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on Market Microstructure |
| By: | Silja Kinnebrock; Mark Podolskij |
| Abstract: | This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis and covariance, for which we obtain the optimal rate of convergence. We demonstrate some positive semidefinite estimators of the covariation and construct a positive semidefinite estimator of the conditional covariance matrix in the central limit theorem. Furthermore, we indicate how the assumptions on the noise process can be relaxed and how our method can be applied to non-synchronous observations. We also present an empirical study of how high-frequency correlations, regressions and covariances change through time. |
| Keywords: | Central Limit Theorem; Diffusion Models; Market Microstructure Noise; Non-synchronous Trading; High-Frequency Data; Semimartingale Theory; |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2008fe25&r=mst |
| By: | Loriana Pelizzon (Department of Economics, University Of Venice Cà Foscari); Monica Billio (Department of Economics, University Of Venice Cà Foscari); Mila Getmansky (Department of Finance and Operations Management Isenberg School of Management University of Massachusetts) |
| Abstract: | This paper examines four different daily datasets of hedge fund return indexes: MSCI, FTSE, Dow Jones and HFRX, all based on investable hedge funds, and three different monthly datasets of hedge fund return indexes: CSFB, CISDM and HFR which comprise both investable and non-investable hedge funds. Our study, based on standard statistical analysis, non-parametric analysis of the distribution and non-parametric regressions with respect to the S&P500 index shows that key data biases and disparate index construction methodologies lead to different statistical properties of hedge fund databases. One key variable that highly affects the statistical properties of hedge fund index returns is the “investability” of hedge funds |
| Keywords: | Hedge Fund, Risk Management, High frequency data |
| JEL: | G12 G29 C51 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2008_11&r=mst |
| By: | Loriana Pelizzon (Department of Economics, University Of Venice Cà Foscari); Monica Billio (Department of Economics, University Of Venice Cà Foscari); Mila Getmansky (Department of Finance and Operations Management Isenberg School of Management University of Massachusetts) |
| Abstract: | We study the effect of financial crises on hedge fund risk. Using a regime-switching beta model, we separate systematic and idiosyncratic components of hedge fund exposure. The systematic exposure to various risk factors is conditional on market volatility conditions. We find that in the high-volatility regime (when the market is rolling-down and is likely to be in a crisis state) most strategies are negatively and significantly exposed to the Large-Small and Credit Spread risk factors. This suggests that liquidity risk and credit risk are potentially common factors for different hedge fund strategies in the down-state of the market, when volatility is high and returns are very low. We further explore the possibility that all hedge fund strategies exhibit a high volatility regime of the idiosyncratic risk, which could be attributed to contagion among hedge fund strategies. In our sample this event happened only during the Long-Term Capital Management (LTCM) crisis of 1998. Other crises including the recent subprime mortgage crisis affected hedge funds only through systematic risk factors, and did not cause contagion among hedge funds. |
| Keywords: | Hedge Fund, Risk Management, High frequency data |
| JEL: | G12 G29 C51 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2008_10&r=mst |