Intraday Price Formation in U.S. Equity Index Markets

We introduce a multivariate GARCH model that incorporates realized measures of volatility and covolatility. The realized measures extract information about the current level of volatility and covolatility from high-frequency data, which is particularly useful for the modeling of return volatility during periods with rapid changes in volatility and covolatility. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than is usually found with rolling-window regressions based exclusively on daily returns. In the empirical part of the paper we examine the cross-sectional as well as the time variation of the conditional beta series during the financial crises.

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Realized Beta GARCH: A Multivariate GARCH model with realized measures of volatility and covolatility

We investigate how individual equity prices respond to continuous and jumpy market price moves and how these different market price risks, or betas, are priced in the cross section of expected stock returns. Based on a novel high-frequency data set of almost one thousand stocks over two decades, we find that the two rough betas associated with intraday discontinuous and overnight returns entail significant risk premiums, while the intraday continuous beta does not. These higher risk premiums for the discontinuous and overnight market betas remain significant after controlling for a long list of other firm characteristics and explanatory variables.

/pdf/roughing-up-beta-continuous-versus-discontinuous-betas-and-g8l8nh7k2k.pdf

Roughing up Beta: Continuous versus Discontinuous Betas and the Cross Section of Expected Stock Returns

https://www.econstor.eu/bitstream/10419/125827/1/84569734X.pdf

Modeling and forecasting exchange rate volatility in time-frequency domain

https://econ.au.dk/fileadmin/site_files/filer_oekonomi/Working_Papers/CREATES/2014/rp14_48.pdf

Roughing up beta: Continuous versus discontinuous betas and the cross section of expected stock returns

Using data on more than 750 million futures trades during 2004-2013, we analyze eight stylized facts of commodity price and volatility dynamics in the post financialization period. We pay particular attention to the factor structure in returns and volatility and to commodity market integration with the equity market. We find evidence of a factor structure in daily commodity futures returns. However, the factor structure in daily commodity futures volatility is even stronger than in returns. When computing model-free realized commodity betas with the stock market we find that they were high during 2008-2010 but have since returned to the pre-crisis level close to zero. The common factor in commodity volatility is nevertheless clearly related to stock market volatility. We conclude that, while commodity markets appear to again be segmented from the equity market when only returns are considered, commodity volatility indicates a nontrivial degree of market integration.

Factor Structure in Commodity Futures Return and Volatility

/pdf/factor-structure-in-commodity-futures-return-and-volatility-16ogvangew.pdf

We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized measures of volatility. An influential bias in these measures is documented, which motivates the use of a flexible and robust methodology such as the Realized GARCH. Within this framework, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit can be obtained in-sample by utilizing high-frequency data compared to standard models that use only daily observations. Additionally, we show that the intraday sampling frequency and method have significant implications for model fit in-sample. Finally, we consider an extensive out-of-sample exercise to forecast the conditional return distribution. The out-of-sample results for the Realized GARCH forecasts suggest a limited added value from using “traditional” realized volatility measures. For the conditional variance, a small gain is found, but for densities the opposite is the case. We conclude that realized measures of volatility developed in recent years must be used with caution in this market, and importantly that the use of high-frequency financial data in this market leaves much room for future research.

/pdf/modeling-and-forecasting-the-distribution-of-energy-forward-1kyoc0cuek.pdf

Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

We introduce a simple and intuitive approach of modeling and forecasting correlations for use in portfolio optimization. The model is composite in nature and consists of elements based on a bivariate realized volatility model. Importantly, our framework allows for volatility spill-overs between assets which provide an edge compared to competing models when forming portfolios. We apply the model to high-frequency data for commodity markets and demonstrate significant economic gains for an investor basing portfolio decisions on our modeling framework. This gain is significant in economic terms, even after imposing realistic constraints on short selling and portfolio turnover.

Kasper V. Olesen

Papers

Factor Structure in Commodity Futures Return and Volatility

Factor Structure in Commodity Futures Return and Volatility

Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

Realizing Correlations Across Asset Classes