Range-based covariance estimation using high-frequency data: The realized co-range

We introduce the realized co-range, utilizing intraday high-lowprice ranges to estimate asset return covariances. Using simulationswe find that for plausible levels of bid-ask bounce and infrequentand non-synchronous trading the realized co-range improves upon therealized covariance, which uses cross-products of intraday returns.One advantage of the co-range is that in an ideal world it is fivetimes more efficient than the realized covariance when sampling atthe same frequency. The second advantage is that the upward bias dueto bid-ask bounce and the downward bias due to infrequent andnon-synchronous trading partially offset each other. In a volatilitytiming strategy for S\\&P500, bond and gold futures we find that theco-range estimates are less noisy as exemplified by lowertransaction costs and also higher Sharpe ratios when using moreweight on recent data for predicting covariances.bias-correction;market microstructure noise;high-frequency date;realized co-range;realized covariance

Measuring and Forecasting Financial Market Volatility using High-Frequency Data

This dissertation consists of three studies on the use of intraday asset price data for accurate measurement and forecasting of financial market volatility. Chapter 2 proposes a refined heuristic bias-correction for the two time scales realized range-based volatility estimator in the presence of bid-ask bounce and non-trading. The merits are illustrated through simulations and an empirical forecasting application. Chapter 3 introduces a novel approach for estimating the covariance between asset returns using intraday high-low price ranges. The realized co-range estimator compares favourably to the realized covariance for plausible levels of microstructure noise and non-synchronous trading. The estimator is successfully implemented in a volatility timing strategy that deals with constructing mean-variance efficient asset allocation portfolios from stock, bond and gold futures. Chapter 4 introduces a mixed-frequency factor model for vast-dimensional covariance estimation. This original approach combines the use of high- and low-frequency data with a linear factor structure. We propose the use of highly liquid ETFs -- that are essentially free of microstructure frictions -- as factors such that factor covariances can be estimated with high precision from ultra-high-frequency data. The factor loadings are estimated from low-frequency data to bypass the potentially severe impacts of noise for individual stocks and to circumvent non-synchronicity issues between returns on stocks and liquid factors. Theoretical, simulation and empirical results illustrate that the mixed-frequency factor model is excellent, both compared to low-frequency factor models and to popular realized covariance estimators based on high-frequency data

Forecasting Volatility with the Realized Range in the Presence of Noise and Non-Trading

We introduce a heuristic bias-adjustment for the transaction price-based realized range estimator of daily volatility in the presence of bid-ask bounce and non-trading. The adjustment is an extension of the estimator proposed in Christensen et al. (2009). We relax the assumption that all intra-day high (low) transaction prices are at the ask (bid) quote. Using data-based simulations we obtain estimates of the probability that a given intraday range is (upward or downward) biased or not, which we use for a more refined bias-adjustment of the realized range estimator. Both Monte Carlo simulations and an empirical application involving a liquid and a relatively illiquid S&P500 constituent demonstrate that ex post measures and ex ante forecasts based on the heuristically adjusted realized range compare favorably to existing bias-adjusted (two time scales) realized range and (two time scales) realized variance estimators

Range-based covariance estimation using high-frequency data: The realized co-range

We introduce the realized co-range, utilizing intraday high-low price ranges to estimate asset return covariances. Using simulations we find that for plausible levels of bid-ask bounce and infrequent and non-synchronous trading the realized co-range improves upon the realized covariance, which uses cross-products of intraday returns. One advantage of the co-range is that in an ideal world it is five times more efficient than the realized covariance when sampling at the same frequency. The second advantage is that the upward bias due to bid-ask bounce and the downward bias due to infrequent and non-synchronous trading partially offset each other. In a volatility timing strategy for S\\&P500, bond and gold futures we find that the co-range estimates are less noisy as exemplified by lower transaction costs and also higher Sharpe ratios when using more weight on recent data for predicting covariances

Realized mixed-frequency factor models for vast dimensional covariance estimation

We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast dimensional covari- ance matrices of asset returns. The MFFM uses high-frequency (intraday) data to estimate factor (co)variances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. We propose the use of highly liquid assets such as exchange traded funds (ETFs) as factors. Prices for these contracts are observed essentially free of microstructure noise at high frequencies, allowing us to obtain precise estimates of the factor covariances. The factor loadings instead are estimated from daily data to avoid biases due to market microstructure effects such as the relative illiquidity of individual stocks and non-synchronicity between the returns on factors and stocks. Our theoretical, simulation and empirical results illustrate that the performance of the MFFM is excellent, both compared to conventional factor models based solely on low-frequency data and to popular realized covariance estimators based on high-frequency data

The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown