Intrinsic bubbles revisited : evidence from nonlinear cointegration and forecasting

Yue MA, Angelos KANAS

Research output: Journal PublicationsJournal Article (refereed)

8 Citations (Scopus)

Abstract

This paper offers strong further empirical evidence to support the intrinsic bubble model of stock prices, developed by Froot and Obstfeld (American Economic Review, 1991), in two ways. First, our results suggest that there is a long-run nonlinear relationship between stock prices and dividends for the US stock market during the period 1871-1996. Second, we find that the out-of-sample forecasting performance of the intrinsic bubbles model is significantly better than the performance of two alternatives, namely the random walk and the rational bubbles model.
Original languageEnglish
Pages (from-to)237-250
Number of pages14
JournalJournal of Forecasting
Volume23
Issue number4
DOIs
Publication statusPublished - 1 Jul 2004

Fingerprint

Cointegration
Bubble
Forecasting
Stock Prices
Dividend
Stock Market
Long-run
Random walk
Model
Economics
Alternatives
Evidence
Intrinsic bubbles
Nonlinear forecasting
Nonlinear cointegration
Stock prices

Keywords

  • Forecasting
  • Intrinsic bubbles
  • Kalman filter
  • Nonlinear cointegration
  • Random walk

Cite this

MA, Yue ; KANAS, Angelos. / Intrinsic bubbles revisited : evidence from nonlinear cointegration and forecasting. In: Journal of Forecasting. 2004 ; Vol. 23, No. 4. pp. 237-250.
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Intrinsic bubbles revisited : evidence from nonlinear cointegration and forecasting. / MA, Yue; KANAS, Angelos.

In: Journal of Forecasting, Vol. 23, No. 4, 01.07.2004, p. 237-250.

Research output: Journal PublicationsJournal Article (refereed)

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AU - KANAS, Angelos

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AB - This paper offers strong further empirical evidence to support the intrinsic bubble model of stock prices, developed by Froot and Obstfeld (American Economic Review, 1991), in two ways. First, our results suggest that there is a long-run nonlinear relationship between stock prices and dividends for the US stock market during the period 1871-1996. Second, we find that the out-of-sample forecasting performance of the intrinsic bubbles model is significantly better than the performance of two alternatives, namely the random walk and the rational bubbles model.

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KW - Kalman filter

KW - Nonlinear cointegration

KW - Random walk

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