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| Author | : Ryohei Oishi |
| Publisher | : |
| Total Pages | : 46 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
This study proposes a multivariate test for linear factor asset pricing models when the number of assets, N, is larger than the time dimension of returns, T. We extend the exact test proposed by Gibbons et al. (1989) to obtain a nonsingular covariance matrix with fewer estimation errors in the case of T
| Author | : M. Hashem Pesaran |
| Publisher | : |
| Total Pages | : 100 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
This paper proposes a novel test of zero pricing errors for the linear factor pricing model when the number of securities, N, can be large relative to the time dimension, T, of the return series. The test is based on Student t tests of individual securities and has a number of advantages over the existing standardised Wald type tests. It allows for non-Gaussianity and general forms of weakly cross correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5;000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe-Lintner CAPM and Fama-French three factor models are found mainly during the recent financial crisis. Also we find a significant negative correlation between a twelve-months moving average p-values of the test and excess returns of long/short equity strategies (relative to the return on S&P 500) over the period November 1994 to June 2015, suggesting that abnormal profits are earned during episodes of market inefficiencies.
| Author | : Soosung Hwang |
| Publisher | : |
| Total Pages | : 41 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
We propose the average F statistic for testing linear asset pricing models. The average pricing error, captured in the the statistic, is of more interest than the ex post maximum pricing error of the multivariate F statistic that is associated with extreme long and short positions and excessively sensitive to small perturbations in the estimates of asset means and covariances. The average F test can be applied to thousands of individual stocks and thus is free from the information loss or the data snooping biases from grouping. This test is robust to ellipticity, and more importantly, our simulation and bootstrapping results show that the power of average F test continues to increase as the number of stocks increases. Empirical tests using individual stocks from 1967 to 2006 demonstrate that the popular four factor model (i.e. Fama-French three factors and momentum) is rejected two sub-periods from from 1967 to 1971 and from 1982 to 1986.
| Author | : Paul Söderlind |
| Publisher | : |
| Total Pages | : 14 |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
A GMM-based system for two different linear factor pricing models is used to test if the pricing errors are the same. Simulations demonstrate the small sample properties. As an illustration, the test is applied to the Fama-French (1996, 2015) models.
| Author | : Andrew Wen-Chuan Lo |
| Publisher | : |
| Total Pages | : 60 |
| Release | : 1989 |
| Genre | : Economics |
| ISBN | : |
We investigate the extent to which tests of financial asset pricing models may be biased by using properties of the data to construct the test statistics. Specifically, we focus on tests using returns to portfolios of common stock where portfolios are constructed by sorting on some empirically motivated characteristic of the securities such as market value of equity. We present both analytical calculations and Monte Carlo simulations that show the effects of this type of data-snooping to be substantial. Even when the sorting characteristic is only marginally correlated with individual security statistics, 5 percent tests based on sorted portfolio returns may reject with probability one under the null hypothesis. This bias is shown to worsen as the number of securities increases given a fixed number of portfolios, and as the number of portfolios decreases given a fixed number of securities. We provide an empirical example that illustrates the practical relevance of these biases.
| Author | : John Knight |
| Publisher | : Elsevier |
| Total Pages | : 298 |
| Release | : 2004-12-01 |
| Genre | : Business & Economics |
| ISBN | : 0080455328 |
The determination of the values of stocks, bonds, options, futures, and derivatives is done by the scientific process of asset pricing, which has developed dramatically in the last few years due to advances in financial theory and econometrics. This book covers the science of asset pricing by concentrating on the most widely used modelling technique called: Linear Factor Modelling.Linear Factor Models covers an important area for Quantitative Analysts/Investment Managers who are developing Quantitative Investment Strategies. Linear factor models (LFM) are part of modern investment processes that include asset valuation, portfolio theory and applications, linear factor models and applications, dynamic asset allocation strategies, portfolio performance measurement, risk management, international perspectives, and the use of derivatives. The book develops the building blocks for one of the most important theories of asset pricing - Linear Factor Modelling. Within this framework, we can include other asset pricing theories such as the Capital Asset Pricing Model (CAPM), arbitrage pricing theory and various pricing formulae for derivatives and option prices. As a bare minimum, the reader of this book must have a working knowledge of basic calculus, simple optimisation and elementary statistics. In particular, the reader must be comfortable with the algebraic manipulation of means, variances (and covariances) of linear combination(s) of random variables. Some topics may require a greater mathematical sophistication.* Covers the latest methods in this area.* Combines actual quantitative finance experience with analytical research rigour* Written by both quantitative analysts and academics who work in this area
