A Filtering Process to Remove the Stochastic Component from Intraday Seasonal Volatility

摘要

The intraday seasonal variance pattern contains stochastic as well as deterministic components. Therefore, the estimation of information arrivals in the associated volatility process requires the proper filtering of both of these seasonal components. However, popular current models remove only the deterministic part of the typical U-shape volatility. Here, we provide the first empirical results of the importance of the stochastic component, as developed by Cho and Daigler (2012). We show that a highly significant additional 8.5% to 12.9% of the total seasonal variance is explained by the stochastic seasonal variance component for S&P500 futures, live cattle futures, and the Japanese yen-U.S. dollar spot exchange rate. Moreover, we show that the stochastic seasonal filtering model implemented here does not create any statistical distortions of the filtered series, as occurs with deterministic-based seasonal adjustment processes, as well as comparing the model examined here with the most popular current deterministic model. As part of our analysis we examine the application of the model to macroeconomic news and out-of-sample results for the model.