NBER Working Papers by Eric Ghysels

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Working Papers

December 2009Momentum Cycles and Limits to Arbitrage Evidence from Victorian England and Post-Depression US Stock Markets
with Benjamin Chabot, Ravi Jagannathan: w15591
We evaluate the importance of “Limits to Arbitrage” to explain profitability of momentum strategies. Specifically, when the availability of arbitrage capital is in short supply, momentum cycles last longer, and breaks in momentum cycles are shorter. We demonstrate the robustness of our findings with a unique database of stock returns from1866-1907 London and the CRSP database. Momentum cycle durations are similar in both databases and all other momentum facts documented in the literature using the CRSP database hold for the Victorian period as well, except for the January reversal due to the absence of capital gains taxation.
November 2008Price Momentum In Stocks: Insights From Victorian Age Data
with Benjamin Chabot, Ravi Jagannathan: w14500
We find that price momentum in stocks was a pervasive phenomenon during the Victorian age (1866-1907) as well. Momentum strategy profits have little systematic risk even at business cycle frequencies; disappear periodically only to reappear later; exhibit long run reversal; and are higher following up markets, suggesting limited availability of arbitrage capital relative to opportunities during those times. Since there were no capital gains taxes during the Victorian age, the long run reversal of momentum profits must have a fundamental component, that is unrelated to tax based trading, identified by Grinblatt and Moskowitz (2004) using CRSP era data.
November 2004There is a Risk-Return Tradeoff After All
with Pedro Santa-Clara, Rossen Valkanov: 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...
Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies
with Pedro Santa-Clara, Rossen Valkanov: 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...

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