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on Market Microstructure |
| By: | Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg |
| Abstract: | We propose two new jump-robust estimators of integrated variance based on high-frequency return observations. These MinRV and MedRV estimators provide an attractive alternative to the prevailing bipower and multipower variation measures. Specifically, the MedRV estimator has better theoretical efficiency properties than the tripower variation measure and displays better finite-sample robustness to both jumps and the occurrence of “zero” returns in the sample. Unlike the bipower variation measure, the new estimators allow for the development of an asymptotic limit theory in the presence of jumps. Finally, they retain the local nature associated with the low-order multipower variation measures. This proves essential for alleviating finite sample biases arising from the pronounced intraday volatility pattern that afflicts alternative jump-robust estimators based on longer blocks of returns. An empirical investigation of the Dow Jones 30 stocks and an extensive simulation study corroborate the robustness and efficiency properties of the new estimators. |
| Keywords: | Stocks - Rate of return ; Stock market ; Stock - Prices |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:465&r=mst |
| By: | Naoto Kunitomo (Faculty of Economics, University of Tokyo); Seisho Sato (Institute of Statistical Mathematics) |
| Abstract: | For estimating the realized volatility and covariance by using high frequency data, we have introduced the Separating Information Maximum Likelihood (SIML) method when there are possibly micro-market noises by Kunitomo and Sato (2008a, 2008b, 2010a, 2010b). The resulting estimator is simple and it has the representation as a specific quadratic form of returns. We show that the SIML estimator has reasonable asymptotic properties; it is consistent and it has the asymptotic normality (or the stable convergence in the general case) when the sample size is large under general conditions including some non-Gaussian processes and some volatility models. Based on simulations, we find that the SIML estimator has reasonable finite sample properties and thus it would be useful for practice. The SIML estimator has the asymptotic robustness properties in the sense it is consistent when the noise terms are weakly dependent and they are endogenously correlated with the efficient market price process. We also apply our method to an analysis of Nikkei-225 Futures, which has been the major stock index in the Japanese financial sector. |
| Date: | 2010–08 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2010cf758&r=mst |
| By: | Rasmus Tangsgaard Varneskov (School of Economics and Management, Aarhus University and CREATES); Valeri Voev (School of Economics and Management, Aarhus University and CREATES) |
| Abstract: | Recently, consistent measures of the ex-post covariation of financial assets based on noisy high-frequency data have been proposed. A related strand of literature focuses on dynamic models and covariance forecasting for high-frequency data based covariance measures. The aim of this paper is to investigate whether more sophisticated estimation approaches lead to more precise covariance forecasts, both in a statistical precision sense and in terms of economic value. A further issue we address, is the relative importance of the quality of the realized measure as an input in a given forecasting model vs. the model’s dynamic specification. The main finding is that the largest gains result from switching from daily to high-frequency data. Further gains are achieved if a simple sparsesampling covariance measure is replaced with a more efficient and noise-robust estimator. |
| Keywords: | Forecast evaluation, Volatility forecasting, Portfolio optimization, Mean-variance analysis. |
| JEL: | C32 C53 G11 |
| Date: | 2010–08–26 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-45&r=mst |
| By: | Rasmus Tangsgaard Varneskov (School of Economics and Management, Aarhus University and CREATES) |
| Abstract: | This paper considers the performance of di erent long-memory dynamic models when forecasting volatility in the stock market using implied volatility as an exogenous variable in the information set. Observed volatility is sep- arated into its continuous and jump components in a framework that allows for consistent estimation in the presence of market microstructure noise. A comparison between a class of HAR- and ARFIMA models is facilitated on the basis of out-of-sample forecasting performance. Implied volatility conveys incremental information about future volatility in both specifications, improv- ing performance both in- and out-of-sample for all models. Furthermore, the ARFIMA class of models dominates the HAR specications in terms of out-of- sample performance both with and without implied volatility in the information set. A vectorized ARFIMA (vecARFIMA) model is introduced to control for possible endogeneity issues. This model is compared to a vecHAR specication, re-enforcing the results from the single equation framework. |
| Keywords: | ARFIMA, HAR, Implied Volatility, Jumps, Market Microstructure Noise, VecARFIMA, Volatility Forecasting |
| JEL: | C14 C22 C32 C53 G10 |
| Date: | 2010–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2010-39&r=mst |