Time-Variation in Expected Returns

Kaul, Gautam

doi:10.1086/296441

Cited by 385 publications

(184 citation statements)

References 22 publications

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“…Possible reasons for autocorrelation in the returns are nonsynchronous trading (see Scholes and Williams, 1977;Fisher, 1966) and timevarying short term expected returns (Conrad and Kaul, 1988).…”

Section: Econometric Methodologymentioning

confidence: 99%

Modelling stock returns in Africa's emerging equity markets

Alagidede

Panagiotidis

2009

International Review of Financial Analysis

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We investigate the behaviour of stock returns in Africa's largest markets namely, Egypt, Kenya, Morocco, Nigeria, South Africa, Tunisia and Zimbabwe.The validity of the random walk hypothesis is examined and rejected by employing a battery of tests. Secondly we employ smooth transition and conditional volatility models to uncover the dynamics of the first two moments and examine weak from efficiency. The empirical stylized facts of volatility clustering, leptokurtosis and leverage effect are present in the African data.

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Section: Econometric Methodologymentioning

confidence: 99%

Modelling stock returns in Africa's emerging equity markets

Alagidede

Panagiotidis

2009

International Review of Financial Analysis

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“…The study of serial correlation of asset returns is particularly important in financial economics, since it can reveal basic features of the trading process. The Efficient Market Hypothesis in its weakest form implies that asset returns should be serially uncorrelated, but there is pervasive evidence of serial autocorrelation in stock index returns (Lo and MacKinlay, 1988;Poterba and Summers, 1988) and stock portfolios (Conrad and Kaul, 1988;Mech, 1993), mixed evidence on stocks (Lo and MacKinlay, 1990b;Conrad and Kaul, 1989;Kim et al, 1991) and international assets (Patro and Wu, 2004), mainly depending on volumes and size (Llorente et al, 2002), while stock index futures display no autocorrelation, see Ahn et al (2002) and Pan et al (1997) for currency futures. There are many theoretical models and explanations, on one side to reconcile the presence of serial correlation with a rational framework, e.g.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of intraday serial correlation in the Italian futures market

Bianco

Renò

2005

Journal of Futures Markets

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We study the serial correlation of high-frequency intraday returns on the Italian stock index futures (FIB30) in the period 2000-2002. We find that intraday autocorrelation is mostly negative for time scales lower than 20 minutes, mainly due to the bid-ask bounce effect. While this supports the efficiency of the Italian futures market, we also provide evidence that intraday serial correlation becomes positive in high volatility regimes. Moreover we find that it is mainly unexpected volatility to make serial correlation rise, and not its predictable part. Our results are supportive of the Chan (1993) model.

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“…On this basis, we use market capitalization and economic freedom as proxies for the economic fundamentals which control the degree of market microstructure factors, also paying attention to the variation of the VR estimates over time. Conrad and Kaul (1988) argue that autocorrelation in stock returns represents time variation of expected returns. However, their model depends heavily on its parametric structure and their result may not be robust to different model specifications.…”

Section: Variance Ratio As a Measure Of Stock Market Efficiencymentioning

confidence: 99%