Modeling conditional volatility by incorporating non-regular trading hours into the APARCH model

Abstract

This study aims to evaluate how the after-market and pre-opening periods affect the estimation of conditional volatility one day ahead. Volatility features quite a lot in Finance studies because it is a fundamental parameter in derivatives pricing, the efficient allocation of portfolios, and risk management. The results are relevant for investment agents to be able to refine volatility forecasting models and achieve better results in derivatives pricing, risk management, and portfolio optimization. We used the asymmetric power autoregressive conditional heteroscedasticity (APARCH) model, incorporating the after-market, pre-opening, and total overnight periods to assess whether they contain important information for modeling volatility. We analyzed the 20 stocks of Brazilian companies listed on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBovespa) and also belonging to the BR Titans 20 with ADRs listed on the New York Stock Exchange and the Nasdaq. The results were evaluated in-sample using the corrected Akaike information criterion (AICc) and the statistical significance of the coefficients, and out-of-sample using root mean squared error (RMSE), mean absolut percentage error (MAPE), the R² of the Mincer-Zarnowitz regression, and the Diebold Mariano test. The analysis does not enable it to be claimed which is the best model, because there is no unanimity among all the stocks; however, non-regular trading hours were shown to incorporate important information for most of the stocks. Furthermore, the models that incorporated the pre-opening period generally obtained superior results to the models that incorporated the after-market period, demonstrating that this period contains important information for forecasting conditional volatility.

THE RELATIONSHIP BETWEEN FINANCIAL INDICATORS AND FINANCIAL INTERMEDIARIES’ STOCK PRICE VOLATILITY LISTED ON THE BM&FBOVESPA INDEX IN THE CRISIS PERIOD OF 2008 AND 2020 (COVID-19)

“…Regarding disclosure levels, Malacrida and Yamamoto (2006) found that companies with a higher level of disclosure have a lower average volatility of stock returns than companies with a lower level of disclosure. Araújo, Camargos and Pinho (2019) found that the non-regular periods of the trading session showed to incorporate relevant information for most of the actions. Furthermore, the models that incorporated the preopening period had a greater impact on the nonregular period as a whole, being more significant for the modeling of conditional volatility.…”

The price continuity, return and volatility spillover effects of regular and after-hours trading

“…After-hours trading metrics, including returns and volatility estimates, have shown a predictive capacity for volatility during regular sessions [ 16 , 17 ]. Nevertheless, this predictive power diminishes when compared to forecasts for pre-market trading volatility [ 18 , 19 ].…”