Modeling and Forecasting the CBOE VIX With the TVP‐HAR Model

This study proposes the use of a heterogeneous autoregressive model with time‐varying parameters (TVP‐HAR) to model and forecast the Chicago Board Options Exchange (CBOE) volatility index (VIX). To demonstrate the superiority of the TVP‐HAR model, we consider six variations of the model with different bandwidths and smoothing variables and include the constant‐coefficient HAR model as a benchmark for comparison. We show that the TVP‐HAR models could beat the HAR model with constant coefficients in modeling and forecasting VIX. Among the TVP‐HAR models, the rule‐of‐thumb bandwidth would be better than the cross‐validation bandwidth. Meanwhile, VIX futures‐driven coefficients could also provide more accurate predictions and smaller capital losses than the other two variables. Overall, the VIX futures‐driven coefficients TVP‐HAR model with the rule‐of‐thumb bandwidth obtains the optimal result for investors in forecasting the market risks and shaping their hedging strategies.