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Asset Pricing Models: Specification, Data and Theoretical Foundation

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Empirical Asset Pricing Models

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Abstract

The author surveys and discusses linear asset pricing models with the intent to identify some sets of variables or factors with reduced dimensionality to approximate the core or pricing kernel of asset returns. A theoretical foundation may start with discussion on factor pricing models where asset returns are projected onto some lower-dimensional sets of factors that possibly explain the major variations of asset returns. The aim is to identify major determinants for the fluctuations of asset returns where these determinants satisfy some systematic properties that ensure their indispensable roles.

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Notes

  1. 1.

    In fact, it is rather intuitive to see the claim. If the theory requires no intercept in the model, inclusion of the intercept is redundant in estimations when the theory holds.

  2. 2.

    This should be denoted as the industrial production growth rate since the variable is calculated as the inter-temporal difference of the logarithms of industrial production.

  3. 3.

    Since the dependent variable is equity premium, it is usually of short memory. It would be more appropriate to consider Eq. (1.2.15) with the error-correction model, given that these regressors are long-memory and of a cointegrating relationship.

References

  • Bai, J. 2003. Inferential Theory for Factor Models of Large Dimensions. Econometrica 71: 135–171.

    Article  Google Scholar 

  • Bai, J., and S. Ng. 2002. Determining the Numbers of Factors in Approximate Factor Models. Econometrica 70: 191–221.

    Article  Google Scholar 

  • Barberis, N. 2000. Investing for the Long Run when Returns Are Predictable. Journal of Finance 55: 225–264.

    Article  Google Scholar 

  • Bekaert, G., and R.J. Hodrick. 1992. Characterizing Predictable Components in Excess Returns on Equity and Foreign Exchange Markets. Journal of Finance 47: 467–509.

    Article  Google Scholar 

  • Bossaerts, P., and P. Hillion. 1999. Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn? Review of Financial Studies 12: 405–428.

    Article  Google Scholar 

  • Boudoukh, J., M.P. Richardson, and R.F. Whitelaw. 1994. A Tale of Three Schools: Insights of Autocorrelations of Short-Horizon Stock Returns. Review of Financial Studies, 7: 539–573.

    Article  Google Scholar 

  • Campbell, J.Y., and S.B. Thompson. 2008. Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? Review of Financial Studies 21: 1509–1531.

    Article  Google Scholar 

  • Chamberlain, G. 1983. Funds, Factors, and Diversification in Arbitrage Pricing Models. Econometrica 51: 1305–1323.

    Article  Google Scholar 

  • Chamberlain, G., and M. Rothschild. 1983. Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets. Econometrica 51: 1281–1304.

    Article  Google Scholar 

  • Chen, N.-F., R. Roll, and S.A. Ross. 1986. Economic Forces and the Stock Market. Journal of Business 59: 383–403.

    Article  Google Scholar 

  • Clements, M.P., and D.F. Hendry. 1999. Forecasting Non-stationary Economic Time Series. Cambridge: MIT Press.

    Google Scholar 

  • Cooper M., and H. Gulen. 2006. Is Time-Series-Based Predictability Evident in Real Time? Journal of Business 79: 1263–1292.

    Article  Google Scholar 

  • Cooper M., R.C. Gutierrez Jr., and B. Marcum. 2005. On the Predictability of Stock Returns in Real Time. Journal of Business 78: 469–489.

    Article  Google Scholar 

  • Deetz, M., T. Poddig, I. Sidorovitch, and A. Varmaz. 2009. An Evaluation of Conditional Multi-Factor Models in Active Asset Allocation Strategies: An Empirical Study for the German Stock Market. Financial Markets and Portfolio Management 23: 285–313.

    Article  Google Scholar 

  • Fama, E.F., and K.R. French. 1992. The Cross-Section of Expected Stock Returns. Journal of Finance, 47: 427–465.

    Article  Google Scholar 

  • Fama, E.F., and K.R. French. 1995. Size and Book-to-Market Factors in Earnings and Returns. Journal of Finance 50: 131–155.

    Article  Google Scholar 

  • Fama, E.F., and K.R. French. 1996. Multifactor Explanations of Asset Pricing Anomalies. Journal of Finance 51: 55–84.

    Article  Google Scholar 

  • Fama, E.F., and J.D. MacBeth. 1973. Risk, Return and Equilibrium: Empirical Tests. Journal of Political Economy 81: 607–636.

    Article  Google Scholar 

  • Ferson, W.E., and C.R. Harvey. 1991. The Variation of Economic Risk Premiums. Journal of Political Economy 99: 385–415.

    Article  Google Scholar 

  • Ferson, W., and R.A. Korajczyk. 1995. Do Arbitrage Pricing Models Explain the Predictability of Stock Returns? Journal of Business 68: 309–349.

