Trading Frequency and Volatility Clustering
AbstractVolatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. This paper presents a market microstructure model, that is able to generate volatility clustering with hyperbolic autocorrelations through traders with multiple trading frequencies using Bayesian information updating in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequency, can increase the persistence of the volatility of returns. Furthermore, we show that the local temporal memory of the underlying time series of returns and their volatility varies greatly varies with the number of traders in the market
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Bibliographic InfoPaper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 31_09.
Date of creation: Jan 2009
Date of revision: Jan 2009
Trading frequency; Volatility clustering; Signal extraction; Hyperbolic decay;
Other versions of this item:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- D43 - Microeconomics - - Market Structure and Pricing - - - Oligopoly and Other Forms of Market Imperfection
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
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