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Performance evaluation of modified adaptive Kalman filters, least means square and recursive least square methods for market risk beta and VaR estimation

Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India

Special Issues: Advances in Forecasting Financial and Macroeconomic Variables Using Econometric Methods

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.
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Keywords adaptive estimation; noise covariance adaptation; recursive least square; least mean square; modified AKF; market risk; beta; value-at-Risk

Citation: Atanu Das. Performance evaluation of modified adaptive Kalman filters, least means square and recursive least square methods for market risk beta and VaR estimation. Quantitative Finance and Economics, 2019, 3(1): 124-144. doi: 10.3934/QFE.2019.1.124


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Copyright Info: © 2019, Atanu Das, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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