TY - GEN
T1 - A Recursive Bayesian Model for Extreme Values
AU - Johnston, Douglas E.
AU - Djuric, Petar M.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper, we propose a new approach for analyzing extreme values such as large losses in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time. We apply a stochastic parametrization of the generalized extreme value distribution to model the asymptotic behavior of the block-maximum and derive a Rao-Blackwellized particle filter. This reduces the parameter space, and we derive a concise, recursive solution. Using the filter, the predictive distribution, conditioned on the past data, is computed at each sample-time. We introduce a new risk-measure, {p-{{\text{Va}}{{\text{R}}-\alpha }}}, that is a more robust estimate of the true nature of value-at-risk, and illustrate our results using both simulated data and actual stock market returns from 1928-2017.
AB - In this paper, we propose a new approach for analyzing extreme values such as large losses in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time. We apply a stochastic parametrization of the generalized extreme value distribution to model the asymptotic behavior of the block-maximum and derive a Rao-Blackwellized particle filter. This reduces the parameter space, and we derive a concise, recursive solution. Using the filter, the predictive distribution, conditioned on the past data, is computed at each sample-time. We introduce a new risk-measure, {p-{{\text{Va}}{{\text{R}}-\alpha }}}, that is a more robust estimate of the true nature of value-at-risk, and illustrate our results using both simulated data and actual stock market returns from 1928-2017.
KW - extreme value theory
KW - particle filter
KW - risk-management
KW - VaR
UR - https://www.scopus.com/pages/publications/85068977602
U2 - 10.1109/ICASSP.2019.8682828
DO - 10.1109/ICASSP.2019.8682828
M3 - Conference contribution
AN - SCOPUS:85068977602
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5062
EP - 5066
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -