This paper examines the importance of forecasting higher moments for optimal hedge ratio estimation. To this end, autoregressive conditional density (ARCD) models are employed which allow for time variation in variance, skewness and kurtosis. The performance of ARCD models is evaluated against that of GARCH and of other conventional hedge ratio estimation methodologies based on exponentially weighted moving averages, ordinary least squares and error correction, respectively. An empirical application using spot and futures data on the DJI, FTSE and DAX equity indices compares the in-sample and out-of-sample hedging effectiveness of each approach in terms of risk minimization. The results show that the ARCD approach has the best performance, thus suggesting that forecasting higher moments is of practical importance for futures hedging. Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes an adjustment of linear autoregressive conditional mean forecasts that exploits the predictive content of uncorrelated model residuals. The adjustment is motivated by non-Gaussian characteristics of model residuals, and implemented in a semiparametric fashion by means of conditional moments of simulated bivariate distributions. A pseudo ex ante forecasting comparison is conducted for a set of 494 macroeconomic time series recently collected by Dees et al. (Journal of Applied Econometrics 2007; 22: 1–38). In total, 10,374 time series realizations are contrasted against competing short-, medium- and longer-term purely autoregressive and adjusted predictors. With regard to all forecast horizons, the adjusted predictions consistently outperform conditionally Gaussian forecasts according to cross-sectional mean group evaluation of absolute forecast errors and directional accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

A risk management strategy designed to be robust to the global financial crisis (GFC), in the sense of selecting a value-at-risk (VaR) forecast that combines the forecasts of different VaR models, was proposed by McAleer and coworkers in 2010. The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. Such a risk management strategy is robust to the GFC in the sense that, while maintaining the same risk management strategy before, during and after a financial crisis, it will lead to comparatively low daily capital charges and violation penalties for the entire period. This paper presents evidence to support the claim that the median point forecast of VaR is generally GFC robust. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. In the empirical analysis we choose several major indexes, namely French CAC, German DAX, US Dow Jones, UK FTSE100, Hong Kong Hang Seng, Spanish Ibex 35, Japanese Nikkei, Swiss SMI and US S&P 500. The GARCH, EGARCH, GJR and RiskMetrics models as well as several other strategies, are used in the comparison. Backtesting is performed on each of these indexes using the Basel II Accord regulations for 2008–10 to examine the performance of the median strategy in terms of the number of violations and daily capital charges, among other criteria. The median is shown to be a profitable and safe strategy for risk management, both in calm and turbulent periods, as it provides a reasonable number of violations and daily capital charges. The median also performs well when both total losses and the asymmetric linear tick loss function are considered Copyright © 2012 John Wiley & Sons, Ltd.

In this paper we derive a test of predictability by exploring the possibility that forecasts from a given model, adjusted by a shrinkage factor, will display lower mean squared prediction errors than forecasts from a simple random walk. This generalizes most previous tests which compare forecast errors of a benchmark model with errors of a proposed alternative model, not allowing for shrinkage. We show that our test is a particular extension of a recently developed test of the martingale difference hypothesis. Using simulations we explore the behavior of our test in small and moderate samples. Numerical results indicate that the test has good size and power properties. Finally, we illustrate the use of our test in an empirical application within the exchange rate literature. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, I investigate the effects of cross-border capital flows induced by the rate of risk-adjusted excess returns (Sharpe ratio) on the transitional dynamics of the nominal exchange rate's deviation from its fundamental value. For this purpose, a two-state time-varying transition probability Markov regime-switching process is added to the sticky price exchange rate model with shares. I estimated this model using quarterly data on the four most active floating rate currencies for the years 1973–2009: the Australian dollar, Canadian dollar, Japanese yen and the British pound. The results provide evidence that the Sharpe ratios of debt and equity investments influence the evolution of transitional dynamics of the currencies' deviation from their fundamental values. In addition, I found that the relationship between economic fundamentals and the nominal exchange rates vary depending on the overvaluation or undervaluation of the currencies. Copyright © 2011 John Wiley & Sons, Ltd.

