Forecasting daily exchange rates : A comparison between SSA and MSSA

• In this paper, daily exchange rates in four of the BRICS emerging economies: Brazil, India, China and South Africa, over the period 2001 to 2015 are considered. In order to predict the future of exchange rate in these countries, it is possible to use both univariate and multivariate time series techniques. Among different time series analysis methods, we choose singular spectrum analysis (SSA), as it is a relatively powerful non-parametric technique and requires the fewest assumptions to be hold in practice. Both multivariate and univariate versions of SSA are considered to predict the daily currency exchange rates. The results show the superiority of MSSA, when compared with univariate SSA, in terms of mean squared error.

[1]  Masoud Yarmohammadi,et al.  Forecasting exchange rates: An optimal approach , 2014, J. Syst. Sci. Complex..

[2]  Lucia Russo,et al.  Can social microblogging be used to forecast intraday exchange rates? , 2013, NETNOMICS: Economic Research and Electronic Networking.

[3]  Anatoly A. Zhigljavsky,et al.  Singular Spectrum Analysis for Time Series , 2013, International Encyclopedia of Statistical Science.

[4]  Mansi Ghodsi,et al.  Exchange rate forecasting with optimum singular spectrum analysis , 2014, J. Syst. Sci. Complex..

[5]  Werner G. Müller,et al.  Prediction of steel prices: A comparison between a conventional regression model and MSSA ∗ , 2010 .

[6]  James M. Nason,et al.  Exchange Rates and Fundamentals: A Generalization , 2008 .

[7]  N. Golyandina,et al.  SSA-based approaches to analysis and forecast of multidimensional time series , 2012 .

[8]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[9]  Dietmar Janetzko,et al.  Using Twitter to Model the EUR/USD Exchange Rate , 2014, ArXiv.

[10]  Anatoly A. Zhigljavsky,et al.  Analysis of Time Series Structure - SSA and Related Techniques , 2001, Monographs on statistics and applied probability.

[11]  Anatoly Zhigljavsky,et al.  Predicting daily exchange rate with singular spectrum analysis , 2010 .

[12]  A. M. Tehranchian,et al.  The Impact of Monetary Policies on the Exchange Rate: A GMM Approach , 2015 .

[13]  Rahim Mahmoudvand,et al.  Forecasting mortality rate by multivariate singular spectrum analysis: Forecasting mortality rate by multivariate singular spectrum analysis , 2017 .

[14]  Herding in China Equity Market , 2010 .

[15]  Emmanuel Sirimal Silva,et al.  Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis , 2015 .

[16]  Kin Keung Lai,et al.  Foreign-Exchange-Rate Forecasting with Artificial Neural Networks , 2007 .

[17]  Rahim Mahmoudvand,et al.  A new parsimonious recurrent forecasting model in singular spectrum analysis , 2018 .

[18]  Abdol S. Soofi,et al.  Predicting inflation dynamics with singular spectrum analysis , 2013 .

[19]  Rahim Mahmoudvand,et al.  Missing value imputation in time series using Singular Spectrum Analysis , 2016 .

[20]  Lucio Sarno,et al.  Spot and Forward Volatility in Foreign Exchange , 2010 .

[21]  Chiun-Sin Lin,et al.  Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting , 2012 .