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

By Yin-Wong Cheung, M. Chinn, Antonio I. Garcia Pascual. Social Science Research Network, 2003

703 citations recorded by the source corpus.

Previous assessments of forecasting performance of exchange rate models have focused upon a narrow set of models typically of the 1970’s vintage. The canonical papers in this literature are by Meese and Rogoff (1983, 1988), who examined monetary and portfolio balance models. Succeeding works by Mark (1995) and Chinn and Meese (1995) focused on similar models. In this paper we re-assess exchange rate prediction using a wider set of models that have been proposed in the last decade: interest rate parity, productivity based models, and a composite specification incorporating the real interest differential, portfolio balance and nontradables price channels. The performance of these models is compared against two reference specifications – the purchasing power parity and the Dornbusch-Frankel sticky price monetary model. The models are estimated in error correction and first-difference specifications. Rather than estimating the cointegrating vector over the entire sample and treating it as part of the ex ante information set as is commonly done in the literature, we also update the cointegrating vector, thereby generating true ex ante forecasts. We examine model performance at various forecast horizons (1 quarter, 4 quarters, 20 quarters) using differing metrics (mean squared error, direction of change), as well as the “consistency” test of Cheung and Chinn (1998). No model consistently outperforms a random walk, by a mean squared error measure; however, along a direction-of-change dimension, certain structural models do outperform a random walk with statistical significance. Moreover, one finds that these forecasts are cointegrated with the actual values of exchange rates, although in a large number of cases, the elasticity of the forecasts with respect to the actual values is different from unity. Overall, model/specification/currency combinations that work well in one period will not necessarily work well in another period.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/28e80b94fe79b44599da9b4d72a0977ca08ad5d6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

Evaluating Direct Multistep Forecasts (2005, methodology): “As described in studies such as Clements and Hendry (1996), Schorfheide (2005), Chevillon and Hendry (2005), and Marcellino et al. (2004), an alternative approach is to form multistep forecasts by iterating forward projections from one step ahead models. Both methods have pros and cons, reviewed in ”
Order Flow and Exchange Rate Dynamics in Brazil (2004, neutral): “Thornton, 1989; Christensen, 2000; Fullerton and Calderon, 2001, Karame, Patureau, and Sopraseith, 2002; Chinn and Meredith, 2002; Cheung, Chinn and Pascual, 2003; Engel and West, 2003).”
Taylor Rules with Real-Time Data: A Tale of Two Countries and One Exchange Rate (2008, background): “Cheung, Chinn, and Pascual (2005), for example, conclude that no model does statistically better than the random walk.”
Can Oil Prices Forecast Exchange Rates (2011, positive): “…this section focuses on the contemporaneous predictive content of oil prices, based on realized oil prices as predictors in the out-of-sample forecasting exercise, the next section veri es the robustness of the results to actual ex-ante predictive content by using lagged oil prices as predictors.”
Exchange Rate Predictability (2013, neutral): “Note that Cheung et al. (2005) also consider the model with productivity (m; y; i; z) as well as a model with net foreign assets (p; r; b; terms of trade, net foreign assets and the relative price of non-tradeables).”
The Economic Crisis is a Crisis for Economic Theory (2010, neutral): “…by assumption, no place for such heterogeneity and many authors have suggested that this is the reason for the poor predictive power of such models, (evidence for the latter was already given by Meese and Rogoff (1983),and somewhat more recently by Frankel and Rose (1995) and Cheung et al.(2002)).”
Using out-of-sample mean squared prediction errors to test the Martingale difference hypothesis (2006, neutral): “One part of this literature then proceeds to use those critical values, albeit sometimes apologetically (e.g., Clarida et al., 2003; Cheung et al., 2003 ).”
On the sources of uncertainty in exchange rate predictability (2018, neutral): “…regressions of the following state space form: ∆et+h = Xtθt + vt+h, vt+h ∼ N(0, Vt), (observation equation); (1) 5See, for example, Byrne et al. (2016), Cheung et al. (2005), Engel et al. (2007), Mark (1995), Molodtsova and Papell (2009), Rossi and Inoue (2012), and Rossi (2013). θt = θt−1 +$t, $t…”
Testing Uncovered Interest Parity at Short and Long Horizons During the Post-Bretton Woods Era (2002, neutral): “14 Cheung et al. (forthcoming) find that UIP performs best at predicting exchange rates at”
Getting Beyond Carry Trade: What Makes a Safe Haven Currency? (2011, positive): “(4) VIX ̇UC (quantity of risk from VIX) 0.”
Factor Model Forecasts of Exchange Rates (2015, neutral): “As in Table 4 in the paper, the estimation method is maximum likelihood.”
Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive? (2004, neutral): “They can be found in the working paper version of this article (Cheung et al., 2003).”