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Esben Hedegaard

Visiting Assistant Professor of Finance
Joined W.P. Carey 2012


Cell: (646) 334-8953
Email:  esben.hedegaard@asu.edu

W. P. Carey School of Business
Arizona State University
400 E. Lemon Street, BAC 518
Tempe, AZ 85287-3906

Curriculum Vitae

Research

Measuring the Risk-Return Tradeoff with Time-Varying Conditional Covariances (Job Market Paper)
With Bob Hodrick
Abstract: We use panel data to examine the prediction of Merton's intertemporal CAPM that time varying risk premiums arise from the conditional covariances of returns on assets with the return on the market. We find a positive and significant risk-return tradeoff that is driven by the time series variation in the conditional covariances, and the risk-premium on the market remains positive and significant after controlling for additional state-variables. Our estimation method allows us to estimate the risk-return tradeoff in the ICAPM using a large number of test assets.

Causes and Consequences of Margin Levels in Futures Markets
Abstract: This paper examines how margin requirements in futures markets are set and how they affect prices an liquidity. Margin increases cause both hedgers and speculators to reduce their open positions, and a margin increase for one contract causes funding liquidity spillovers that affect price impact for all contracts. I do not find a significant effect of margin changes on the futures price level. However, realized variance increases by 50% on average on the day of a margin increase and remains high in the following weeks. The findings imply that imposing higher margins adversely affects liquidity and volatility, and that regulation of margins can make trading more costly for all market participants.

Estimating the Conditional CAPM with Overlapping Data Inference
With Bob Hodrick
Abstract: Asset pricing models such as the conditional CAPM are typically estimated with MLE using a monthly or quarterly horizon with data sampled to match the horizon even though daily data are available. We develop an overlapping data inference methodology (ODIN) that uses all of the data while maintaining the monthly or quarterly forecasting period, and we apply it to the conditional CAPM. Our approach recognizes that the first order conditions of MLE can be used as orthogonality conditions of GMM. Using historical data, we find considerable differences in the estimates from the non-overlapping samples that begin on different days.