Variance‐ratio Tests of Random Walk: An Overview

Charles, Amélie; Darné, Olivier

doi:10.1111/j.1467-6419.2008.00570.x

Cited by 134 publications

(101 citation statements)

References 82 publications

(225 reference statements)

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“…With the variance ratio calculated, the test statistic is then calculated using Lo and MacKinlay's heteroscedasticity method (Charles & Darné, 2003) (Equation 4). Heteroscedasticity is needed because the assumption that are independently and identically distributed is not required for this test.…”

Section: Discussionmentioning

confidence: 99%

“…The variance ratio test has been primarily used to test the random walk hypothesis for market efficiencies in finance (Charles & Darné, 2003). The test is particularly useful for testing if the process eventually returns to the average (mean reversion) (Charles & Darné, 2003). The random walk model was first introduced in 1828 when the botanist Brown described his Brownian motion.…”

Section: Introductionmentioning

confidence: 99%

“…The variance ratio test investigates the "random fluctuations" of a time series dataset and tests if changes in the time series are statistically independent or if these changes are correlated with one another. The variance ratio test has been primarily used to test the random walk hypothesis for market efficiencies in finance (Charles & Darné, 2003). The test is particularly useful for testing if the process eventually returns to the average (mean reversion) (Charles & Darné, 2003).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Not So-Random Walk with Wind: Evaluating Wind Velocity Update Methods in Ground Based Spray Deposition Models

Schramm¹,

Hanna²,

Darr³

et al. 2016

2016 ASABE International Meeting

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The notion that wind speed and direction can be approximated by adding a random fluctuation to the previous value was investigated. The data were recorded at one meter above a field to simulate conditions that are present at a ground sprayer's boom. Variance ratio tests were carried out to test the null hypothesis that wind possesses similar properties to a random walk versus the alternative that wind does not. More specifically, are the random fluctuations auto correlated with one another in time? This process was done to a 10Hz sample and averages of the measured wind data at 0.5, 1, 5, 10, 30, 60, 300, and 600 seconds. It was found that for all tests, except for the 300 and 600 second data samples, the null hypothesis was rejected at greater than 99.9% certainty. This indicates that there is evidence of autocorrelation (rather than randomness) in the measurements of wind speed and direction, associated with each other in time.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Not So-Random Walk with Wind: Evaluating Wind Velocity Update Methods in Ground Based Spray Deposition Models

Schramm¹,

Hanna²,

Darr³

et al. 2016

2016 ASABE International Meeting

View full text Add to dashboard Cite

show abstract

“…This assumption is not necessary for the distribution theory; imposing 9 Some papers including Whang and Kim (2003) dispense with this latter assumption but maintain the mixing and moment assumption.…”

Section: Regularity Conditionsmentioning

confidence: 99%

“…Most treatments of variance ratios follow the Lo and MacKinlay (1988) assumption H, which includes a mixing condition and some further restriction on the structure of the higher moments (their condition H4), which purportedly implies that the sample autocorrelations are asymptotically independent. 9 In the multivariate context, their assumption H4 would be that…”

Section: Regularity Conditionsmentioning

confidence: 99%

An investigation into multivariate variance ratio statistics and their application to stock market predictability

Hong¹,

Linton²,

Zhang³

2015

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We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form E¢ cient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a time varying risk premium through common systematic factors. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to …ve weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. We …nd evidence of a reduction of linear predictability in the most recent hjz20@cam.ac.uk 1 period, for small and medium cap stocks. The main …ndings are not substantially a¤ected by allowing for a common factor time varying risk premium.

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Does geographic location matter to stock return predictability?

Boubaker

Chkir

Chourou

et al. 2018

Journal of Forecasting

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Building on recent and growing evidence that geographic location influences information diffusion, this paper examines the relation between firm's location and the predictability of stock returns. We hypothesize that returns on a portfolio composed of firms located in central areas are more likely to follow a random walk than returns on a portfolio composed of firms located in remote areas. Using a battery of variance ratio tests, we find strong and robust support for our prediction. In particular, we show that the returns on a portfolio composed of the 500 largest urban firms follow a random walk; however, all variance ratio tests reject the random walk hypothesis for a portfolio that includes the 500 largest rural firms. Our results are robust to alternative definitions of firm's location and portfolio formation.

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Variance‐ratio Tests of Random Walk: An Overview

Cited by 134 publications

References 82 publications

A Not So-Random Walk with Wind: Evaluating Wind Velocity Update Methods in Ground Based Spray Deposition Models

A Not So-Random Walk with Wind: Evaluating Wind Velocity Update Methods in Ground Based Spray Deposition Models

An investigation into multivariate variance ratio statistics and their application to stock market predictability

Does geographic location matter to stock return predictability?

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