Valeri Voev’s research while affiliated with Aarhus University and other places

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.

In this article, we introduce a new method of forecasting large-dimensional covariance matrices by exploiting the theoretical and empirical potential of mixing forecasts derived from different information sets. The main theoretical contribution of the article is to find the conditions under which a mixed approach (MA) provides a smaller mean squared forecast error than a standard one. The conditions are general and do not rely on distributional assumptions of the forecasting errors or on any particular model specification. The empirical contribution of the article regards a comprehensive comparative exercise of the new approach against standard ones when forecasting the covariance matrix of a portfolio of thirty stocks. The implemented MA uses volatility forecasts computed from high-frequency-based models and correlation forecasts using realized-volatility-adjusted dynamic conditional correlation models. The MA always outperforms the standard methods computed from daily returns and performs equally well to the ones using high-frequency-based specifications, however at a lower computational cost. © The Author, 2014. Published by Oxford University Press. All rights reserved.

We introduce a multivariate GARCH model that utilizes and models realized measures of volatility and covolatility. The realized measures extract information contained in high-frequency data that is particularly beneficial during periods with variation in volatility and covolatility. Applying the model to market returns in conjunction with an individual asset yields a model for the conditional regression coefficient, known as the beta. We apply the model to a set of highly liquid stocks and find that conditional betas are much more variable than usually observed with rolling-window OLS regressions with dailty data. In the empirical part of the paper we examine the cross-sectional as well as the time variation of the conditional beta series. The model links the conditional and realized second moment measures in a self-contained system of equations, making it amenable to extensions and easy to estimate. A multi-factor extension of the model is briefly discussed.

In this paper we provide a simple framework for the estimation of the variance-covariance matrix in the presence of MMS noise and non-synchronous trading. To accomplish that we start from the formula of the realized variance and the Hayashi-Yoshida realized covariance estimator and derive two separate pooled OLS regressions whose byproducts are the intergrated variance and covariance, respectively. An comprehensive simulation study shows that the least square approach gives rise to very precise estimators for all elements of the covariation matrix and outperforms other widely applied estimation techniques. A similar picture emerges when we use historical high frequency data.

In this article, we introduce a new method of forecasting large-dimensional covariance matrices by exploiting the theoretical and empirical potential of mixing forecasts derived from different information sets. The main theoretical contribution of the article is to find the conditions under which a mixed approach (MA) provides a smaller mean squared forecast error than a standard one. The conditions are general and do not rely on distributional assumptions of the forecasting errors or on any particular model specification. The empirical contribution of the article regards a comprehensive comparative exercise of the new approach against standard ones when forecasting the covariance matrix of a portfolio of thirty stocks. The implemented MA uses volatility forecasts computed from high-frequency-based models and correlation forecasts using realized-volatility-adjusted dynamic conditional correlation models. The MA always outperforms the standard methods computed from daily returns and performs equally well to the ones using high-frequency-based specifications, however at a lower computational cost.

We develop a panel intensity framework for the analysis of complex trading activity datasets containing detailed information on individual trading actions in different securities for a set of investors. A feature of the model is the presence of a time-varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. We contribute to the literature on market microstructure and behavioral finance by providing new results on the disposition effect and on the manifestation of risk aversion on the high-frequency trading level. These novel insights are made possible by the joint characterization of not only the decision to close (exit) a position, usually considered in isolation in the literature, but also the decision to open (enter) a position, which together describe the trading process in its entirety. While the disposition effect is defined with respect to the willingness to realize profits/losses with respect to the performance of the position under consideration, we find that the performance of the total portfolio of positions is an additional factor influencing trading decisions that can reinforce or dampen the standard disposition effect. Moreover, the proposed methodology allows the investigation of the strength of these effects for different groups of investors ranging from small retail investors to professional and institutional investors.

This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical application of the model, which shows that it outperforms other approaches in the extant literature, both in terms of statistical precision as well as in terms of providing a superior mean-variance trade-off in a classical investment decision setting. Copyright © 2010 John Wiley & Sons, Ltd.

The expected value of sums of squared intraday returns (realized variance) gives rise to a least squares regression which adapts itself to the assumptions of the noise process and allows for joint inference on integrated volatility (IV), noise moments and price-noise relations. In the iid noise case, we derive the asymptotic variance of the IV and noise variance estimators and show that they are consistent. The joint estimation approach is particularly attractive as it reveals important characteristics of the noise process which can be related to liquidity and market efficiency. The analysis of dependence between the price and noise processes provides an often missing link to market microstructure theory. We find substantial differences in the noise characteristics of trade and quote data arising from the effect of distinct market microstructure frictions.