Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Evolving efficiency of the BRICS markets

Maria V. Kulikova, David R. Taylor, Gennady Yu. Kulikov

Abstract

This paper investigates a time-varying version of weak-form market efficiency in the BRICS countries. A moving window test for sample autocorrelations is applied alongside a Kalman filter approach to recover the hidden dynamics of the market efficiency process through appropriate time-varying autoregressive models with both homoscedastic and heteroscedastic conditional variance. Monthly data covers the period from January 1995 to December 2020, which includes the 2008-2009 global financial crisis and the recent COVID-19 recession. The results reveal that all the BRICS stock markets were affected during both periods, but generally remained weak-form efficient, with the exception of China.

Evolving efficiency of the BRICS markets

Abstract

This paper investigates a time-varying version of weak-form market efficiency in the BRICS countries. A moving window test for sample autocorrelations is applied alongside a Kalman filter approach to recover the hidden dynamics of the market efficiency process through appropriate time-varying autoregressive models with both homoscedastic and heteroscedastic conditional variance. Monthly data covers the period from January 1995 to December 2020, which includes the 2008-2009 global financial crisis and the recent COVID-19 recession. The results reveal that all the BRICS stock markets were affected during both periods, but generally remained weak-form efficient, with the exception of China.
Paper Structure (10 sections, 16 equations, 6 figures, 4 tables)

This paper contains 10 sections, 16 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. A brief overview of the market efficiency literature for BRICS countries
  3. Statistical description of BRICS markets data
  4. Rolling window tests for evolving efficiency of BRICS markets
  5. Modeling techniques for evolving efficiency estimation and application to BRICS
  6. Estimation results for evolving efficiency of BRICS markets and discussion
  7. Concluding remarks
  8. Classical Kalman filter and state-space model calibration
  9. Linear state-space form for time-varying AR($n$) models
  10. Linear state-space form for GARCH-type model

Figures (6)

  • Figure 1: The sample autocorrelation coefficient $\hat{\rho}_1$ computed by the moving window method for the Brazilian stock market with $1$% confidence bounds (left) and the $p$-values of the rolling window Q-test for residual autocorrelation (right).
  • Figure 2: The sample autocorrelation coefficient $\hat{\rho}_1$ computed by the moving window method for the Russian stock market with $1$% confidence bounds (left) and the $p$-values of the rolling window Q-test for residual autocorrelation (right).
  • Figure 3: The sample autocorrelation coefficient $\hat{\rho}_1$ computed by the moving window method for the Indian stock market with $1$% confidence bounds (left) and the $p$-values of the rolling window Q-test for residual autocorrelation (right).
  • Figure 4: The sample autocorrelation coefficient $\hat{\rho}_1$ computed by the moving window method for the Chinese stock market with $1$% confidence bounds (left) and the $p$-values of the rolling window Q-test for residual autocorrelation (right).
  • Figure 5: The sample autocorrelation coefficient $\hat{\rho}_1$ computed by the moving window method for the South African stock market with $1$% confidence bounds (left) and the $p$-values of the rolling window Q-test for residual autocorrelation (right).
  • ...and 1 more figures