Constructing weekly returns based on daily stock market data: A puzzle for empirical research?

Baumöhl, Eduard and Lyócsa, Štefan (2012): Constructing weekly returns based on daily stock market data: A puzzle for empirical research?

Abstract

The weekly returns of equities are commonly used in the empirical research to avoid the non-synchronicity of daily data. An empirical analysis is used to show that the statistical properties of a weekly stock returns series strongly depend on the method used to construct this series. Three types of weekly returns construction are considered: (i) Wednesday-to-Wednesday, (ii) Friday-to-Friday, and (iii) averaging daily observations within the corresponding week. Considerable distinctions are found between these procedures using data from the S&P500 and DAX stock market indices. Differences occurred in the unit-root tests, identified volatility breaks, unconditional correlations, ARMA-GARCH and DCC MV-GARCH models as well. Our findings provide evidence that the method employed for constructing weekly stock returns can have a decisive effect on the outcomes of empirical studies.

• Item Type: MPRA Paper
• Language: English
• Keywords: stock markets, weekly returns, statistical properties
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General; G - Financial Economics > G1 - General Financial Markets > G10 - General; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C80 - General

References

Baumöhl, E. – Výrost, T. 2010. Stock Market Integration: Granger Causality Testing with respect to Nonsynchronous Trading Effects. Czech Journal of Economics and Finance, 60(5), p. 414-25.

Cheung, Y. W. – Lai, K. S. 1995. Lag Order and Critical Values of a Modified Dickey-Fuller Test. Oxford Bulletin of Economics and Statistics, 57(3), p. 411-19.

Cook, S. – Manning, N. 2004. Lag optimisation and finite-sample size distortion of unit root tests. Economics Letters, 84(2), p. 267-74.

Engle, R. F. – Ng, V. K. 1993. Measuring and Testing the Impact of News On Volatility. Journal of Finance, 48(5), pp. 1749-78.

Engle, R. F. – Sheppard, K. 2001. Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working Paper 8554.

Engle, R. F. 2002. Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business Economic Statistics, 20(3), pp. 339-50.

Hobijn, B. – Franses, P. H. – Ooms, M. 2004. Generalizations of the KPSS-test for stationarity. Statistica Neerlandica, 58(4), p. 483-502.

Inclán, C. – Tiao, G. 1994. Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89(427), p. 913-23.

Newey, W. K. – West, K. D. 1994. Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61(4), p. 631-53.

Ng, S. – Perron, P. 1995. Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag. Journal of the American Statistical Association, 90(429), p. 268-81.

Ng, S. – Perron, P. 2001. Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), p. 1519-54.

Perron, P. – Qu, Z. 2007. A simple modification to improve the finite sample properties of Ng and Perron`s unit root tests. Economics Letters, 94(1), p. 12-19.

Sansó, A. – Arragó, V. – Carrion, J. 2004. Testing for change in the unconditional variance of financial time series. Revista de Economiá Financiera, 4, p. 32-53.

Výrost, T. – Baumöhl, E. – Lyócsa, Š. 2011. On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries. MPRA Working Paper 27927, University Library of Munich, Germany.