This paper examines the long-run relationship between implied and realised volatility for a sample of 16 FTSE-100 stocks. We find strong evidence of long-memory, fractional integration in equity volatility and show that this long-memory characteristic is not an outcome of structural breaks experienced during the sample period. Fractional cointegration between the implied and realised volatility is shown using recently developed rank cointegration tests by Robinson and Yajima (2002). The predictive ability of individual equity options is also examined and composite implied volatility estimates are shown to contain information on future idiosyncratic or stock-specific risk that is not captured using popular statistical approaches. Implied volatilities on individual UK equities are thus closely related to realised volatility and are an effective forecasting method particularly over medium forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather-derivative contracts. The pricing of such contracts requires the development of appropriate models for the prediction of the underlying weather variables. Within this framework, a commonly used specification is the ARFIMA-GARCH. We provide a generalization of such a model, introducing time-varying memory coefficients. Our model satisfies the empirical evidence of the changing memory level observed in average temperature series, and provides useful improvements in the forecasting, simulation, and pricing issues related to weather derivatives. We present an application related to the forecast and simulation of a temperature index density, which is then used for the pricing of weather options. Copyright © 2011 John Wiley & Sons, Ltd.

This paper considers the problem of forecasting in a panel data model with random individual effects and MA (q) remainder disturbances. It utilizes a recursive transformation for the MA (q) process derived by Baltagi and Li (Econometric Theory 1994; 10: 396–408) which yields a simple generalized least-squares estimator for this model. This recursive transformation is used in conjunction with Goldberger's result (Journal of the American Statistical Association 1962; 57: 369–375) to derive an analytic expression for the best linear unbiased predictor, for the ith cross-sectional unit, s periods ahead. Copyright © 2011 John Wiley & Sons, Ltd.

Recent advances in the measurement of beta (systematic return risk) and volatility (total return risk) demonstrate substantial advantages in utilizing high-frequency return data in a variety of settings. These advances in the measurement of beta and volatility have resulted in improvements in the evaluation of alternative beta and volatility forecasting approaches. In addition, more precise measurement has also led to direct modeling of the time variation of beta and volatility. Both the realized beta and volatility literature have most commonly been modeled with an autoregressive process. In this paper we evaluate constant beta models against autoregressive models of time-varying realized beta. We find that a constant beta model computed from daily returns over the last 12 months generates the most accurate quarterly forecast of beta and dominates the autoregressive time series forecasts. It also dominates (dramatically) the popular Fama–MacBeth constant beta model, which uses 5 years of monthly returns. Copyright © 2011 John Wiley & Sons, Ltd.

This paper applies a triple-choice ordered probit model, corrected for nonstationarity to forecast monetary decisions of the Reserve Bank of Australia. The forecast models incorporate a mix of monthly and quarterly macroeconomic time series. Forecast combination is used as an alternative to one multivariate model to improve accuracy of out-of-sample forecasts. This accuracy is evaluated with scoring functions, which are also used to construct adaptive weights for combining probability forecasts. This paper finds that combined forecasts outperform multivariable models. These results are robust to different sample sizes and estimation windows.Copyright © 2011 John Wiley & Sons, Ltd.

We question the ability of macroeconomic data to predict risk appetite and ‘flight-to-quality’ periods in the European credit market using a model inspired by the Markov switching literature. This model allows for a direct mapping of exogenous variables into state probabilities. We find that various surveys and transformed hard data have a forecasting power. We show that despite its depth, the 2008–2009 crisis should not be regarded as an unusual episode that would have to be modelled by an additional state. Finally, we show that our model outperforms a pure Markov switching model in terms of forecasting accuracy, thus clearly indicating that economic figures are helpful in forecasting the credit cycle. Copyright © 2011 John Wiley & Sons, Ltd.

