Causality of Weather Effects on Stock Returns and Volatility
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Published:
May 28, 2019
Keywords:
Causality test, contemporaneous causality, directed acyclic graph, Granger causality, model misspecification, return behavior, weather effects
Main Article Content
Abstract
Causality tests for weather effects on stock market returns and volatility were conducted to ensure that the weather was in fact the cause. Based on the data for Thailand from 1992 to 2016 (6,091 daily observations), both Granger causality and directed acyclic graph causality tests revealed bi-directional relationships of the weather with returns and volatility. Despite the bi-directional relationships, the analyses led to the conclusion that there were weather-caused effects on returns and volatility.
Article Details
Section
Research Articles
Copyright: Asia-Pacific International University reserve exclusive rights to publish, reproduce and distribute the manuscript and all contents therein.
References
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Sims, C. (1986). Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review, 10(1), 2–16.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT Press.
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Swanson, N., & Granger, C. (1997). Impulse response functions based on a causal approach to residual orthogonalization in vector autoregression. Journal of the American Statistical Association, 92(437), 357–367.
Teramoto, R., Saito, C., & Funahashi, S. (2014). Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments. BMC Bioinformatics, 15, 228. doi:10.1186/1471-2105-15-228
Tufan, E., & Hamarat, B. (2003). Weather effect: An evidence from Turkish Stock Exchange (Working Paper). Eskişehir: Anadolu University. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 463880
Tuna, G., & Bektur, C. (2015). The relationship between price-trade volume and weather effect in Istanbul Stock Exchange: Asymmetric causality test analysis. Financial Studies, 4, 6–20.
Vlady, S., & Tufan, E. (2011). Causality of weather conditions in Australian stock equity returns. Young Economists Journal, 1(17), 184–197.
Zellner, A. (1962). An efficient method of estimating seemingly unrelated regression and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368.
Zivot, E., & Wang, J. (2006). Vector autoregressive models for multivariate time series: Modeling financial time series with S-Plus® (2nd ed.). New York: Springer.
Choudhry, T. (1996). Stock market volatility and the crash of 1987: Evidence from six emerging markets. Journal of International Money and Finance, 15(6), 969–981.
Cunningham, M. (1979). Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan. Journal of Personality and Social Psychology, 37(11), 1947–1956.
DeLong, B., Shleifer, A., Summers, L., & Waldmann, R. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703–738.
Diebold, F. X. (2006). Elements of forecasting (4th ed.). Cincinnati, OH: South-Western.
Doyle, J., & Chen, C. (2009). The wandering weekday effect in major stock markets. Journal of Banking and Finance, 33(1), 1388–1399.
Enders, W. (1995). Applied econometrics time series. New York: John Wiley & Sons.
Gelper, S., & Croux, C. (2007). Multivariate out-of-sample tests for Granger causality. Computational Statistics & Data Analysis, 51(7), 3319–3329.
Glymour, C., Scheines, R., Spirtes, P., & Ramsey, J. (2004). TETRAD-IV new manual. Retrieved from www.phil.cmu.edu/projects/tetrad_download/files/new_manual.pdf
Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
Harris, N., & Drton, M. (2013). PC algorithm for nonparanormal graphical models. Journal of Machine Learning Research, 14, 3365–3383.
Hines, C., & Halevy, I. (1997). On the reality and nature of a certain sun-weather correlation. Journal of the Atmospheric Science, 34(2), 382–404.
Hirshleifer, D, & Shumway, T. (2003). Good day sunshine: Stock returns and the weather. Journal of Finance, 58(3), 1009–1032.
Howarth, E., & Hoffman M. (1984). A multidimensional approach to the relationship between mood and weather. British Journal of Psychology, 75(1), 15–23.
Jacobsen, B., & Marquering, W. (2008). Is it the weather? Journal of Banking and Finance, 32(4), 526–540.
Kathiravan, C., Selvam, M., Venkateswar, S., Lingaraja, K., Vasani, S. A., & Kannaiah, D. (2018). An empirical investigation of the interlinkages of stock returns and the weather at the Indian stock exchange. Academy of Strategic Management Journal, 17(1), 1–14.
Khanthavit, A. (2016). The fast and slow speed of convergence to market efficiency: A note for large and small stocks on the Stock Exchange of Thailand. Social Science Asia, 2(2), 1–6.
Khanthavit, A. (2017). Instrumental-variable estimation of Bangkok-weather effects in the Stock Exchange of Thailand. Asian Academy of Management Journal of Accounting and Finance, 13(1), 83–111.
Khanthavit, A. (2019). Time-varying weather effects on Thai government bond returns. DLSU Business and Economics Review, 28(2), 122–132.
Krivelyova, A., & Robotti, C. (2003). Playing the field: Geomagnetic storms and international stock markets (Working Paper). Atlanta: Federal Reserve Bank of Atlanta. Retrieved from https://www.econstor.eu/ bitstream/10419/100979/1/wp2003-05a.pdf
Maziarz, M. (2015). A review of the Granger-causality fallacy, Journal of Philosophical Economics, 8(2), 86–105.
Mehra, R., & Sah, R. (2002). Mood fluctuations, projection bias, and volatility of equity prices. Journal of Economic Dynamics and Control, 26(5), 869–887.
Morgan, S., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. New York: Cambridge University Press.
Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press.
Pereda, E., Quiroga, R., & Bhattcharya, J. (2005). Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology, 77(1–2), 1–37.
Persinger, M. (1975). Lag responses in mood reports to changes in the weather matrix. International Journal of Biometeorology, 19(2), 108–114.
Rogers, L., & Satchell, S. (1991). Estimating variance from high, low, and closing prices. Annals of Applied Probability, 1(4), 504–512.
Roll, R. (1984). Orange juice and weather. American Economic Review, 74(5), 861–880.
Shalen, C. (1993). Volume, volatility, and the dispersion of beliefs. Review of Financial Studies, 6(2), 405–434.
Sheikh, M., Shah, S., & Mahmood, S. (2017). Weather effects on stock returns and volatility in South Asian markets. Asia-Pacific Financial Markets, 24(2), 75–107.
Sims, C. (1986). Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review, 10(1), 2–16.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT Press.
Statman, M., Thorley, S., & Vorkink, K. (2006). Investor overconfidence and trading volume. Review of Financial Studies, 19(4), 1531–1565.
Swanson, N., & Granger, C. (1997). Impulse response functions based on a causal approach to residual orthogonalization in vector autoregression. Journal of the American Statistical Association, 92(437), 357–367.
Teramoto, R., Saito, C., & Funahashi, S. (2014). Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments. BMC Bioinformatics, 15, 228. doi:10.1186/1471-2105-15-228
Tufan, E., & Hamarat, B. (2003). Weather effect: An evidence from Turkish Stock Exchange (Working Paper). Eskişehir: Anadolu University. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 463880
Tuna, G., & Bektur, C. (2015). The relationship between price-trade volume and weather effect in Istanbul Stock Exchange: Asymmetric causality test analysis. Financial Studies, 4, 6–20.
Vlady, S., & Tufan, E. (2011). Causality of weather conditions in Australian stock equity returns. Young Economists Journal, 1(17), 184–197.
Zellner, A. (1962). An efficient method of estimating seemingly unrelated regression and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368.
Zivot, E., & Wang, J. (2006). Vector autoregressive models for multivariate time series: Modeling financial time series with S-Plus® (2nd ed.). New York: Springer.