Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks

Arbeitspapier

It is well known that intraday volatilities and trading volumes exhibit strong seasonal features. These seasonalities are usually modeled using dummy variables or deterministic functions. Here, we propose a test for seasonal long memory with a known frequency. Using this test, we show that deterministic seasonality is an accurate model for the DJIA index but not for the component stocks. These still exhibit significant and persistent periodicity after seasonal de-meaning so that more evolved seasonal long memory models are required to model their behavior.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Hannover Economic Papers (HEP) ; No. 599

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Financial Econometrics ; Asset Pricing; Trading Volume; Bond Interest Rates ; International Financial Markets
• Subject: Intraday Volatility ; Trading Volume ; Seasonality ; Long Memory

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Voges, Michelle ; Leschinski, Christian ; Sibbertsen, Philipp
• Event: Veröffentlichung
• (who): Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
• (where): Hannover
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/172853
• Last update: 18.04.0004, 6:42 AM CET