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Forecasting Expected Returns in the Financial Markets

Author

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  • Satchell, Stephen

    (Consultant to financial institutions and Reader in Financial Econometrics at Trinity College, Cambridge, Stephen Satchell is Editor-in-Chief of the Journal of Asset Management and Derivatives, Use, Trading, and Regulation. He has edited or authored over 20 books on finance.)

Abstract

Forecasting returns is as important as forecasting volatility in multiple areas of finance. This topic, essential to practitioners, is also studied by academics. In this new book, Dr Stephen Satchell brings together a collection of leading thinkers and practitioners from around the world who address this complex problem using the latest quantitative techniques. *Forecasting expected returns is an essential aspect of finance and highly technical *The first collection of papers to present new and developing techniques *International authors present both academic and practitioner perspectives

Suggested Citation

  • Satchell, Stephen, 2007. "Forecasting Expected Returns in the Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780750683210.
    • Handle: RePEc:eee:monogr:9780750683210
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    Citations

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    Cited by:

    1. Moritz Duembgen & L. C. G. Rogers, 2014. "Estimate nothing," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2065-2072, December.
    2. Lin, Lisha & Li, Yaqiong & Gao, Rui & Wu, Jianhong, 2021. "The numerical simulation of Quanto option prices using Bayesian statistical methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    3. Benjamin Hippert & André Uhde & Sascha Tobias Wengerek, 2019. "Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe," Review of Derivatives Research, Springer, vol. 22(2), pages 203-259, July.
    4. Hanno Gottschalk & Elpida Nizami & Marius Schubert, 2016. "Option Pricing in Markets with Unknown Stochastic Dynamics," Papers 1602.04848, arXiv.org, revised Jan 2017.
    5. Zura Kakushadze, 2014. "Mean-Reversion and Optimization," Papers 1408.2217, arXiv.org, revised Feb 2016.
    6. Jacquelyn E Humphrey & Darren D Lee & Yaokan Shen, 2012. "The independent effects of environmental, social and governance initiatives on the performance of UK firms," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 135-151, August.
    7. Jim Liew & Ryan Roberts, 2013. "U.S. Equity Mean-Reversion Examined," Risks, MDPI, vol. 1(3), pages 1-14, December.
    8. François Ogliaro & Robert K Rice & Stewart Becker & Raul Leote de Carvalho, 2012. "Explicit coupling of informative prior and likelihood functions in a Bayesian multivariate framework and application to a new non-orthogonal formulation of the Black–Litterman model," Journal of Asset Management, Palgrave Macmillan, vol. 13(2), pages 128-140, April.
    9. Unni, Arjun C. & Ongsakul, Weerakorn & Madhu M., Nimal, 2020. "Fuzzy-based novel risk and reward definition applied for optimal generation-mix estimation," Renewable Energy, Elsevier, vol. 148(C), pages 665-673.
    10. Zhao, Daping & Bai, Lin & Fang, Yong & Wang, Shouyang, 2022. "Multi‐period portfolio selection with investor views based on scenario tree," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    11. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.
    12. Frieder Meyer-Bullerdiek, 2021. "Out-of-sample performance of the Black-Litterman model," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 10(2), pages 1-2.
    13. Markus Hertrich & Heinz Zimmermann, 2017. "On the Credibility of the Euro/Swiss Franc Floor: A Financial Market Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 567-578, March.
    14. Hertrich Markus, 2016. "The Costs of Implementing a Unilateral One-Sided Exchange Rate Target Zone," Review of Economics, De Gruyter, vol. 67(1), pages 91-120, May.
    15. Markus Hertrich, 2015. "A Cautionary Note on the Put-Call Parity under an Asset Pricing Model with a Lower Reflecting Barrier," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 151(III), pages 227-260, September.
    16. Shu Wing Ho & Alan Lee & Alastair Marsden, 2011. "Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models," JRFM, MDPI, vol. 4(1), pages 1-23, December.
    17. Bogdan, Dima & Ştefana Maria, Dima & Roxana, Ioan, 2022. "A Value-at-Risk forecastability indicator in the framework of a Generalized Autoregressive Score with “Asymmetric Laplace Distribution”," Finance Research Letters, Elsevier, vol. 45(C).
    18. Silva, Thuener & Pinheiro, Plácido Rogério & Poggi, Marcus, 2017. "A more human-like portfolio optimization approach," European Journal of Operational Research, Elsevier, vol. 256(1), pages 252-260.
    19. Lisha Lin & Yaqiong Li & Rui Gao & Jianhong Wu, 2019. "The Numerical Simulation of Quanto Option Prices Using Bayesian Statistical Methods," Papers 1910.04075, arXiv.org.
    20. Wickern, Tobias, 2011. "Confidence in prior knowledge: Calibration and impact on portfolio performance," Discussion Papers in Econometrics and Statistics 7/11, University of Cologne, Institute of Econometrics and Statistics.
    21. Hubert Dichtl & Wolfgang Drobetz & Viktoria‐Sophie Wendt, 2021. "How to build a factor portfolio: Does the allocation strategy matter?," European Financial Management, European Financial Management Association, vol. 27(1), pages 20-58, January.
    22. Martin Schans & Hens Steehouwer, 2017. "Time-Dependent Black–Litterman," Journal of Asset Management, Palgrave Macmillan, vol. 18(5), pages 371-387, September.

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