Survey-based indicators are widely seen as leading indicators for economic activity. As such, consumer confidence might be informative for the future path of private consumption. Although the indicators receive high attention in the media, their forecasting power often appears to be very limited. This paper takes a fresh look at the data that serve as a basis for the consumer confidence indicator (CCI) reported by the EU Commission for the euro area. Different pooling methods are applied to exploit the survey information. Forecasts are based on mixed data sampling (MIDAS) and bridge equations. While the CCI does not outperform the autoregressive benchmark, the new indicators are able to raise forecasting performance. The best performing indicator should be built upon pre-selection methods. Data-driven aggregation methods should be preferred to determine the weights of the individual ingredients. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes a new evaluation framework for interval forecasts. Our model-free test can be used to evaluate interval forecasts and high-density regions, potentially discontinuous and/or asymmetric. Using a simple J-statistic, based on the moments defined by the orthonormal polynomials associated with the binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypotheses. Third, Monte Carlo simulations show that for realistic sample sizes our GMM test has good small-sample properties. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. It confirms that using this GMM test leads to major consequences for the ex post evaluation of interval forecasts produced by linear versus nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.

This paper compares the in-sample fitting and the out-of-sample forecasting performances of four distinct Nelson–Siegel class models: Nelson–Siegel, Bliss, Svensson, and a five-factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.

Most economic variables are released with a lag, making it difficult for policy-makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce an index of online interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in-sample and out-of-sample nowcasts, provide substantial gains in information delivery times, and are better at identifying turning points in the sales data. Copyright © 2011 John Wiley & Sons, Ltd.

This paper develops a state space framework for the statistical analysis of a class of locally stationary processes. The proposed Kalman filter approach provides a numerically efficient methodology for estimating and predicting locally stationary models and allows for the handling of missing values. It provides both exact and approximate maximum likelihood estimates. Furthermore, as suggested by the Monte Carlo simulations reported in this work, the performance of the proposed methodology is very good, even for relatively small sample sizes. Copyright © 2011 John Wiley & Sons, Ltd.

Recently, analysts' cash flow forecasts have become widely available through financial information services. Cash flow information enables practitioners to better understand the real operating performance and financial stability of a company, particularly when earnings information is noisy and of low quality. However, research suggests that analysts' cash flow forecasts are less accurate and more dispersed than earnings forecasts. We thus investigate factors influencing cash flow forecast accuracy and build a practical model to distinguish more accurate from less accurate cash flow forecasters, using past cash flow forecast accuracy and analyst characteristics. We find significant power in our cash flow forecast accuracy prediction models. We also find that analysts develop cash flow-specific forecasting expertise and knowhow, which are distinct from those that analysts acquire from forecasting earnings. In particular, cash flow-specific information is more useful in identifying accurate cash flow forecasters than earnings-specific information.Copyright © 2011 John Wiley & Sons, Ltd.

The implication of corporate bankruptcy prediction is important to financial institutions when making lending decisions. In related studies, many bankruptcy prediction models have been developed based on some machine-learning techniques. This paper presents a meta-learning framework, which is composed of two-level classifiers for bankruptcy prediction. The first-level multiple classifiers perform the data reduction task by filtering out unrepresentative training data. Then, the outputs of the first-level classifiers are utilized to create the second-level single (meta) classifier. The experiments are based on five related datasets and the results show that the proposed meta-learning framework provides higher prediction accuracy rates and lower type I/II errors when compared with the stacked generalization classifier and other three widely developed baselines, such as neural networks, decision trees, and logistic regression. Copyright © 2011 John Wiley & Sons, Ltd.

This paper examined the forecasting performance of disaggregated data with spatial dependency and applied it to forecasting electricity demand in Japan. We compared the performance of the spatial autoregressive ARMA (SAR-ARMA) model with that of the vector autoregressive (VAR) model from a Bayesian perspective. With regard to the log marginal likelihood and log predictive density, the VAR(1) model performed better than the SAR-ARMA( 1,1) model. In the case of electricity demand in Japan, we can conclude that the VAR model with contemporaneous aggregation had better forecasting performance than the SAR-ARMA model. Copyright © 2011 John Wiley & Sons, Ltd.

