Forecasting exchange rates with linear and nonlinear models

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however,implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of Forecasting exchange rates with linear and nonlinear models 415 performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.

Monetary models of exchange rates and sweep programs

Numerous studies find that monetary models of exchange rates cannot beat a random walk model. Such a finding, however, is not surprising given that such models are built upon money demand functions and traditional money demand functions appear to have broken down in many developed countries. In this article, we investigate whether using a more stable underlying money demand function results in improvements in forecasts of monetary models of exchange rates. More specifically, we use a sweep-adjusted measure of US monetary aggregate M1 which has been shown to have a more stable money demand function than the official M1 measure. The results suggest that the monetary models of exchange rates contain information about future movements of exchange rates, but the success of such models depends on the stability of money demand functions and the specifications of the models.

Volatility changes in drachma exchange rates

In January 2001 Greece joined the eurozone. The aim of this article is to examine whether an intention to join the eurozone had any impact on exchange rate volatility. We apply the Iterated Cumulative Sum of Squares (ICSS) algorithm of Inclan and Tiao (1994) to a set of Greek drachma exchange rate changes. We find evidence to suggest that the unconditional volatility of the drachma exchange rate against the dollar, British pound, yen, German mark and ECU/Euro was nonstationary, exhibiting a large number of volatility changes prior to European Monetary Union (EMU) membership. We then use a news archive service to identify the events that might have caused exchange rate volatility to shift. We find that devaluation of the drachma increased exchange rate volatility but ERM membership and a commitment to joining the eurozone led to lower volatility. Our findings therefore suggest that a strong commitment to join the eurozone may be sufficient to reduce some exchange rate volatility which has implications for countries intending to join the eurozone in the future.

Model specification and forecasting foreign exchange rates with vector autoregressions

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak.

Forecasting exchange rates with linear and nonlinear models

In this paper the exchange rate forecasting performance of neural network models are evaluated against random walk and a range of time series models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high which could explain why in many studies neural network models do not consistently perform better than their time series counterparts. In this paper through extensive experimentation the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. Our results show that in general neural network models perform better than traditionally used time series models in forecasting exchange rates.

Monetary models of exchange rates and sweep programs

Numerous studies find that monetary models of exchange rates cannot beat a random walk model. Such a finding, however, is not surprising given that such models are built upon money demand functions and traditional money demand functions appear to have broken down in many developed countries. In this paper we investigate whether using a more stable underlying money demand function results in improvements in forecasts of monetary models of exchange rates. More specifically, we use a sweepadjusted measure of US monetary aggregate M1 which has been shown to have a more stable money demand function than the official M1 measure. The results suggest that the monetary models of exchange rates contain information about future movements of exchange rates but the success of such models depends on the stability of money demand functions and the specifications of the models.

Kelet-közép-európai devizaárfolyamok előrejelzése határidős árfolyamok segítségével (Forecasting the exchange rates of three Central-Eastern European currencies with forward exchange rates)

Írásunkban azt vizsgáljuk, hogy a hosszú lejáratú határidős árfolyamok stacionaritását feltételező hibakorrekciós modellek, amelyeknek korábbi számítások szerint - a világ devizapiaci forgalmának mintegy 75 százalékát kitevő fejlett ipari országokra alkalmazva - kitűnő a mintán kívüli előrejelző erejük, hogyan képesek három keletközép- európai ország devizaárfolyamát előrejelezni. A három vizsgálat alá vont deviza (cseh, magyar, lengyel) esetében az eredmények relációnként nagyon eltérnek, és összességében kedvezőtlenebbek, mint a fejlett ipari országokra kapott eredmények, amit a nem teljesen rugalmas árfolyamrezsim, a rendelkezésre álló adatsor rövidsége, az eurózóna-csatlakozáshoz kapcsolódó bizonytalanságok, a devizakockázati és a határidős kamatprémium létezése, továbbá a Balassa-Samuelson-hatás együttes befolyásaként tudunk értelmezni. JEL kód: E43, F31, F47. /===/ This paper studies whether models that assume long-maturity forward exchange rates are stationary (which proved in earlier studies to provide superior forecasting ability when applied to exchange rates of major currencies) are capable of forecasting the Euro exchange rates of three Central-East European currencies (the Czech koruna, Hungarian forint and Polish zloty). The results for the three currencies differ from each other and are generally much worse than those obtained earlier for major currencies. These unfavourable results are attributed to the consequences of managed exchange-rate systems, to the short time series available, to uncertainties related to future Euro-zone entry, to the existence of a foreign exchange and term premium, and to the Balassa–Samuelson effect.

