Results 1  10
of
47
Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test
 REVIEW OF FINANCIAL STUDIES
, 1988
"... In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (19621985) and for all subperiod for a variety of aggrega ..."
Abstract

Cited by 226 (13 self)
 Add to MetaCart
In this article we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (19621985) and for all subperiod for a variety of aggregate returns indexes and sizesorted portofolios. Although the rejections are due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or timevarying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a meanreverting model of asset prices.
A critique of the application of unit root tests
 Journal of Economic Dynamics and Control
, 1991
"... This paper exploits the fact that any time series with a unit root can de decomposed into a stationary series and a random walk. Since the random walk component can have arbitrarily small variance, tests for unit roots or trend stationarity have arbitrarily low power in finite samples. Furthermore, ..."
Abstract

Cited by 28 (0 self)
 Add to MetaCart
This paper exploits the fact that any time series with a unit root can de decomposed into a stationary series and a random walk. Since the random walk component can have arbitrarily small variance, tests for unit roots or trend stationarity have arbitrarily low power in finite samples. Furthermore, there are unit root processes whose likelihood functions and autocorrelation functions are arbitrarily close to those of any given stationary processes and vice versa, so there are stationary and unit root processes for which the result of any inference is arbitrarily close in finite samples. 1.
Testing Black’s CAPM with possibly nonGaussian error distributions: An exact simulationbased approach
"... recherche sur la société et la culture (Québec), and the Fonds de recherche sur la nature et les technologies (Québec). This paper was also partly written at the Centre de recherche en Économie et Statistique (INSEE, Paris), the Technische Universität Dresden (Fakultät Wirtschaftswissenschaften) and ..."
Abstract

