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on Market Microstructure |
| By: | Francis X. Diebold (Department of Economics, University of Pennsylvania); Georg H. Strasser (Department of Economics, Boston College) |
| Abstract: | We argue for incorporating the financial economics of market microstructure into the financial econometrics of asset return volatility estimation. In particular, we use market microstructure theory to derive the cross-correlation function between latent returns and market microstructure noise, which feature prominently in the recent volatility literature. The cross-correlation at zero displacement is typically negative, and cross-correlations at nonzero displacements are positive and decay geometrically. If market makers are sufficiently risk averse, however, the cross-correlation pattern is inverted. Our results are useful for assessing the validity of the frequently-assumed independence of latent price and microstructure noise, for explaining observed crosscorrelation patterns, for predicting as-yet undiscovered patterns, and for making informed conjectures as to improved volatility estimation methods. |
| Keywords: | Realized volatility, Market microstructure theory, High-frequency data, Financial econometrics |
| JEL: | G14 G20 D82 D83 C51 |
| Date: | 2008–10–09 |
| URL: | http://d.repec.org/n?u=RePEc:boc:bocoec:693&r=mst |
| By: | Naoto Kunitomo (Faculty of Economics, University of Tokyo); Seisho Sato (Institute of Statistical Mathematics) |
| Abstract: | For the estimation problem of the realized volatility, covariance and hedging coefficient by using high frequency data with possibly micro-market noises, we use the Separating Information Maximum Likelihood (SIML) method, which was recently developed by Kunitomo and Sato (2008). By analyzing the Nikkei 225 futures and spot index markets, we have found that the estimates of realized volatility, covariance and hedging coefficient have significant bias by the traditional method which should be corrected. Our method can handle the estimation bias and the tick-size effects of Nikkei 225 futures by removing the possible micro-market noise in multivariate high frequency data. |
| Date: | 2008–11 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2008cf601&r=mst |