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Statistical Interpretation of Economic Bubbles
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 Title & Authors
Statistical Interpretation of Economic Bubbles
Yeo, In-Kwon;
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In this paper, we propose a statistic to measure investor sentiment. It is a usual phenomenon that an asymmetric volatility (referred to as the leverage effect) is observed in financial time series and is more sensitive to bad news rather than good news. In a bubble state, investors tend to continuously speculate on financial instruments because of optimism about the future; subsequently, prices tend to abnormally increase for a long time. Estimators of the transformation parameter and the skewness based on Yeo-Johnson transformed GARCH models are employed to check whether a bubble or abnormality exist. We verify the appropriacy of the proposed interpretation through analyses of KOSPI and NIKKEI.
GARCH model;leverage effect;skewness;Yeo-Johnson transformation;
 Cited by
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-328. crossref(new window)

Box, G. E. P. and Cox, C. R. (1964). An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-252.

Camerer, C. (1989). Bubbles and fads in asset prices, Journal of Economic Surveys, 3, 3-41. crossref(new window)

Diba, B. T. and Grossman, H. I. (1988). The theory of rational bubbles in stock prices, The Economic Journal, 98, 746-754. crossref(new window)

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of the United Kingdom inflation, Econometrica, 50, 987-1007. crossref(new window)

Flood, R. P. and Hodrick, R. J. (1990). On testing for speculative bubbles, The Journal of Economic Perspectives, 4, 85-101.

Garber, P. M. (1990). Famous first bubbles, The Journal of Economic Perspectives, 4, 35-54. crossref(new window)

Glosten, L. R., Jagannathan, R. and Runkle, D. (1993). On the relation between the expected value and the volatility of nominal excess returns on stocks, Journal of Finance, 48, 1779-1801. crossref(new window)

Henry, O. (1998). Modelling the asymmetry of stock market volatility. Applied Financial Economics, 8, 145-153. crossref(new window)

John, J. A. and Draper, N. R. (1980). An alternative family of transformations, Applied Statistics, 29, 190-197. crossref(new window)

Ma, Y. and Kanas, A. (2004). Intrinsic bubbles revisited: Evidence from nonlinear cointegration and forecasting, Journal of Forecasting, 23, 237-250. crossref(new window)

Mardia, K. V. (1980). Tests of univariate and multivariate normality, Handbook of Statistics, 1, 279-320. crossref(new window)

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370. crossref(new window)

Spence, J. (1820). Anecdotes, Observations, and Characters, of Books and Men, Edited by Singer, S. S., London.

van Zwet, W. R. (1964). Convex Transformations of Random Variables, Mathematisch Centrum, Amsterdam.

Wu, Y. (1997). Rational bubbles in the stock market: Accounting for the U.S. stock-price volatility, Economic Inquiry, 35, 309-319. crossref(new window)

Yeo, I. K. and Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry, Biometrika, 87, 954-959. crossref(new window)

Zakoian, J. M. (1994). Threshold heteroskedastic models, Journal of Economic Dynamics and Control, 18, 931-955. crossref(new window)