文章基本信息

标题：Performance evaluation of modified adaptive Kalman filters, least means square and recursive least square methods for market risk beta and VaR estimation
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作者：Eni Kusumaningtyas ; Raphaella Widiastuti ; Harsi Dewantari Kusumaningrum 等
期刊名称：Quantitative Finance and Economics
电子版ISSN：2573-0134
出版年度：2019
卷号：3
期号：1
页码：124-144
DOI：10.3934/QFE.2019.1.124
语种：English
出版社：AIMS Press
摘要：Adaptive Kalman Filters (AKFs) are well known for their navigational applications. This work bridges the gap in the evolution of AKFs to handle parameter inconsistency problems with adaptive noise covariances. The focus is to apply proposed techniques for beta and VaR estimation of assets. The empirical performance of the proposed filters are compared with the standard least square family and KF with respect to VaR backtesting, expected shortfall analysis and in-sample forecasting performance analysis using Indian market data. Results show that the Modified AKFs are performing at par with the bench mark even with these adaptive noise covariance assumptions.
关键词：adaptive estimation;noise covariance adaptation;recursive least square;least mean square;modified AKF;market risk;beta;value-at-Risk