Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting

To address the nonlinear and non-stationary characteristics of financial time series such as foreign exchange rates, this study proposes a hybrid forecasting model using empirical mode decomposition (EMD) and least squares support vector regression (LSSVR) for foreign exchange rate forecasting. EMD is used to decompose the dynamics of foreign exchange rate into several intrinsic mode function (IMF) components and one residual component. LSSVR is constructed to forecast these IMFs and residual value individually, and then all these forecasted values are aggregated to produce the final forecasted value for foreign exchange rates. Empirical results show that the proposed EMD-LSSVR model outperforms the EMD-ARIMA (autoregressive integrated moving average) as well as the LSSVR and ARIMA models without time series decomposition.     

Professional forecasts of interest rates and exchange rates: Evidence from the Wall Street Journal’s panel of economists

We analyze economists’ forecasts of interest rates and exchange rates from the Wall Street Journal. We find that a majority of economists produced unbiased forecasts but that none predicted directions of changes more accurately than chance. Most economists’ forecast accuracy is statistically indistinguishable from a random walk model in forecasting the Treasury bill rate, but many are significantly worse in forecasting the Treasury bond rate and the exchange rate. We also find systematic forecast heterogeneity, support for strategic models predicting the industry employing the economist matters, and evidence that economists deviate less from the consensus as they age.     

Balassa-Samuelson Theory and Predictability of the US/UK Real Exchange Rate

This paper performs a theory-based forecast of the US/UK real exchange rate. The theory is the Balassa-Samuelson hypothesis that productivity differentials between two countries would determine long-run movements of real exchange rates. The relative income and real exchange rate set a bivariate system, which considers the heteroskedasticity in the real exchange rate movements. The model, to which the Kalman filter and Markov-switching algorithm are applied, is compared with the random walk model and reports significant improvements in forecasting in the medium and long term. [C53, F31]     

Dissecting models' forecasting performance

The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.     

UNOFFICIAL EXCHANGE RATES IN BANGLADESH: A COINTEGRATION ANALYSIS

This article estimates a structural model for unofficial market foreign exchange (forex) rates (E U ) and examines the stability of the forex market in Bangladesh using an autoregressive distributed lag approach to cointegration analysis on quarterly data from 1976Q2–1995Q2. It also compares the in-sample and out-of-sample (from 1995Q3–1999Q2) forecasting performances of the structural model with other timE-series models. With E U as a dependent variable and official exchange rates (E O ) , money supply (M) , the difference between foreign and domestic interest rates (I) , forex reserves relative to imports (Q) and political along with some structural factors (D85) as explanatory variables, a multivariate cointegrated relationship is found in which E O , Q , and I cause an appreciation and M and D85 cause a depreciation in E U . These results imply that the overvaluation of the official exchange rate, increases in money supply, the paucity of official forex reserves, and structural factors are the main causes for the creation of the unofficial market for forex in Bangladesh. Results also reveal that the forex market in Bangladesh is stable during the sample period. The structural model performs well in in-sample prediction, and the random walk model performs best in out-of-sample forecasting.     

A heterogeneous agent exchange rate model with speculators and non-speculators

This paper constructs a heterogeneous agent exchange rate model of speculators and non-speculators from a simple monetary framework. The model replaces rational expectations with an adaptive learning rule that forecasts future exchange rates with an econometric model, and assumes two types of market participants, speculators and non-speculators, that differ by their forecasting model. Speculators employ a correctly specified forecasting model, are relatively short-term oriented, and are subject to momentum and herding effects via an expectation shock; non-speculators utilize a simple forecasting model, have no incentive to be short-term oriented, and are not subject to herding effects. Parameters are calibrated and estimated using the method of simulated moments, and simulation results show that the model is able to replicate foreign exchange market stylized facts better than a model of representative agent rational expectations. Furthermore, the dynamics of the model are shown to derive from both agent heterogeneity and the expectation shock.     

A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

Foreign equity option pricing under stochastic volatility model with double jumps

This paper considers the challenging problem advocated by Huang and Hung (2005), that is to incorporate the stochastic volatility into the foreign equity option pricing. Foreign equity options (quanto options) are contingent claims where the payoff is determined by an equity in one currency but the actual payoff is done in another currency. Huang and Hung (2005) priced foreign equity options under the Lévy processes. In Huang and Hung's paper, they considered jumps in the foreign asset prices and exchange rates and assumed the volatility as constant. However, many studies showed that constant volatility and jumps in returns are incapable of fully capturing the empirical features of equity returns or option prices. In this paper, the stochastic volatility with simultaneous jumps in prices and volatility is proposed to model foreign asset prices and exchange rates. The foreign equity option pricing formula is given by using the Fourier inverse transformation. The numerical results show that the use of stochastic volatility with simultaneous jumps in prices and volatility proposed to model foreign asset prices and exchange rates is necessary and this approach can help us to capture more accurately the foreign equity option prices.     

Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

Volatility forecasting: combinations of realized volatility measures and forecasting models

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.     

Election Surprises and Exchange Rate Uncertainty

This papers shows that unexpected election results explain some of the unexpected variation in foreign exchange rates. The result is based on an event study which examines the behavior of the size of forecast errors implied by futures contracts for exchange rates around elections. Though elections can produce large unexpected effects on exchange rates, the effects on forecast errors are short-lived. [ JEL codes: D72, F31.]     

D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy

This work proposes a new forecasting model to analyse the economic development of Sichuan province of China. The model, which introduces the concept of diversity, is based on an improvement of the -GMDH algorithm. The new method, called D-GMDH, is compared with two ensemble approaches which are introduced by Dutta (2009), and D-GMDH is better than the two approaches in forecasting accuracy. D-GMDH is also applied to forecast the industrial added value of the Sichuan province. The obtained results are compared with those of the traditional GMDH model, GMDH combination model and the widely used ARMA model. The results show that D-GMDH has good prediction accuracy and is an effective means for economic forecasting when data is contaminated by noise.     

Asymmetric loss and herding behaviour of exchange rate forecasters: evidence from South Africa

This study examines the asymmetry of the loss function for private forecasters in exchange rate forecasts of the South African rand. It tests rationality under the possibility of an asymmetric loss function. The results indicate less evidence of asymmetry for a horizon of 1 month but considerable evidence of asymmetry for a horizon of 3 months. However, the shapes of the distributions formed by estimated asymmetry parameters of sub-samples for each forecaster are symmetric, regardless of the forecast horizons, which implies that these forecasters do not herd or antiherd. In fact, the results of our empirical herding test show that forecasters neither herd nor antiherd, which is in sharp contrast to recent findings on antiherding for foreign exchange rates in emerging market economies. Our findings provide consistent evidence for a recent suggestion that antiherding might result in the rejection of rationality, even under asymmetric loss functions. Our findings also suggest that central bank transparency might be associated with herding behaviours.     

Forecasting and combining competing models of exchange rate determination

This article investigates the out-of-sample forecast performance of a set of competing models of exchange rate determination. We compare standard linear models with models that characterize the relationship between exchange rate and the underlying fundamentals by nonlinear dynamics. Linear models tend to outperform at short forecast horizons especially when deviations from long-term equilibrium are small. In contrast, nonlinear models with more elaborate mean-reverting components dominate at longer horizons especially when deviations from long-term equilibrium are large. The results also suggest that combining different forecasting procedures generally produces more accurate forecasts than can be attained from a single model.     

Foreign Direct Investment in Australia Following the Australia–US Free Trade Agreement

A model of inward foreign direct investment for Australia is estimated. Foreign direct investment is found to be positively related to economic and productivity growth and negatively related to foreign portfolio investment, trade openness, the exchange rate and the foreign real interest rate. Foreign direct investment is found to be a substitute for both portfolio investment and trade in goods and services. The exchange rate and the US bond rate affect foreign direct investment through the relative attractiveness of domestic assets. Actual foreign direct investment outperforms a model‐derived forecast in recent years, consistent with the liberalisation of foreign investment screening rules following the Australia–US Free Trade Agreement.     

Forecaster Rationality and Expectation Formation in Foreign Exchange Markets: Do Emerging Countries Differ from Industrialized Economies?

ABSTRACT

This paper uses the Consensus Economic Forecast poll to investigate how forecasters in the foreign exchange market form expectations and whether the expectation formation process differs between industrialized and emerging countries. In order to explain the expectation formation of forecasters in countries and country groups, we analyze around 50,000 forecasts for 22 OECD member currencies. We find that differences between the way forecasters in industrialized countries and emerging countries form exchange rate expectations. However, we show that one important difference is due to a difference in forecasting behavior of emerging countries. Controlling for this feature lets the forecasting behavior in emerging countries resemble more the ones found for industrialized countries, but not for all forecast horizons.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

基于小波自适应支持向量机的短期电价预测

电价预测对于发电商、供电企业以及市场监管者都具有重要的意义。提出一种小波自适应支持向量机预测模型,先将电价时间序列作小波分解得到低频和高频分量,再采用自适应调整法,自动地为支持向量机选择较好的参数对电价小波分量逐一预测,最后通过小波重构得到电价最终预测结果。实例证明前述方法得到的预测精度高于BP、RBF、SVM等传统预测模型。     

UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.