Rady School of Management, University of Californi
9500 Gilman Drive
MC0553, La Jolla
Institutional Affiliation: University of California at San Diego
Information about this author at RePEc
NBER Working Papers and Publications
|December 2004||Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns|
with Michael W. Brandt, Pedro Santa-Clara: w10996
We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple, easily modified and extended, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. Our approach also provides a new test of the ...
Published: Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2009. "Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 22(9), pages 3411-3447, September. citation courtesy of
|November 2004||Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies|
with Eric Ghysels, Pedro Santa-Clara: w10914
We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequen...
Published: Ghysels, Eric, Pedro Santa-Clara and Rossen Valkanov. "Predicting Volatility: Getting The Most Our Of Return Data Sampled At Different Frequencies," Journal of Econometrics, 2006, v131(1-2,Mar-Apr), 59-95. citation courtesy of
|There is a Risk-Return Tradeoff After All|
with Eric Ghysels, Pedro Santa-Clara: w10913
This paper studies the ICAPM intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns -- the Mixed Data Sampling (or MIDAS) approach. Using MIDAS, we find that there is a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process, and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the ICAPM based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional varia...
Published: Ghysels, Eric, Pedro Santa-Clara and Rossen Valkanov. "There is a risk-return tradeoff after all." Journal of Financial Economics 76 (June 2005): 509-548. citation courtesy of