    Article  Google Scholar 

  • Ferson, W.E., S. Sarkissian, and T. Simin. 2003. Spurious Regressions in Financial Economics. Journal of Finance 58: 1393–1413.

    Article  Google Scholar 

  • Giacomini, R., and H. White. 2006. Tests of Conditional Predictive Ability. Econometrica 74: 1545–1578.

    Article  Google Scholar 

  • Granger, C.W.J., and F. Marmol. 1998. The Correlogram of a Long Memory Process Plus a Simple Noise, Discussion Paper, 97-29. University of California, San Diego.

    Google Scholar 

  • Granger, C.W.J., and P. Newbold. 1974. Spurious Regressions in Economics. Journal of Econometrics 4: 111–120.

    Article  Google Scholar 

  • Grinblatt, M., and S. Titman. 1985. Approximate Factor Structures: Interpretations and Implications for Empirical Tests. Journal of Finance 40: 1367–1373.

    Article  Google Scholar 

  • He, J., and K.K. Ng. 1994. Economic Forces, Fundamental Variables, and Equity Returns. Journal of Business 67: 599–609.

    Article  Google Scholar 

  • Koopmans, T.C. 1947. Measurement Without Theory. Review of Economics and Statistics 29: 161–172.

    Article  Google Scholar 

  • Kirby, C. 1998. The Restrictions on Predictability Implied by Rational Asset Pricing Models. Review of Financial Studies 11: 343–382.

    Article  Google Scholar 

  • Lettau, M., and S. Ludvigson. 2001. Consumption, Aggregate Wealth, and Expected Stock Returns. Journal of Finance 56: 815–849.

    Article  Google Scholar 

  • Lewellen, J., S. Nagel, and J. Shanken. 2010. A Skeptical Appraisal of Asset Pricing Tests. Journal of Financial Economics 96: 175–194.

    Article  Google Scholar 

  • Lo, A.W., and A.C. MacKinlay. 1990. Data Snooping Biases in Tests of Financial Asset Pricing Models. Review of Financial Studies 3: 431–467.

    Article  Google Scholar 

  • Petkova, R. 2006. Do the Fama-French Factors Proxy for Innovations in Predictive Variables? Journal of Finance 61: 581–621.

    Article  Google Scholar 

  • Pettit, R.R., and R. Westerfield. 1974. Using the Capital Asset Pricing Model and the Market Model to Predict Security Returns. Journal of Financial and Quantitative Analysis 9: 579–605.

    Article  Google Scholar 

  • Rapach, D.E., J.K. Strauss, and G. Zhou. 2010. Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy. Review of Financial Studies 23: 821–862.

    Article  Google Scholar 

  • Reisman, H. 1988. A General Approach to the Arbitrage Pricing Theory. Econometrica 56: 473–476.

    Article  Google Scholar 

  • Reisman, H. 1992. Reference Variables, Factor Structure, and the Approximate Multibeta Representation. Journal of Finance 47: 1303–1314.

    Article  Google Scholar 

  • Ross, S.A. 1976. The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory 13: 341–360.

    Article  Google Scholar 

  • Rust, J. 2014: The Limits of Inference with Theory: A Review of Wolpin (2013). Journal of Economic Literature 52: 820–850.

    Article  Google Scholar 

  • Shmueli, G. 2010. To Explain or to Predict? Statistical Science 25: 289–310.

    Article  Google Scholar 

  • Simin, T. 2008. The Poor Predictive Performance of Asset Pricing Models. Journal of Financial and Quantitative Analysis 43: 366–380.

    Article  Google Scholar 

  • Torous, W., R. Valkanov, and S. Yan. 2004. On Predicting Stock Returns with Nearly Integrated Explanatory Variables. Journal of Business 77: 937–966.

    Article  Google Scholar 

  • Welch, I., and A. Goyal. 2008. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction. Review of Financial Studies 21: 1455–1508.

    Article  Google Scholar 

  • Wolpin, K.I. 2013. The Limits of Inference Without Theory. Cambridge: MIT Press.

    Google Scholar 

  • Yule, G.U. 1926. Why Do We Sometimes Get Nonsense Correlations Between Time Series? A Study in Sampling and the Nature of Time Series. Journal of the Royal Statistical Society 89: 1–63.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Business and Management, Azusa Pacific University, Stevenson Ranch, CA, USA

    Jau-Lian Jeng

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  1. Jau-Lian Jeng
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Jeng, JL. (2018). Asset Pricing Models: Specification, Data and Theoretical Foundation. In: Empirical Asset Pricing Models. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-74192-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-74192-5_1

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-319-74191-8

  • Online ISBN: 978-3-319-74192-5

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