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Estimating and Forecasting

  • Harold L. Vogel
Chapter

Abstract

The basic elements needed for defining and finding bubbles and crashes are now in place and this chapter collates and summarizes the concepts that have earlier been developed. The result is a practical empirical method that enables extreme events to be statistically defined, detected, and tested by applying the elasticity-of-variance approach to real-world data. Given the fractal, scaled nature of financial markets, all bubbles and crashes are comprised of what can be called microbubbles and microcrashes.

References

  1. Ang, A., & Bekaert, G. (2007). Stock Return Predictability: Is It There? Review of Financial Studies, 20(3), 651–707.CrossRefGoogle Scholar
  2. Armitage, S. (2005). The Cost of Capital: Intermediate Theory. New York: Cambridge University Press.CrossRefGoogle Scholar
  3. Benzoni, L., Collin-Dufresne, P., & Goldstein, R. S. (2010). Explaining Asset Pricing Puzzles Associated with the 1987 Market Crash. Working Paper 2010–10, Federal Reserve Bank of Chicago.Google Scholar
  4. Blanchard, O. J., & Watson, M. W. (1982). Bubbles, Rational Expectations, and Financial Markets. In P. Wachtel (Ed.), Crises in the Economic and Financial Structure. Lexington: Lexington Books.Google Scholar
  5. Browning, E. S. (2007a, July 30). Analysts Debate If Bull Market Has Peaked. Wall Street Journal.Google Scholar
  6. Casti, J. (2002, August 31). I Know What You’ll Do Next Summer. New Scientist.Google Scholar
  7. Cochrane, J. H. (2005). Asset Pricing (revised ed.). Princeton: Princeton University Press.Google Scholar
  8. Collyns, C., & Senhadji, A. (2003). Lending Booms, Real Estate Bubbles, and the Asian Crisis. In Hunter et al. (2003).Google Scholar
  9. Cuthbertson, K., & Nitzsche, D. (2004). Quantitative Financial Economics (2nd ed.). West Sussex: Wiley.Google Scholar
  10. Da, Z., Engelberg, J., & Gao, P. (2015). The Sum of All FEARS Investor Sentiment and Asset Prices. Review of Financial Studies, 28(1), 1–32.CrossRefGoogle Scholar
  11. Dornbusch, R. (1976). Expectations and Exchange Rate Dynamics. Journal of Political Economy, 84(6), 1161–1176.CrossRefGoogle Scholar
  12. Fama, E. F. (1965). The Behavior of Stock-Market Prices. The Journal of Business, 38(1), 34–105.CrossRefGoogle Scholar
  13. Ferson, W., & Harvey, C. (1991). Sources of Predictability in Portfolio Returns. Financial Analysts Journal, 47(3), 49–56.CrossRefGoogle Scholar
  14. Fleckenstein, W. A., & Sheehan, F. (2008). Greenspan’s Bubbles: The Age of Ignorance at the Federal Reserve. New York: McGraw-Hill.Google Scholar
  15. Frost, A. J., & Prechter, R. R. (1978). Elliot Wave Principle. Chappaqua: New Classics Library.Google Scholar
  16. Geary, R. C. (1970). Relative Efficiency of Count of Sign Changes for Assessing Residual Autoregression in Least Squares Regression. Biometrika, 57(1), 123–127.CrossRefGoogle Scholar
  17. Gray, W. R., & Vogel, J. R. (2016). Quantitative Momentum. Hoboken: Wiley.Google Scholar
  18. Gujarati, D. N. (2003). Basic Econometrics, 4th (international) ed. New York: McGraw-Hill.Google Scholar
  19. Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor Sentiment Aligned: A Powerful Predictor of Stock Returns. Review of Financial Studies, 28(3), 791–837.CrossRefGoogle Scholar
  20. Hunter, W. C., Kaufman, G. G., & Pomerleano, M. (Eds.). (2003). Asset Price Bubbles: The Implications for Monetary, Regulatory, and International Policies. Cambridge, MA: MIT Press (Paperback edition, 2005).Google Scholar
  21. Jagadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65–91.CrossRefGoogle Scholar
