dependence [Elektronische Daten] [Kunio So, Junichi Imai] The present paper proposes a new method for estimating portfolio risk by applying the concept of bounds to a dependence structure. We introduce four tail dependence measures as partial dependence information and derive bounds on the distribution of a non-decreasing function to obtain bounds on risk measures. We show that bounds on risk measures can be tightened significantly in the probability levels with which we are concerned, those for financial risk management. In the present paper, we provide theorems describing the distributional bounds of the proposed method and prove that these bounds are pointwise best-possible bounds. Furthermore, we calculate risk measures, i.e., value at risk and expected shortfall, from empirical return data and compare the effectiveness of the proposed model with that of typical parametric copula models. Springer Science+Business Media New York, 2014 Risk management nationallicence Market risks nationallicence Tail dependence nationallicence Copulas nationallicence Fréchet bounds nationallicence So Kunio Graduate School of Science and Technolog, Keio University, 3-14-1 Hiyoshi, Kohoku, 223-8522, Yokohama, Japan aut Imai Junichi Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku, 223-8522, Yokohama, Japan aut Methodology and Computing in Applied Probability Springer US; http://www.springer-ny.com 17/3(2015-09-01), 795-816 1387-5841 17:3<795 2015 17 11009 https://doi.org/10.1007/s11009-014-9396-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11009-014-9396-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- So Kunio Graduate School of Science and Technolog, Keio University, 3-14-1 Hiyoshi, Kohoku, 223-8522, Yokohama, Japan aut NATIONALLICENCE 700 1- Imai Junichi Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku, 223-8522, Yokohama, Japan aut NATIONALLICENCE 773 0- Methodology and Computing in Applied Probability Springer US; http://www.springer-ny.com 17/3(2015-09-01), 795-816 1387-5841 17:3<795 2015 17 11009