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

100%

Gurgul H. , Syrek R.

Managerial Economics

2017

tom 18

nr 1

87-102

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

100%

Yin Z. , O’Sullivan C. , Brabazon A.

2016

tom 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.

WYKORZYSTANIE DANYCH OCZYSZCZONYCH O WYSOKIEJ CZĘSTOTLIWOŚCI W PROGNOZOWANIU ZMIENNYCH ZE ZŁOŻONĄ SEZONOWOŚCIĄ

63%

Szmuksta-Zawadzka M. , Zawadzki J.

2015

tom 16

nr 4

147-159

W pracy przedstawione zostanie procedura modelowania i prognozowania zmiennej o bardzo wysokiej częstotliwości obserwowania na podstawie szeregów, z których wyeliminowano dwa lub trzy rodzaje sezonowości. Do budowy prognoz zostaną wykorzystane wybrane modele adaptacyjne. Rozważania teoretyczne zilustrowane zostaną przykładem empirycznym dotyczącym, kształtowania się zapotrzebowania na moc energetyczną w okresach godzinnych w aglomeracji A.

In the article will be presented procedure to modeling and forecasting of the high frequency variable, based on series, from which was eliminated two or three types of seasonality. Forecasts will be built on the basis of exponential smoothing models. The theoretical considerations will be illustrated with empirical example about demand for electricity in hour periods in the agglomeration A.

MODELE ADAPTACYJNE W PROGNOZOWANIU NA PODSTAWIE SZEREGÓW CZASOWYCH O WYSOKIEJ CZĘSTOTLIWOŚCI Z LUKAMI SYSTEMATYCZNYMI

63%

Zawadzki J.

Metody Ilościowe w Badaniach Ekonomicznych

2017

tom 18

nr 2

374-389

W pracy przedstawione zostaną wyniki zastosowania modeli Browna, Holta i Holta-Wintersa w prognozowaniu zmiennej o bardzo wysokiej częstotliwości obserwowania w warunkach braku pełnej informacji na podstawie danych oczyszczonych z dwóch lub trzech rodzajów sezonowości. Rozpatrywany były dwa warianty luk systematycznych.

In the paper will be presented results of the application of Brown, Holt and Holt-Winters models in the forecasting of a very high frequency variable in condition of lack of full information, based on seasonal adjusted time series, from which two or three types of seasonal fluctuations were removed. Two variants of systematic gaps were considered.