An EMS target zone model in discrete time

The discrete time analogue of the continuous time Krugman target zone model is developed in order to capture the typical volatility clusters and fat-tailed distributed innovations of exchange rates. It is shown that under these more general stochastic conditions the S-shaped relation between exchange rate and fundamentals is preserved, but is less pronounced. The model is tested for its S-shape and stochastic properties. Two clearly distinct sets of EMS currencies are detected on the basis of the curvature features. One-step-ahead realignment probabilities are used as an alternative evaluation method. © 1998 John Wiley & Sons, Ltd.     

A flexible parametric GARCH model with an application to exchange rates

Many asset prices, including exchange rates, exhibit periods of stability punctuated by infrequent, substantial, often one‐sided adjustments. Statistically, this generates empirical distributions of exchange rate changes that exhibit high peaks, long tails, and skewness. This paper introduces a GARCH model, with a flexible parametric error distribution based on the exponential generalized beta (EGB) family of distributions. Applied to daily US dollar exchange rate data for six major currencies, evidence based on a comparison of actual and predicted higher‐order moments and goodness‐of‐fit tests favours the GARCH‐EGB2 model over more conventional GARCH‐t and EGARCH‐t model alternatives, particularly for exchange rate data characterized by skewness. Copyright © 2001 John Wiley & Sons, Ltd.     

A model of dynamic tail dependence between crude oil prices and exchange rates

We explore the tail dependence between crude oil prices and exchange rates via a dynamic quantile association regression model based on the flexible Fourier form. This method allows us to describe the quantile dependence between conditional distributions of assets. We first perform simulation exercises to gauge the estimation precision of our model. We then undertake empirical analyses to examine the dynamic relation between crude oil and nine exchange rates. We reveal a mildly symmetric tail dependence between these two assets but it increases sharply during the Great Recession of 2008. Further robustness check substantiates the baseline results.     

Model selection in univariate time series forecasting using discriminant analysis

When a large number of time series are to be forecast on a regular basis, as in large scale inventory management or production control, the appropriate choice of a forecast model is important as it has the potential for large cost savings through improved accuracy. A possible solution to this problem is to select one best forecast model for all the series in the dataset. Alternatively one may develop a rule that will select the best model for each series. Fildes (1989) calls the former an aggregate selection rule and the latter an individual selection rule. In this paper we develop an individual selection rule using discriminant analysis and compare its performance to aggregate selection for the quarterly series of the M-Competition data. A number of forecast accuracy measures are used for the evaluation and confidence intervals for them are constructed using bootstrapping. The results indicate that the individual selection rule based on discriminant scores is more accurate, and sometimes significantly so, than any aggregate selection method.     

Forecasting exchange rates using feedforward and recurrent neural networks

In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample mean squared prediction error relative to the random walk model.     

Mexico’s balance of payments and exchange rates: a cointegration analysis

Mexico’s recurrent economic crises have cast serious doubts on the existence of a long-run relationship between the country’s balance-of-payments and exchange rates. In this paper, cointegration and vector autoregression techniques are applied to Mexico’s data covering the period 1971 through 1988. Despite the presence of nonstationarity, the statistical analysis supports a long-run relationship between changes in international reserves and the exchange rate and changes in domestic credit. Further multivariate Granger causality tests, together with innovation accounting, indicate that Mexico’s monetary authorities adjust domestic assets to sterilize balance-of-payments deficits in a futile attempt to control its monetary policy.     

Long-run dynamics of exchange rates: A multi-frequency investigation

The empirical observation that purchasing power parity (PPP) holds in the long run but not in the short run has enjoyed a near-consensus status in international finance literature. However, a similar degree of agreement has not been reached with respect to the exact horizon of this “long run” aspect. To shed light on this matter, a novel approach is adopted in this paper to combine conventional time series methodology with insights from multi-frequency analyses. In particular, we simultaneously explore price-exchange-rate dynamics not only through time, but also at various horizons via a wavelet decomposition. Unit root tests applied to wavelet-based decomposed real exchange rates indicates that PPP holds at horizons consistent with the literature. With respect to the predictive value of our approach, we show that our decomposed measures provide guidance to future movements of real change rates. Additionally, we find that nominal exchange-rate dynamics are dominated by activities corresponding to low frequencies. Results from this study thus enable researchers and practitioners to establish an exchange-rate modelling framework with increased efficiency.     

