Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise

This paper introduces a new class of generalized at-top realized kernels for estimation of quadratic variation in the presence of market microstructure noise that is allowed to exhibit a non-trivial dependence structure and to be correlated with the ecient price process. The estimators in this class are shown to be consistent, asymptotically unbiased, and mixed gaussian with an optimal n^(1/4)-convergence rate. In addition, an ecient and asymptotically normal estimator of the long run variance of the market microstructure noise is provided along with novel and consistent estimators of the asymptotic variance of the at-top realized kernels and of the integrated quarticity, respectively, creating a powerful, unied framework for analyzing quadratic variation. A nite sample correction ensures non-negativity of the at-top realized kernels without a ecting asymptotic properties. Lastly, in an extensive simulation study, important practical issues such as the choice of kernel function and tuning parameters are addressed, the adequacy of the asymptotic distribution in nite samples is assessed, and it is shown that estimators in this class exhibit a superior bias and root mean squared error tradeo relative to competing estimators. The impact of using various realized estimators is illustrated in a small empirical application to noisy high frequency stock market data.Bias Reduction, Nonparametric Estimation, Market Microstructure Noise, Quadratic Variation.

The Role of Dynamic Specification in Forecasting Volatility in the Presence of Jumps and Noisy High-Frequency Data

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.ARFIMA, HAR, Implied Volatility, Jumps, Market Microstructure Noise, VecARFIMA, Volatility Forecasting

The Role of Realized Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts

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.Forecast evaluation, Volatility forecasting, Portfolio optimization, Mean-variance analysis.

Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns

We propose a parametric state space model with accompanying estimation and forecasting framework that combines long memory and level shifts by decomposing the underlying process into a simple mixture model and ARFIMA dynamics. The Kalman filter is used to construct the likelihood function after augmenting the probability of states by a mixture of normally distributed processes. The forecasts are constructed by exploiting the information in the Kalman recursions. The validity of the estimation methodology is shown through a comprehensive simulation study. Besides being able to identify the true memory of a process, the model consistently belongs to the 10% Model Confidence Set when considering out-of-sample forecasting performance as the only one among four competing dynamic models for all forecasting horizons when applied to high frequency stock- and bond market data together with time series of daily returns on stock market and exchange rate data. As a by-product, we provide simulation and empirical evidence of the "Spurious Break" phenomenon when estimating the number of level shifts in structural models for I(d) processes.Forecasting, Kalman Filter, Long Memory Processes, State Space Modeling, Structural Change.

Option characteristics as cross-sectional predictors

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant predictive power, even after controlling for firm characteristics, earning a Fama-French three-factor alpha in excess of 20% per annum. Our analysis further reveals that the strongest option characteristics are associated with information about asset mispricing and future tail return realizations. Our findings are consistent with models of informed trading and limits to arbitrage