Model Uncertainty and Exchange Rate Volatility*

Markiewicz, Agnieszka

doi:10.1111/j.1468-2354.2012.00702.x

Cited by 29 publications

(20 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every change in the fundamentals' importance (weight) can feed back into the exchange rate and change its behavior. This idea has been formalized only recently, first by Bacchetta and van Wincoop (2004 and more recently by Markiewicz (2012). Bacchetta and van Wincoop (2004 propose the scapegoat theory in which investors have incomplete and heterogeneous information about underlying model parameters.…”

Section: Time-varying Fundamentalsmentioning

confidence: 99%

“…The models developed by Bacchetta and van Wincoop (2004), (2006), and (2013) and Markiewicz (2012) suggest that the exchange rate is typically driven by only a subset of the available fundamentals, namely, those that recently predicted the exchange rate well. We therefore analyze the out-of-sample prediction errors that the exchange rate models made in the last 20 quarters, trying to select those models that are best performing.…”

Section: Model Selection By Backward Eliminationmentioning

confidence: 99%

“…Over time, different observed variables can be taken as scapegoats, so that the weights attributed to fundamentals become time varying. Markiewicz (2012) proposes an alternative framework based on model learning theory. In this framework, investors do not know the set of fundamentals in the true exchange rate model, and they deal with this uncertainty by estimating a large model with all fundamentals and its parameters with the available historical data.…”

Section: Time-varying Fundamentalsmentioning

confidence: 99%

“…This article is closely related to the theoretical literature on imperfect knowledge, recently introduced in the context of exchange rate modeling by Bacchetta and van Wincoop (2004 and Markiewicz (2012). Bacchetta and van Wincoop propose a framework in which investors attribute exchange rate movements to a scapegoat variable, due to incomplete information.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model Uncertainty and Exchange Rate Forecasting

Kouwenberg¹,

Markiewicz²,

Verhoeks³

et al. 2017

J. Financ. Quant. Anal.

Self Cite

View full text Add to dashboard Cite

Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show that the forecasts made by this rule significantly beat a random walk for 5 out of 10 currencies. Furthermore, the currency forecasts generate meaningful investment profits. We demonstrate that the strong performance of the model selection rule is driven by time-varying weights attached to a small set of fundamentals, in line with theory.

show abstract

Section: Time-varying Fundamentalsmentioning

confidence: 99%

Section: Model Selection By Backward Eliminationmentioning

confidence: 99%

Section: Time-varying Fundamentalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model Uncertainty and Exchange Rate Forecasting

Kouwenberg¹,

Markiewicz²,

Verhoeks³

et al. 2017

J. Financ. Quant. Anal.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another, potentially complementary, approach to exchange-rate modeling is based on dynamic predictor selection, see De Grauwe and Grimaldi (2006). Further applications of learning to exchange rates include Kasa (2004), Mark (2007), and Markiewicz (2010). …”

Section: Asset Pricing and Learningmentioning

confidence: 99%

2. Learning as a Rational Foundation for Macroeconomics and Finance

Evans

Honkapohja

2013

Rethinking Expectations

View full text Add to dashboard Cite

Expectations play a central role in modern macroeconomics. The econometric learning approach, in line with the cognitive consistency principle, models agents as forming expectations by estimating and updating subjective forecasting models in real time. This approach provides a stability test for RE equilibria and a selection criterion in models with multiple equilibria. Further features of learning, such as discounting of older data, use of misspecified models, or heterogeneous choice by agents between competing models, generate novel learning dynamics. Empirical applications are reviewed and the roles of the planning horizon and structural knowledge are discussed. We develop several applications of learning to macroeconomic policy: the scope of Ricardian equivalence, appropriate specification of interest-rate rules, implementation of price-level targeting to achieve learning-stability of the optimal RE equilibrium and whether under learning price-level targeting can rule out the deflation trap at the zero-lower-bound.

show abstract

Exchange rate predictability and dynamic Bayesian learning

Beckmann

Koop

Korobilis

et al. 2020

J of Applied Econometrics

View full text Add to dashboard Cite

We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross-section.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Model Uncertainty and Exchange Rate Volatility*

Cited by 29 publications

References 73 publications

Model Uncertainty and Exchange Rate Forecasting

Model Uncertainty and Exchange Rate Forecasting

2. Learning as a Rational Foundation for Macroeconomics and Finance

Exchange rate predictability and dynamic Bayesian learning

Contact Info

Product

Resources

About