Open AccessEvaluating factor pricing models using high‐frequency panels
Author(s) -
Chang Yoosoon,
Choi Yongok,
Kim Hwagyun,
Park Joon Y.
Publication year - 2016
Publication title -
quantitative economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.062
H-Index - 27
eISSN - 1759-7331
pISSN - 1759-7323
DOI - 10.3982/qe251
Subject(s) - econometrics , factor analysis , leverage (statistics) , stock (firearms) , volatility (finance) , multivariate statistics , realized variance , regression , explanatory power , computer science , stochastic volatility , capital asset pricing model , economics , statistics , mathematics , engineering , mechanical engineering , philosophy , epistemology
This paper develops a new framework and statistical tools to analyze stock returns using high‐frequency data. We consider a continuous‐time multifactor model via a continuous‐time multivariate regression model incorporating realistic empirical features, such as persistent stochastic volatilities with leverage effects. We find that the conventional regression approach often leads to misleading and inconsistent test results when applied to high‐frequency data. We overcome this by using samples collected at random intervals, which are set by the clock running inversely proportional to the market volatility. Our results show that the conventional pricing factors have difficulty in explaining the cross section of stock returns. In particular, we find that the size factor performs poorly in fitting the size‐based portfolios, and the returns on the consumer industry have some explanatory power on the small growth stocks.