Nonlinear prediction of exchange rates with monetary fundamentals

Qi, Meiling; Wu, Yangru

doi:10.1016/s0927-5398(03)00008-2

Cited by 91 publications

(65 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only a few papers focus on non-linear models, including: non-parametric methods (locally-weighted regressions), as in Diebold and Nason (1990), Meese and Rose (1991), Chinn (1991), Mizrach (1992) and Chinn and Meese (1995); neural networks, as in by Qi and Wu (2003); or (exponential) transition autoregressive models (ESTAR), as in Rapach and Wohar (2006). Again, the literature di¤ers regarding whether actual, realized fundamentals or forecasted fundamentals are used -see Table 2.…”

Section: Non-linear Modelsmentioning

confidence: 99%

“…39 Including Chinn (1991) and Chinn and Meese (1995) for the monetary model; Diebold and Nason (1990) for univariate models; Mizrach (1992) for locally weighted regression model across several currencies; Qi and Wu (2003) and Rapach and Wohar (2006). 40 Meese and Rose (1991) …nd signi…cant in-sample and out-of-sample predictability in the non-linear monetary model.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Exchange Rate Predictability

Rossi

2013

Journal of Economic Literature

520

333

View full text Add to dashboard Cite

The main goal of this article is to provide an answer to the question: "Does anything forecast exchange rates, and if so, which variables?". It is well known that exchange rate ‡uctuations are very di¢ cult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogo¤ puzzle). However, the recent literature has identi…ed a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an upto-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: "Are exchange rates predictable?" is, "It depends" -on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift.

show abstract

Section: Non-linear Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

Exchange Rate Predictability

Rossi

2013

Journal of Economic Literature

520

333

View full text Add to dashboard Cite

show abstract

“…It is well known that financial markets, and particularly time series describing returns on financial instruments, involve terms that are not first order. There is now strong evidence of the existence of non-linear dynamics in stock returns [30][31][32][33][34], market index returns [35][36][37][38][39], and currency exchange rate changes [30,[40][41][42][43]. Meanwhile Pearson's correlation coefficient is strictly not sensitive to any non-linear dependencies.…”

Section: Introductionmentioning

confidence: 99%

Information-theoretic approach to lead-lag effect on financial markets

Fiedor

2014

Eur. Phys. J. B

View full text Add to dashboard Cite

Abstract.Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both types of analysis are concentrated mostly on Pearson's correlation coefficient and consequently intraday lead-lag relationships (where one of the variables in a pair is time-lagged) are also associated with them. Under the Efficient-Market Hypothesis such relationships are not possible as all information is embedded in the prices, but in real markets we find such dependencies. In this paper we analyse lead-lag relationships of financial instruments and extend known methodology by using mutual information instead of Pearson's correlation coefficient. Mutual information is not only a more general measure, sensitive to non-linear dependencies, but also can lead to a simpler procedure of statistical validation of links between financial instruments. We analyse lagged relationships using New York Stock Exchange 100 data not only on an intraday level, but also for daily stock returns, which have usually been ignored.

show abstract

“…Even after including ex-post data on the fundamentals, out-of-sample forecasting performance at 1-, 6-and 12-month horizons was surprisingly low. These findings were reinforced by a number of authors such as Baxter and Stockman (1989), Meese and Rose (1991), Flood and Rose (1995), Frankel and Rose (1995) and, very recently, Qi and Wu (2003). Contrary to this literature, a more favorable evidence was found in Mark (1995), Chinn and Meese (1995), Kilian and Taylor (2003) and Mark and Sul (2001).…”

Section: Introductionmentioning

confidence: 80%

“…While there has been a significant criticism related to the statistical robustness of these results (e.g., Kilian, 1999), it has become apparent that some of the gains in the forecasting performance were due to accounting for non-linearities in the data (e.g., Kilian and Taylor, 2003). Noteworthy, Meese and Rose (1991) and Qi and Wu (2003) did not find non-linearities and market fundamentals useful for lower frequency forecasting. Obstfeld and Rogoff (2000) referred to this kind of weak relationship between the exchange rate and market fundamentals as the "exchange rate disconnect puzzle."…”

Section: Introductionmentioning

confidence: 99%