Exchange rate forecasting and the performance of currency portfolios

• Author: Ines Fortin,Jesus Crespo Cuaresma,Jaroslava Hlouskova
• Published date: 01 August 2018
• Date: 01 August 2018
• DOI: http://doi.org/10.1002/for.2518

Received: 21 October 2016 Revised: 30 January 2018 Accepted: 31 January 2018

DOI: 10.1002/for.2518

RESEARCH ARTICLE

Exchange rate forecasting and the performance of

currency portfolios

Jesus Crespo Cuaresma1,2,3,4 Ines Fortin5Jaroslava Hlouskova5,6,7

1Department of Economics, Vienna

University of Economics and Business

(WU), Vienna, Austria

2Wittgenstein Centre for Demography and

Global Human Capital (WIC), Vienna,

Austria

3World PopulationProgram, International

Institute for Applied Systems Analysis

(IIASA), Laxenburg, Austria

4Austrian Institute of Economic Research

(WIFO), Vienna, Austria

5Research Group Macroeconomics and

Economic Policy,Institute for Advanced

Studies, Vienna, Austria

6Department of Economics, Thompson

Rivers University, Kamloops, BC, Canada

7Ecosystems Services and Management,

International Institute for Applied

Systems Analysis, Laxenburg, Austria

Correspondence

Jesus Crespo Cuaresma, Department of

Economics, Vienna University of

Economics and Business (WU), 1020

Vienna, Austria.

Email: jcrespo@wu.ac.at

Funding information

Austrian Central Bank, Grant/Award

Number: 16250

Abstract

We examine the potential gains of using exchange rate forecast models and

forecast combination methods in the management of currency portfolios for

three exchange rates: the euro versus the US dollar, the British pound, and the

Japanese yen. Weuse a battery of econometric specifications to evaluate whether

optimal currency portfolios implied by trading strategies based on exchange

rate forecasts outperform single currencies and the equally weighted portfo-

lio. We assess the differences in profitability of optimal currency portfolios for

different types of investor preferences, two trading strategies, mean squared

error-based composite forecasts, and different forecast horizons. Our results

indicate that there are clear benefits of integrating exchange rate forecasts from

state-of-the-art econometric models in currency portfolios. These benefits vary

across investor preferences and predictionhorizons but are rather similar across

trading strategies.

KEYWORDS

currency portfolios, exchange rate forecasting, profitability, tradingstrategies

Journal of Forecasting. 2018;37:519–540. wileyonlinelibrary.com/journal/for 519

1INTRODUCTION

Foreign exchange risk is omnipresent in international

portfolio diversification, but forecasting exchange rates is

well known to be a difficult task. Since the seminal work

by Meese and Rogoff (1983), which shows that economet-

ric specifications based on macroeconomic fundamentals

are unable to outperform simple random walk forecasts

at short time horizons (up to 1 year), a large number of

studies have proposed models aimed at providing accu-

rate out-of-sample predictions of spot exchange rates (see,

among others, Berkowitz & Giorgianni, 2001; Boudoukh,

Richardson, & Whitelaw,2008; Cheung, Chinn, & Pascual,

2005; Chinn & Meese, 1995; Kilian, 1999; MacDonald &

Taylor, 1994; Mark, 1995; Mark & Sul, 2001). In parallel,

a literature has emerged which examines empirically the

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520 CRESPO CUARESMA ETAL.

potential profitability of technical trading rules based on

exchange rate predictions (see Menkhoff & Taylor, 2007,

for a review). Although the random walk specification has

naturally emerged as the benchmark to beat in terms of

out-of-sample predictive accuracy, it is not clear that it

will also yield the most profitable trading strategy. Port-

folio managers are expected to be more concerned with

profitability than with out-of-sample accuracy.

Our study aims at addressing how the joint modeling of

exchange rates and fundamentals provide economic value

in terms of improving currency portfolio performance.