Real effective exchange rates for 178 countries: A new database

We use data on exchange rates and consumer price indices and the weighting matrix derived by Bayoumi, Lee and Jaewoo (2006) to calculate consumer price index-based REER. The main novelties of our database are that (1) it includes data for 178 countries –many more than in any other publicly available database– plus an external REER for the euro area, using a consistent methodology; (2) it includes up-to-date REER values, such as data for January 2012; and (3) it is relatively easy to calculate REER against any arbitrary group of countries. The annual database is complete for 172 countries and the euro area for 1992-2011 and data is available for six other countries for a shorter period. For several countries annual data is available for earlier years as well, eg data is available for 67 countries from 1960. The monthly database is complete for 138 countries for January 1995-January 2012, and data is also available for 15 other countries for a shorter period. The indicators calculated by us are freely downloadable and will be irregularly updated.

Empirical study of exchange rates in Brazil, Canada and the Caribbean

This dissertation examines the monetary models of exchange rate determination for Brazil, Canada, and two countries in the Caribbean, namely, the Dominican Republic and Jamaica. With the exception of Canada, the others adopted the floating regime during the past ten years.^ The empirical validity of four seminal models in exchange rate economics were determined. Three of these models were entirely classical (Bilson and Frenkel) or Keynesian (Dornbusch) in nature. The fourth model (Real Interest Differential Model) was a mixture of the two schools of economic theory.^ There is no clear empirical evidence of the validity of the monetary models. However, the signs of the coefficients of the nominal interest differential variable were as predicted by the Keynesian hypothesis in the case of Canada and as predicted by the Chicago theorists in the remaining countries. Moreover, in case of Brazil, due to hyperinflation, the exchange rate is heavily influenced by domestic money supply.^ I also tested the purchasing power parity (PPP) for this same set of countries. For both the monetary as well as the PPP hypothesis, I tested for co-integration and applied ordinary least squares estimation procedure. The error correction model was also used for the PPP model, to determine convergence to equilibrium.^ The validity of PPP is also questionable for my set of countries. Endogeinity among the regressors as well as the lack of proper price indices are the contributing factors. More importantly, Central Bank intervention negate rapid adjustment of price and exchange rates to their equilibrium value. However, its forecasting capability for the period 1993-1994 is superior compared to the monetary models in two of the four cases.^ I conclude that in spite of the questionable validity of these models, the monetary models give better results in the case of the "smaller" economies like the Dominican Republic and Jamaica where monetary influences swamp the other determinants of exchange rate. ^

Forecasting exchange rates in the presence of instabilities

Many exchange rate papers articulate the view that instabilities constitute a major impediment to exchange rate predictability. In this thesis we implement Bayesian and other techniques to account for such instabilities, and examine some of the main obstacles to exchange rate models' predictive ability. We first consider in Chapter 2 a time-varying parameter model in which fluctuations in exchange rates are related to short-term nominal interest rates ensuing from monetary policy rules, such as Taylor rules. Unlike the existing exchange rate studies, the parameters of our Taylor rules are allowed to change over time, in light of the widespread evidence of shifts in fundamentals - for example in the aftermath of the Global Financial Crisis. Focusing on quarterly data frequency from the crisis, we detect forecast improvements upon a random walk (RW) benchmark for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the time-varying parameters of the Taylor rules to differ between countries. In Chapter 3 we look closely at the role of time-variation in parameters and other sources of uncertainty in hindering exchange rate models' predictive power. We apply a Bayesian setup that incorporates the notion that the relevant set of exchange rate determinants and their corresponding coefficients, change over time. Using statistical and economic measures of performance, we first find that predictive models which allow for sudden, rather than smooth, changes in the coefficients yield significant forecast improvements and economic gains at horizons beyond 1-month. At shorter horizons, however, our methods fail to forecast better than the RW. And we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients variability to incorporate in the models, as the main factors obstructing predictive ability. Chapter 4 focus on the problem of the time-varying predictive ability of economic fundamentals for exchange rates. It uses bootstrap-based methods to uncover the time-specific conditioning information for predicting fluctuations in exchange rates. Employing several metrics for statistical and economic evaluation of forecasting performance, we find that our approach based on pre-selecting and validating fundamentals across bootstrap replications generates more accurate forecasts than the RW. The approach, known as bumping, robustly reveals parsimonious models with out-of-sample predictive power at 1-month horizon; and outperforms alternative methods, including Bayesian, bagging, and standard forecast combinations. Chapter 5 exploits the predictive content of daily commodity prices for monthly commodity-currency exchange rates. It builds on the idea that the effect of daily commodity price fluctuations on commodity currencies is short-lived, and therefore harder to pin down at low frequencies. Using MIxed DAta Sampling (MIDAS) models, and Bayesian estimation methods to account for time-variation in predictive ability, the chapter demonstrates the usefulness of suitably exploiting such short-lived effects in improving exchange rate forecasts. It further shows that the usual low-frequency predictors, such as money supplies and interest rates differentials, typically receive little support from the data at monthly frequency, whereas MIDAS models featuring daily commodity prices are highly likely. The chapter also introduces the random walk Metropolis-Hastings technique as a new tool to estimate MIDAS regressions.