Models of exchange rates : A comparison of forecasting results

In this note we compare the results of several published papers on exchange rate forecasting. With regard to univariate time series models, we confirm the result that such models, on average, do not outperform the simple random walk forecasting rule. This conclusion corrects results reported in this Journal by Alexander and Thomas (1987).     

Exchange rate target zone models: a Bayesian evaluation

This paper develops a Bayesian approach to estimating exchange rate target zone models and rational expectations models in general. It also introduces a simultaneous-equation target zone model that incorporates stochastic realignment risk. Using FF/DM and IL/DM exchange rate data, we find that the signing of the 1987 Basle–Nyborg Agreement reduces both the magnitude and the likelihood of a central parity realignment, while the lagged exchange rate deviation from its central parity increases them. Furthermore, the interest rate policies and the monetary conditions of the participating countries signal a forthcoming realignment. In general, we are unable to improve upon a simple random walk model in out-of-sample exchange rate prediction by introducing target zone models. Copyright © 1999 John Wiley & Sons, Ltd.     

A simple combination of univariate models

This paper describes the approach that we implemented for producing the point forecasts and prediction intervals for our M4-competition submission. The proposed simple combination of univariate models (SCUM) is a median combination of the point forecasts and prediction intervals of four models, namely exponential smoothing, complex exponential smoothing, automatic autoregressive integrated moving average and dynamic optimised theta. Our submission performed very well in the M4-competition, being ranked 6^th for the point forecasts (with a small difference compared to the 2^nd submission) and prediction intervals and 2^nd and 3^rd for the point forecasts of the weekly and quarterly data respectively.     

The behavior of real exchange rates

Dominating the behavior of real exchange rates for the dollar during the course of the past two and a half decades have been two substantial and for many countries largely offsetting movements. In the years surrounding the breakdown of Bretton Woods most exchange rates fell precipitously and throughout the 1970s remained low. Near the start of the 1980s they began a rise that continued more or less unabated until early 1985. Any explanation of exchange rate behavior over this period, therefore, has to account for both of these movements, not simply the increase in real exchange rates for the dollar in the 1980s that has been the topic of so much discussion in the financial press. The explanation offered in this paper attributes these movements to the two important changes in monetary policy that occurred during these years.     

Threshold cointegration in international exchange rates:A Bayesian approach

This paper considers Bayesian estimation of the threshold vector error correction (TVECM) model in moderate to large dimensions. Using the lagged cointegrating error as a threshold variable gives rise to additional difficulties that typically are solved by utilizing large sample approximations. By relying on Markov chain Monte Carlo methods, we are enabled to circumvent these issues and avoid computationally-prohibitive estimation strategies like the grid search. Due to the proliferation of parameters, we use novel global-local shrinkage priors in the spirit of Griffin and Brown (2010). We illustrate the merits of our approach in an application to five exchange rates vis-á-vis the US dollar by means of a forecasting comparison. Our findings indicate that adopting a non-linear modeling approach improves the predictive accuracy for most currencies relative to a set of simpler benchmark models and the random walk.     

A goal programming model for the siting of multilevel EMS systems.

Facility siting models known as location covering techniques have proven to be useful particularly for emergency medical services (EMS) planning, given the importance of ambulances responding to demand within some maximum time constraint. These models represent a set of methods which focus the health planner's attention on the access of people to health care, since they attempt to “cover” people in need of service within some specified time standard.This research develops a technique for the locational planning of sophisticated EMS systems, characterized by multiple levels of emergency health services. Specifically, a two-tiered system with “basic life support” and “advanced life support” capabilities is modeled as a goal program.By applying location covering techniques within a goal programming framework, this study develops a method for the siting of multilevel EMS systems so that (1) each service level maximizes coverage of its own demand population, and (2) “back-up” coordination between levels is assured. The usefulness of this goal program as a health planning tool is evidenced in the model's explicit articulation of EMS policy objectives and its ability to link system levels in terms of “goal-directed behavior”. The working of this multilevel covering model is demonstrated by reference to EMS planning scenarios and related numerical examples.     

