Empirical Exchange Rate Models of the Seventies: Are Any Fit to Survive?

Meese, Richard; Rogoff, Kenneth

doi:10.17016/ifdp.1981.184

Cited by 693 publications

(999 citation statements)

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“…Following conventions in the literature, three benchmark models, random walk (RW), ARMA, and SVR model, are used in the model evaluation process [63]. The lag order for benchmark ARMA(r,m) during the forecasting process is set to ARMA(1,1).…”

Section: Empirical Studiesmentioning

confidence: 99%

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Zhu

Zou

et al. 2014

The Scientific World Journal

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As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

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Section: Empirical Studiesmentioning

confidence: 99%

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Zhu

Zou

et al. 2014

The Scientific World Journal

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“…Most studies find that exchange rates follow martingale processes, so that the best forecast for time t + 1 is the value at time t (e.g. Meese and Singleton 1982;Meese and Rogoff 1983). Specifically, if S t is the logarithm of the exchange rate level then E t−1 (S t ) = S t−1 , where E t−1 is the conditional expectations operator as of time t − 1.…”

Section: Econometric Modelmentioning

confidence: 99%

Exchange rate exposure in the pre- and post-Euro periods: evidence from Finland

Koutmos

Knif

2011

The European Journal of Finance

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This paper investigates asymmetric as well as first- and second-moment exchange rate exposure of the Finnish stock exchange (FSE) during the pre- and post-Euro periods. There is evidence of significant market-level and residual exchange rate exposure in the pre-Euro period. In the period, following the introduction of the Euro, however, exchange rate exposure becomes insignificant both at the market level and at the individual portfolio level with minor exceptions. Obviously, the introduction of the Euro has had a profound impact on the exchange rate exposure of the FSE.exchange rate exposure, post-Euro exposure, asymmetric exposure, exchange rate volatility,

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“…Meese and Rogoff (1983a) showed that a random walk model of the exchange rate performed better in forecasting than any structural model. To date, this result still holds true, and as noted in Devereux (1997, p. 774), "this result has not been substantially overturned''.…”

Section: Forecasting Performance Of Estimated Modelsmentioning

confidence: 99%

Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models

Baharumshah

Liew²

2006

Open Econ Rev

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This paper compares the forecasting performance of the Smooth Transition Autoregressive (STAR) model with the conventional linear Autoregressive (AR) and Simple Random Walk (SRW) models. The empirical analysis was conducted using quarterly data for the yen-based currencies of six major East Asian countries. We discovered strong evidence on nonlinear mean reversion in deviation from purchasing power parity (PPP). The results suggest that both the STAR and AR models outperform or at least match the performance of the SRW model. The results also show that the STAR model outperforms the AR model, its linear competitor in a 14-quarter forecast horizon. This finding is consistent with the emerging line of research that emphasizes the importance of allowing nonlinearity in the adjustment of exchange rate.Since the establishment of the free float regime in March 1973, exchange rate forecasting has been an important research issue in exchange rate study. However, previous findings generally cannot negate over the fact that exchange rate models forecast no better than the random walk, the so-called "model of no change '' (Meese and Rogoff, 1983a,b; Diebold and Nason, 1990; Meese and Rose, 1991; Lin and Chen, 1998; Kilian and Taylor, 2001). Several authors have argued that this forecast failure is due to the fact that exchange rate models ignore nonlinearity adjustments to their equilibrium values (e.g.

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Empirical Exchange Rate Models of the Seventies: Are Any Fit to Survive?

Cited by 693 publications

References 0 publications

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Exchange rate exposure in the pre- and post-Euro periods: evidence from Finland

Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models

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