In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting perf...

9:55 – 10:40 Rodney Strachan (University of Queensland) Reducing Dimensions in Large Time-varying Parameter VAR Models This paper proposes a new approach to estimating high dimensional time varying parameter vector autoregressive models (TVP-VARs). Such models are rarely used with more than 4-5 variables. However recent work has shown the advantages of modelling VARs with large numbers of varia...

We estimate a large Bayesian time-varying parameter vector autoregressive (TVP-VAR) model of daily stock return volatilities for 35 U.S. and European financial institutions. Based on that model we extract a connectedness index in the spirit of Diebold and Yilmaz (2014) (DYCI). We show that the connectedness index from the TVP-VAR model captures abrupt turning points better than the one obtained...

This paper examined the volatility spillover effects between futures market and spot market in China, using both VAR model and TVP-VAR model. This study found strong bi-directional volatility spillovers between CSI futures and spot markets, and the change of futures’ volatility decreased the change of spot market’s volatility. This results support the hypothesis that the risk management functio...

We investigated the connectedness of returns and volatility clean energy stock, technology crude oil, natural gas, investor sentiment based on time-varying parameter vector autoregressive (TVP-VAR) approach. The empirical results indicate that average total is higher in system than return system. has a weak impact stock. Our show dynamic across assets varies with time. Furthermore, increases si...