  22. Jagadeesh, N., & Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699–720.CrossRefGoogle Scholar
  23. Kaizoji, T., & Sornette, D. (2008). Market Bubbles and Crashes. MPRA Paper 15204, University Library Munich, Germany. http://arxiv.org/pdf/0812.2449
  24. Kennedy, P. (2003, 2008). A Guide to Econometrics (6th ed.). Malden/Oxford: Blackwell.Google Scholar
  25. Livio, M. (2002). The Golden Ratio. New York: Random House/Broadway Books.Google Scholar
  26. Lo, A. W. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton: Princeton University Press.CrossRefGoogle Scholar
  27. McQueen, G., & Thorley, S. (1994). Bubbles, Stock Returns, and Duration Dependence. Journal of Financial and Quantitative Analysis, 29(3), 379–401.CrossRefGoogle Scholar
  28. Mood, A. (1940). The Distribution Theory of Runs. Annals of Mathematical Statistics, 11, 367–392.Google Scholar
  29. Napier, R. (2007). Anatomy of the Bear: Lessons from Wall Street’s Four Great Bottoms (2nd ed.). Hampshire: Harriman House.Google Scholar
  30. O’Brien, P. C., & Dyck, P. J. (1985). A Runs Test Based on Run Lengths. Biometrics, 41(1), 237–244.CrossRefGoogle Scholar
  31. Pesaran, M. H., & Timmermann, A. (1994). Forecasting Stock Returns: An Examination of Stock Market Trading in the Presence of Transaction Costs. Journal of Forecasting, 13(4), 335–367.CrossRefGoogle Scholar
  32. Poon, S., & Granger, C. (2005). Practical Issues in Forecasting Volatility. Financial Analysts Journal, 61(1), 45–56.CrossRefGoogle Scholar
  33. Prechter, R. R, Jr. (2003). Conquer the Crash. West Sussex: Wiley.Google Scholar
  34. Prechter, R. R, Jr. (2016). The Socionomic Theory of Finance. Gainesville: Socionomics Institute Press.Google Scholar
  35. Roehner, B. M. (2002). Patterns of Speculation: A Study in Observational Econophysics. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  36. Schaller, H., & van Norden, S. (1997). Fads or Bubbles? Working Paper 97–2, Bank of Canada. http://www.bankofcanada.ca/en/res/wp/1997/wp97-2.html
  37. Shreve, S. E. (2008). Stochastic Calculus for Finance II: Continuous Time Models. New York: Springer.Google Scholar
  38. Skidelsky, R. (2009). Keynes: The Return of the Master. New York: Public Affairs (Perseus).Google Scholar
  39. Sornette, D. (2009). Dragon-Kings, Black Swans and the Prediction of Crises. International Journal of Terraspace Science and Engineering, 2(1), 1–18. arXiv:0907.4290.Google Scholar
  40. Taleb, N. N. (2007). The Black Swan. New York: Random House.Google Scholar
  41. Thaler, R. H. (1992). The Winner’s Curse: Paradoxes and Anomalies of Economic Life. Princeton: Princeton University Press.Google Scholar
  42. Tirole, J. (1985). Asset Bubbles and Overlapping Generations. Econometrica, 53(6), 1499–1528.CrossRefGoogle Scholar
  43. Treynor, J. L. (1998). Bulls, Bears, and Market Bubbles. Financial Analysts Journal, 54(2), 69–74.CrossRefGoogle Scholar
  44. Wachtel, P. (Ed.). (1982). Crises in the Economic and Financial Structure. Lexington: Lexington Books.Google Scholar
  45. West, G. (2017). Scale: The Universal Laws of Growth, Innovation, and Sustainability. New York: Penguin/Random House.Google Scholar
  46. Western, D. L. (2004). Booms, Bubbles and Busts in US Stock Markets. New York: Routledge.CrossRefGoogle Scholar
  47. Wilmott, P., & Orrell, D. (2017). The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets. Hoboken: Wiley.CrossRefGoogle Scholar
  48. Woo, W. T. (1987). Some Evidence of Speculative Bubbles in the Foreign Exchange Markets. Journal of Money, Credit and Banking, 19(4), 499–514.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Harold L. Vogel
    • 1
  1. 1.New YorkUSA

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