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.

中文翻译：

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改进的自适应卡尔曼滤波器的性能评估，最小均方和递归最小二乘法用于市场风险beta和VaR估计

自适应卡尔曼滤波器（AKF）以其导航应用而闻名。这项工作弥合了AKF发展过程中的差距，以处理具有自适应噪声协方差的参数不一致问题。重点是将建议的技术应用于资产的beta和VaR估算。拟议的过滤器的经验性能与标准最小二乘族和KF进行了VaR回测，预期不足分析和使用印度市场数据进行的样本内预测性能分析。结果表明，即使在这些自适应噪声协方差假设下，修改后的AKF的性能也与基准相当。