Jump probability using volatility periodicity filters in US Dollar/Euro exchange rates

The discrete daily and intraday jump probabilities of US dollar/euro returns from February 2010 to February 2018 are analyzed using five-minute returns considering several periodicity filters of volatility. When the max outlying statistics are used with Gumbel distribution with periodicity filters such as weighted standard deviation, shortest half scale, and median absolute deviation, the empirical estimates show that the five-minute US dollar/euro returns have lower daily jump probabilities by 13–28% at common critical levels. To detect intraday jumps using the max outlying Gumbel jump statistics, the five-minute US dollar/euro returns have lower daily jump probabilities by 2–10% when the periodicity filters are included at common critical levels. Therefore, when the periodicity filters of volatility are considered, the five-minute US dollar/euro returns have significantly lower daily and intraday jump probabilities than when the periodicity filters are not considered.     

The credibility of the Colombian exchange rate target zone: Its impact over the volatility of interest rate differentials

This paper analyzes the credibility of Colombia’s exchange rate target zone, and in particular its impact on the volatility of interest rate differentials. Bertola and Caballeros’ (1995) model of a target zone with imperfect credibility is used to derive the impact of a reduction in credibility over the variance of the interest rate differential. It is theoretically shown, that as credibility decreases the variance of the interest rate differential increases. This theoretical argument is used to estimate credibility in Colombia’s exchange rate target zone. Using a SWARCH model, the probability of being in a regime with high interest rate volatility is estimated, and is related with events in the exchange rate market. Results suggest that there is evidence of lack of credibility even before the recent international financial turmoil was triggered.     

A comparison of the performance of different univariate forecasting methods in a model of capacity acquisition in UK electricity supply

Forecasting competitions have usually compared the accuracy of different forecasting methods across a number of different time series. This paper describes a study of the application of ten forecasting methods to a single time series: that of peak electricity demand in England and Wales. The performance measure used, however, is not one of the usual forecast ones, e.g., MSE or MAPS, but a managerial one in that the impact of different forecast methods on the profitability of the Central Electricity Generating Board for England and Wales is assessed using a financial simulation model. As well as examing the effects of forecast method on profitability the effects of two other factors, namely the use of a temperature corrected data series and the impact of log transformation of the data are considered. All these effects are both statistically and practically significant. The results are then examined from a different standpoint: specifically the extent to which the financial impacts of alternative forecast methods can be explained using a number of conventional forecast accuracy measures. This question is of major importance in applications since accuracy is one of the few easily measured characteristics of a potential forecasting method. It is concluded that much, though not all, of the results can be explained by accuracy considerations.     

A decomposition clustering ensemble learning approach for forecasting foreign exchange rates

A decomposition clustering ensemble (DCE) learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition (VMD), the self-organizing map (SOM) network, and the kernel extreme learning machine (KELM). First, the exchange rate time series is decomposed into N subcomponents by the VMD method. Second, each subcomponent series is modeled by the KELM. Third, the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers. Finally, each cluster's ensemble weight is estimated by another KELM, and the final forecasting results are obtained by the corresponding clusters' ensemble weights. The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance, and statistically outperform some other benchmark models in directional and level forecasting accuracy.     

Forecasting the volatility of currency exchange rates

Currency volatility is defined to be the standard deviation of day-to-day changes in the logarithm of the exchange rate. After a discussion of statistical models for exchange rates, the paper describes methods for choosing and assessing volatility forecasts using open, high, low and close prices. Results for DM/$ futures prices at the IMM in Chicago from 1977 to 1983 show high and low prices are valuable when seeking accurate volatility forecasts. The best forecasts are a weighted average of present and past high, low and close prices, with adjustments for weekend and holiday effects. The forecasts can be used to value currency options.     

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.