A Bayesian Approach for Dynamic Variation of Specific Sectors in Stock Exchange: A Case Study of Stock Exchange Thailand (SET) Indexes

This paper presents macroeconomic forecasting by using a time-varying Bayesian compressed vector autoregression approach. We apply a random compression by using projection matrix to randomly select predictive variables in vector autoregression (VAR), and then perform true out-of-sample forecast where the forecast values are averaged across all estimated models, containing different in both explanatory variables and number of those variables by using Bayesian model averaging (BMA). In addition to this, we allow the parameters in Bayesian compressed VAR to be time-varying by implementing dynamic model averaging (DMA) algorithm that is applicable with VAR using forgetting factor to control the degree of time-varying in the estimating parameters. We validate the performance of the proposed method via real macroeconomic data including up to 53 variables. The empirical results demonstrate that the predictive performance of time-varying Bayesian compressed VAR can beat traditional VAR types which are considered to have a potentiality to deal with large size variables. INDEX TERMS Bayesian econometrics, macroeconomics, Bayesian model averaging, mathematical models, time-varying parameters, dynamic model averaging, compression, Kalman filter

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A Bayesian Approach for Dynamic Variation of Specific Sectors in Stock Exchange: A Case Study of Stock Exchange Thailand (SET) Indexes

Cited by 3 publications

References 48 publications

A Large Dimensional VAR Model with Time-Varying Parameters for Daily Forex Forecasting

A Large Dimensional VAR Model with Time-Varying Parameters for Daily Forex Forecasting

Effects of macroeconomic factors on stock prices for BRICS using the variational mode decomposition and quantile method

A Time-Varying Bayesian Compressed Vector Autoregression for Macroeconomic Forecasting

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