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Trading volume and volatility patterns across selected Central European stock markets from microstructural perspective

Gurgul H., Syrek R.

Managerial Economics

2017

Vol. 18, no. 1

87--101

In this paper, the intraday patterns of trading volumes and volatilities as well as autocorrelations are investigated using high-frequency data. The analysis is performed for companies listed in the main German, Austrian, and Polish indices with the aid of Flexible Fourier Form regression. We have found some similarities to prior investigations in light of stylized facts about intraday patterns. We noted the differences in intraday patterns and autocorrelations across markets, which depend on the maturity level of the market. The most-regular patterns are observed for DAX companies. Additionally, using day-of-the-week dummies, we discover some peaks that can be associated with macroeconomic announcements in Germany and the US. This leads to conclusions that the day of the week and announcements should be taken into account in modeling volatilities (returns) and volumes from high-frequency data.

An analysis of the performance of genetic programming for realised volatility forecasting

Yin Z., O’Sullivan C., Brabazon A.

Journal of Artificial Intelligence and Soft Computing Research

2016

Vol. 6, No. 3

155--172

Traditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.