Volatility Computed by Time Series Operators at High Frequency

Olsen & Associates Working Paper No. 323

20 Pages Posted: 21 Mar 2000

Date Written: February 1, 2000

Abstract

Most financial markets produce inhomogeneous (i.e. unequally spaced) tick-by-tick data at high frequency. Recently developed time series operators can be used to directly compute statistical variables such as volatility from inhomogeneous data. This is not possible with traditional time series methods. Value-at-Risk computations require measurements of current volatility, but the conventional calculation from daily data, sampled at a certain daytime, is strongly sensitive to the choice of this daytime, revealing a high amount of stochastic noise. An alternative calculation from high-frequency, tick-by-tick data with time series operators is shown to have similar results, except for two advantages: distinctly reduced noise and up-to-date results at each tick. The time series operator method is flexible and computationally efficient. It can be used to express generating process equations and to compute the Value-at-Risk in real time.

JEL Classification: C14, C52, C53, C63, G10

Suggested Citation

Müller, Ulrich A., Volatility Computed by Time Series Operators at High Frequency (February 1, 2000). Olsen & Associates Working Paper No. 323, Available at SSRN: https://ssrn.com/abstract=208276 or http://dx.doi.org/10.2139/ssrn.208276

Ulrich A. Müller (Contact Author)

Olsen & Associates ( email )

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