Cited by 13 (10 self)
 Add to MetaCart
recherche sur la société et la culture (Québec), and the Fonds de recherche sur la nature et les technologies (Québec). This paper was also partly written at the Centre de recherche en Économie et Statistique (INSEE, Paris), the Technische Universität Dresden (Fakultät Wirtschaftswissenschaften) and the Finance Division at the University of British Columbia. Centre interuniversitaire sur le risque, les politiques économiques et l’emploi (CIRPÉE), CIRANO, and Département de finance et assurance, Université Laval. Mailing address: Département de finance et assurance, Pavillon
Empirically Relevant Critical Values For Hypothesis Tests: A Bootstrap Approach
 Journal of Econometrics
, 1998
"... Tests of statistical hypotheses can be based on either of two critical values: the Type I critical value or the sizecorrected critical value. The former usually depends on unknown population parameters and cannot be evaluated exactly in applications, but it can often be estimated very accurately by ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
Tests of statistical hypotheses can be based on either of two critical values: the Type I critical value or the sizecorrected critical value. The former usually depends on unknown population parameters and cannot be evaluated exactly in applications, but it can often be estimated very accurately by using the bootstrap. The latter does not depend on unknown population parameters but is likely to yield a test with low power. The critical values used in most Monte Carlo studies of the powers of tests are neither Type I nor sizecorrected. They are irrelevant to empirical research. Key words: Hypothesis test, critical value, size, Type I error, bootstrap JEL classification: C12, C15 ___________________________________________________________________________ Corresponding author: Joel L. Horowitz, Department of Economics, University of Iowa, Iowa City, IA, 54442. Tel: (319) 3350844. Fax: (319) 3351956. Email: joelhorowitz @uiowa.edu. We thank Art Goldberger, Beth Ingram, Tom Rothenberg,...
Structural Breaks and LongRun Trends in Commodity Prices
"... This paper a product of the Macroeconomics and Growth Division, Policy Research Department m is part of a larger effort in the department to understand the links of foreign shocks and macroeconomic policies. Copies of the paper are available free from the World Bank, 1818 H Street lqW, Washington, ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
This paper a product of the Macroeconomics and Growth Division, Policy Research Department m is part of a larger effort in the department to understand the links of foreign shocks and macroeconomic policies. Copies of the paper are available free from the World Bank, 1818 H Street lqW, Washington, DC 20433. Please contact Raquel Luz, room N11043, extension 31320 (24 pages). January 1995
Maximizing Predictability in the
, 1991
"... We construct portfolios of stocks and of bonds that are maximally predictable with respect to a set of ex ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for datasnooping biases. We disaggregate the sources for p ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
We construct portfolios of stocks and of bonds that are maximally predictable with respect to a set of ex ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for datasnooping biases. We disaggregate the sources for predictability by using several asset groups, including sizesorted and industrysorted portfolios, and find that the sources of maximal predictability shift considerably across sectors and size classes as the returnhorizon changes. Using three outofsample measures of predictability, we show that the predictability of the maximally predictable portfolio is genuine and economically significant.
Bootstrapping the BoxPierce Q test: A robust test of uncorrelatedness
, 2003
"... This paper describes a test of the null hypothesis that the first K autocorrelations of a covariance stationary time series are zero in the presence of statistical dependence. The test is based on the Box Pierce Q statistic with bootstrapbased Pvalues. The bootstrap is implemented using a double ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
This paper describes a test of the null hypothesis that the first K autocorrelations of a covariance stationary time series are zero in the presence of statistical dependence. The test is based on the Box Pierce Q statistic with bootstrapbased Pvalues. The bootstrap is implemented using a double blocksofblocks procedure with prewhitening. The finite sample performance of the bootstrap Q test is investigated by simulation. In our experiments, the performance is satisfactory for samples of n = 500. At this sample size, the differences between the empirical and nominal rejection probabilities are essentially eliminated. KEY WORDS: Serial correlation tests; BoxPierce Q; blocks of blocks bootstrap, adjusted Pvalues, double bootstrap. 1 1.
Markov Chain Test for Time Dependence and Homogeneity: An Analytical and Empirical Evaluation
"... This paper presents a complete framework for the testing procedure based on statistical theory of Markov chains. First, based on this methodology, an analytical evaluation of the Markov representation of time series is undertaken. It is shown that there is a onetoone correspondence between the AR( ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
This paper presents a complete framework for the testing procedure based on statistical theory of Markov chains. First, based on this methodology, an analytical evaluation of the Markov representation of time series is undertaken. It is shown that there is a onetoone correspondence between the AR(1) parameter and the transition probability matrix of the Markov representation of that series. Using this result, we derived an analytical measure of the statistical power of the Markov chain test to detect structural break in the data. We later used Monte Carlo experiments to examine the finitesample properties of the Markov chain time dependence and homogeneity tests taking the circular dependence between the two into account. The results showed that under the null hypothesis of an i.i.d. random walk, the empirical size of the Markov chain time dependence test is close to its nominal value irrespective of the sample size and the number of states. Unlike the size calculations, sample size...
Testing meanvariance efficiency in CAPM with possibly nonGaussian errors: an exact simulationbased approach
, 2002
"... ..."
The Great Rebound, The Great Crash, and Persistence in British Stock Prices
, 2000
"... In this paper, we investigate the persistence of British stock returns over the period 196298 using the Variance Ratio (VR) test to check for shortrange dependence and the Modified Rescaled Range (MRS) test to check for longrange dependence. A central contribution of the paper is that we investig ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
In this paper, we investigate the persistence of British stock returns over the period 196298 using the Variance Ratio (VR) test to check for shortrange dependence and the Modified Rescaled Range (MRS) test to check for longrange dependence. A central contribution of the paper is that we investigate the role of the great rebound in stock prices in January 1975 and the crash of October 1987. These shocks, which together represent less than 1% of the data fundamentally alter the time series properties of the data, with extreme skewness, excess kurtosis, and ARCH present in the unadjusted data, but absent from much of the shockpurged data. The VR and MRS tests reveal relatively little evidence of persistence in the original data. However, the VR tests exhibit systematic and significant reversals of sign as between the original and the shockpurged data. It appears that stock prices in Britain persistently stayed away from the mean, and then reverted back towards it in just two excepti...