Estimation of market efficiency process within time-varying autoregressive models by extended Kalman filtering approach
Maria Kulikova, Gennady Kulikov

TL;DR
This paper introduces an extended Kalman filtering approach to better estimate the evolving level of market efficiency in time-varying autoregressive models, applied to major stock indices over nearly a century.
Contribution
It proposes a novel estimation method using extended Kalman filter for nonlinear dynamics in market efficiency models, improving upon traditional linear Kalman filter approaches.
Findings
Market remained weak-form efficient since 1946
Market efficiency was affected during major crises
Extended Kalman filter provided more accurate estimates
Abstract
This paper explores a time-varying version of weak-form market efficiency that is a key component of the so-called Adaptive Market Hypothesis (AMH). One of the most common methodologies used for modeling and estimating a degree of market efficiency lies in an analysis of the serial autocorrelation in observed return series. Under the AMH, a time-varying market efficiency level is modeled by time-varying autoregressive (AR) process and traditionally estimated by the Kalman filter (KF). Being a linear estimator, the KF is hardly capable to track the hidden nonlinear dynamics that is an essential feature of the models under investigation. The contribution of this paper is threefold. We first provide a brief overview of time-varying AR models and estimation methods utilized for testing a weak-form market efficiency in econometrics literature. Secondly, we propose novel accurate estimation…
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