We therefore contribute to the long-standing literature on

the use of exchange rate models based on fundamentals

for forecasting, taking a new perspective in the evalua-

tion of different econometric specifications. Weprovide an

evaluation framework where we take the perspective of a

currency portfolio manager (investor) who follows trad-

ing strategies based on exchange rate forecasts and whose

main goal is to maximize (risk-adjusted) profits, under cer-

tain types of preferences. Our currency portfolio manager

considerstheexchangeratesoftheeuroagainsttheUS

dollar (USD), the British pound (GBP), and the Japanese

yen (JPY), and for each of these three exchange rates cre-

ates a “single asset.” The returns of this asset are implied

by a certain trading strategy that is based on exchange rate

forecasts. The optimal portfolio is then made up of these

three single assets according to the manager's—or some

investor's—preferences.

The two primary research questions in our study are

the following. First, does the information on exchange

rate fundamentals provide valuable information to con-

struct optimal currency portfolio that outperform simple

benchmark portfolios, and thus is there a value added in

engaging in active portfolio management—or can the port-

folio manager achieve the same (risk-adjusted) profit by

just investing in some simpler assets (benchmark portfo-

lios)? As simpler assets we consider the single assets of

which the optimal portfolio consists as well as the equally

weighted portfolio based on forecasts from the model

based on macroeconomic fundamentals as well as on ran-

dom walk predictions. This research question links our

work to the large literature on the statistical and economic

evaluation of exchange rate forecasts (see Abbate & Mar-

cellino, 2018; Della Corte, Sarno, & Tsiakas, 2009; Rossi,

2013) and provides a novel evaluation context that goes

beyond the existing methods based on forecast errors and

directional change statistics.

Relating to the first question, there is some empirical

evidence indicating that simple portfolios, like equally

weighted portfolios, are not necessarily outperformed

(e.g., in terms of the Sharpe ratio) by more complex port-

folios (see DeMiguel, Garlappi, & Uppal, 2009; Jacobs,

Müller, & Weber, 2014). The existing evidence in the liter-

ature, however, relates to equity markets (DeMiguel et al.

2009) and equity, bond and commodity markets (Jacobs

et al. 2014), and it is not obvious that these findings

carry over to foreign exchange markets. Our study con-

tributes to enlarge this body of empirical evidence by

concentrating on foreign exchange markets. In order to

compare the different currency portfolios, we employ a

number of (risk-adjusted) performance measures, includ-

ing the Omega measure, the Sharpe ratio, and the Sortino

ratio. We consider all the multivariate time series models

and the methods of forecast combinations entertained in

Costantini, Crespo Cuaresma, and Houskova (2014, 2016)

to generate exchange rate forecasts.1

Weconsider two different trading strategies in construct-

ing the single assets. The first one is the simple “buy

low, sell high” trading strategy described, for example,

in Gençay (1998), where the trading signal is based on

the spot exchange rate and its forecast. The second one

is based on exploiting the forward rate unbiased expec-

tation hypothesis, using forward contracts, and is similar

to the carry trade strategy used, for example, in Burn-

side, Eichenbaum, and Rebelo (2008). In this case the

trading signal is based on the forward exchange rate and

the exchange rate forecast. In order to assess the perfor-

mance of optimal currency portfolios versus benchmark

portfolios (single assets, equally weighted portfolio), we

use a data-snooping bias-free test, which is based on an

extensive bootstrap procedure. By employing this test we

ensure that the performance superiority of certain opti-

mal portfolios—if any—is systematic and not merely due

to luck. The test identifies which optimal portfolios sig-

nificantly beat the benchmark portfolio in terms of cer-

tain risk-adjusted performance measures. In addition, we

assess the values of optimal portfolios with respect to

benchmark portfolios by computing break-even transac-

tion costs (see Della Corte et al., 2009; Della Corte &

Tsiakas, 2013).

Returns implied by trading strategies have also been

investigated in other exchange rate studies. Burnside et al.

(2008), for example, examine the returns implied by the

carry trade strategy, which determines to sell (buy) a cur-

rency forward when it trades at a forward premium (dis-

count). This trading strategy is similar to our second trad-

ing strategy. The authors apply the carry trade strategy to

individual currencies as well as to an equally weighted

portfolio of 23 currencies and find that constructing a

portfolio improves the performance of the carry trade strat-

egy substantially: the Sharpe ratio of the equally weighted

carry trade strategy is more than 50% higher than the

1See also Crespo Cuaresma and Hlouskova (2005), Crespo Cuaresma

(2007), Costantini and Pappalardo (2010), and Costantini and Kunst

(2011).