The Monetary Model Strikes Back: Evidence From the World

Cerra, Valerie; Saxena, Sweta

doi:10.5089/9781451869354.001

Cited by 3 publications

(8 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frankel and Rose () initiated a literature on the panel data analysis of PPP, which is surveyed by Caporale and Cerrato (). Cerra and Saxena () employed a panel data set with a large number (98) of countries in a study of the monetary model of exchange rates.…”

mentioning

confidence: 99%

Identifying Exchange Rate Common Factors

Greenaway‐McGrevy

Mark

Sul

et al. 2018

Int Economic Review

View full text Add to dashboard Cite

Using recently developed model selection procedures, we determine that exchange rate returns are driven by a two‐factor model. We identify them as a dollar factor and a euro factor. Exchange rates are thus driven by global, U.S., and euro‐zone stochastic discount factors. The identified factors can also be given a risk‐based interpretation. Identification motivates multilateral models for bilateral exchange rates. Out‐of‐sample forecast accuracy of empirically identified multilateral models dominates the random walk and a bilateral purchasing power parity fundamentals prediction model. Twenty‐four‐month‐ahead forecast accuracy of the multilateral model dominates those of a principal components forecasting model.

show abstract

mentioning

confidence: 99%

Identifying Exchange Rate Common Factors

Greenaway‐McGrevy

Mark

Sul

et al. 2018

Int Economic Review

View full text Add to dashboard Cite

show abstract

“…Rossi, ; Bacchetta et al ., ). Furthermore, we have not undertaken the cross‐sectional dependence in the regression model that has been found to yield superior forecast performance of the exchange rate models such as the panel model in Cerra and Saxena () and Wu and Wang () and the factor model in Engel et al . () and Greenaway‐McCrevy et al .…”

Section: Discussionmentioning

confidence: 99%

“…In addition, another strand of the literature investigates the same relation by accounting for the cross‐sectional dependence in the exchange rate, such as the panel model (e.g. Mark and Sul, ; Groen, ; Cerra and Saxena, ) and the factor model (e.g. Engel et al ., ).…”

Section: Introductionmentioning

confidence: 97%

Exchange Rate Predictability in Finite Samples

2016

The Japanese Economic Review

View full text Add to dashboard Cite

The goal of the present study is to re‐examine the exchange rate predictability with an approach that accounts for the negative effect of the finite‐sample estimation error on forecast accuracy in the in‐sample test. We consider various exchange rate models and find that despite the presence of significant population‐level predictive content in the exchange rate model, the coefficients of the predictive variables could be small enough that, with the available sample, they are estimated so imprecisely that a random walk model can be expected to forecast at least as well as the exchange rate model.

show abstract

“…Their findings indicate that while martingale behavior cannot be rejected for euro exchange rates with the yen, pound and dollar, there is indeed nonlinear predictability in terms of economic criteria with respect to several smaller currencies. Most recently, Cerra and Saxena () revisit the dynamic failure of the monetary models in explaining exchange rate movements by using information content from 98 countries to test for co‐integration between nominal exchange rates and monetary fundamentals. They find robust evidence for co‐integrating relationships, and that fundamentals‐based models are very successful at beating a random walk in out‐of‐sample exchange rate prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a benchmark, inspired by Meese and Rogoff (), our first two forecasting models are: (1) a random walk, and (2) a random walk with drift, in which the drift is estimated using a rolling window of 12 months of data. The random walk with drift specification is reasonable to consider, as do Engel () and Cerra and Saxena (), because the majority of black market episodes have occurred in the context of monetary expansions and display positive tendencies towards depreciation. The three structural models we employ are (3) a structural black market model in levels, (4) a structural black market model in differences and (5) a BTGP model in levels…”

Section: Forecasting Models the Black Market Trading Strategy And Mmentioning

confidence: 99%

Exchange Rate Fundamentals, Forecasting, and Speculation: Bayesian Models in Black Markets

2013

View full text Add to dashboard Cite

SUMMARY Although speculative activity is central to black markets for currency, the out‐of‐sample performance of structural models in those settings is unknown. We substantially update the literature on empirical determinants of black market rates and evaluate the out‐of‐sample performance of linear models and non‐parametric Bayesian treed Gaussian process (BTGP) models against the random walk benchmark. Fundamentals‐based models outperform the benchmark in out‐of‐sample prediction accuracy and trading rule profitability measures given future values of fundamentals. In simulated real‐time trading exercises, however, the BTGP achieves superior realized profitability, accuracy and market timing, while linear models do no better than a random walk. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

The Monetary Model Strikes Back: Evidence From the World

Cited by 3 publications

References 43 publications

Identifying Exchange Rate Common Factors

Identifying Exchange Rate Common Factors

Exchange Rate Predictability in Finite Samples

Exchange Rate Fundamentals, Forecasting, and Speculation: Bayesian Models in Black Markets

Contact Info

Product

Resources

About