Cambium non est mutuum: exchange and interest rates in medieval Europe

A major gap in our understanding of the medieval economy concerns interest rates, especially relating to commercial credit. Although direct evidence about interest rates is scattered and anecdotal, there is much more surviving information about exchange rates. Since both contemporaries and historians have suggested that exchange and rechange transactions could be used to disguise the charging of interest in order to circumvent the usury prohibition, it should be possible to back out the interest rates from exchange rates. The following analysis is based on a new dataset of medieval exchange rates collected from commercial correspondence in the archive of Francesco di Marco Datini of Prato, c.1383-1411. It demonstrates that the time value of money was consistently incorporated into market exchange rates. Moreover, these implicit interest rates are broadly comparable to those received from other types of commercial loan and investment. Although on average profitable, the return on any individual exchange and rechange transaction did involve a degree of uncertainty that may have justified their non-usurious nature. However, there were also practical reasons why medieval merchants may have used foreign exchange transactions as a means of extending credit.

Tailoring of ion species composition in complex plasmas with charge exchange collisions

A generic approach towards tailoring of ion species composition in reactive plasmas used for nanofabrication of various functional nanofilms and nanoassemblies, based on a simplified model of a parallel-plate rf discharge, is proposed. The model includes an idealized reactive plasma containing two neutral and two ionic species interacting via charge exchange collisions in the presence of a microdispersed solid component. It is shown that the number densities of the desired ionic species can be efficiently managed by adjusting the dilution of the working gas in a buffer gas, rates of electron impact ionization, losses of plasma species on the discharge walls, and surfaces of fine particles, charge exchange rates, and efficiency of three-body recombination processes in the plasma bulk. The results are relevant to the plasma-aided nanomanufacturing of ordered patterns of carbon nanotip and nanopyramid microemitters.

Electron self-exchange reaction of viologen and its derivatives in poly(ethylene glycol)

The electron self-exchange rates (k(ex)) of viologen and its derivatives are estimated by using microelectrode voltammetry in poly(ethylene glycol) films. The dependences of supporting electrolyte concentration and sizes of viologen and its derivatives on k(ex) and diffusion coefficients (D) are discussed. Results show that k(ex) increases with the decrease of supporting electrolyte concentration and sizes of reactants. (C) 2000 Elsevier Science S.A. All rights reserved.

Essays on Vietnam’s Financial Reforms: Foreign Exchange Statistics and Evidence of Long-Run Equilibrium

In this paper, we examine exchange rates in Vietnam’s transitional economy. Evidence of long-run equilibrium are established in most cases through a single co-integrating vector among endogenous variables that determine the real exchange rates. This supports relative PPP in which ECT of the system can be combined linearly into a stationary process, reducing deviation from PPP in the long run. Restricted coefficient vectors ß’ = (1, 1, -1) for real exchange rates of currencies in question are not rejected. This empirics of relative PPP adds to found evidences by many researchers, including Flre et al. (1999), Lee (1999), Johnson (1990), Culver and Papell (1999), Cuddington and Liang (2001). Instead of testing for different time series on a common base currency, we use different base currencies (USD, GBP, JPY and EUR). By doing so we want to know the whether theory may posit significant differences against one currency? We have found consensus, given inevitable technical differences, even with smallerdata sample for EUR. Speeds of convergence to PPP and adjustment are faster compared to results from other researches for developed economies, using both observed and bootstrapped HL measures. Perhaps, a better explanation is the adjustment from hyperinflation period, after which the theory indicates that adjusting process actually accelerates. We observe that deviation appears to have been large in early stages of the reform, mostly overvaluation. Over time, its correction took place leading significant deviations to gradually disappear.