Nowcasting has been a challenge in the recent economic crisis. We introduce the Toll Index, a new monthly indicator for business cycle forecasting, and demonstrate its relevance using German data. The index measures the monthly transportation activity performed by heavy transport vehicles across the country and has highly desirable availability properties (insignificant revisions, short publication lags) as a result of the innovative technology underlying its data collection. It is coincident with production activity due to the prevalence of just-in-time delivery. The Toll Index is a good early indicator of production as measured, for instance, by the German Production Index, provided by the German Statistical Office, which is a well-known leading indicator of the gross national product. The proposed new index is an excellent example of technological, innovation-driven economic telemetry, which we suggest should be established more around the world. Copyright © 2011 John Wiley & Sons, Ltd.

Case-based reasoning (CBR) is considered a vital methodology in the current business forecasting area because of its simplicity, competitive performance with modern methods, and ease of pattern maintenance. Business failure prediction (BFP) is an effective tool that helps business people and entrepreneurs make more precise decisions in the current crisis. Using CBR as a basis for BFP can improve the tool's utility because CBR has the potential advantage in making predictions as well as suggestions compared with other methods. Recent studies indicate that an ensemble of various techniques has the possibility of improving the performance of predictive model. This research focuses on an early investigation on predicting business failure using a CBR ensemble (CBRE) forecasting method constructed from the use of random similarity functions (RSF), dubbed RSF-based CBRE. Four issues are discussed: (i) the reasons for the use of RSF as the basis in the CBRE forecasting method for BFP; (ii) the means to construct the RSF-based CBRE forecasting method for BFP; (iii) the empirical test on sensitivity of the RSF-based CBRE to the number of member CBR predictors; and (iv) performance assessment of the ensemble forecasting method. Results of the RSF-based CBRE forecasting method were statistically validated by comparing them with those of multivariate discriminant analysis, logistic regression, single CBR, and a linear support vector machine. The results from Chinese hotel BFP indicate that the RSF-based CBRE forecasting method could significantly improve CBR's upper limit of predictive capability. Copyright © 2011 John Wiley & Sons, Ltd.

Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimalforecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes value-at risk (VaR) estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market's expectation of risk. Forecast-combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models—a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residuals. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P 500 daily returns. Copyright © 2011 John Wiley & Sons, Ltd.

This paper compares various ways of extracting macroeconomic information from a data-rich environment for forecasting the yield curve using the Nelson–Siegel model. Five issues in extracting factors from a large panel of macro variables are addressed; namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data-driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. Factor-augmented methods perform well in relatively volatile periods, including the crisis period in 2008–9, when simpler models do not suffice. The macroeconomic information is exploited best by partial least squares methods, with principal component methods ranking second best. Reductions of mean squared prediction errors of 20–30% are attained, compared to the Nelson–Siegel model without macro factors. Copyright © 2011 John Wiley & Sons, Ltd.

Using factors in forecasting exercises reduces the dimensionality of the covariates set and, therefore, allows the forecaster to explore possible nonlinearities in the model. For an American macroeconomic dataset, I present evidence that the employment of nonlinear estimation methods can improve the out-of-sample forecasting accuracy for some macroeconomic variables, such as industrial production, employment, and Fed fund rate. Copyright © 2011 John Wiley & Sons, Ltd.

This paper explores the use of a maximum entropy econometric approach to combine forecasts when the small amount of information available does not allow the use of regression procedures since a dimensionality problem arises. This approach has its roots in information theory and builds on the entropy information measures and the classical maximum entropy principle, which was developed to recover information from underdetermined models. More specifically, we use the maximum entropy econometric approach for the measure of Shannon and we also propose its extension to the quadratic uncertainty measure. The experimental results over a pool of forecasts referring to Spanish inflation show some improvements when compared with equally weighted combined forecasting. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods—Dalhart's group method and Withycombe's group method—with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation. Copyright © 2011 John Wiley & Sons, Ltd.

This paper deals with the nonlinear modeling and forecasting of the dollar–sterling and franc–sterling real exchange rates using long spans of data. Our contribution is threefold. First, we provide significant evidence of smooth transition dynamics in the series by employing a battery of recently developed in-sample statistical tests. Second, we investigate the small-sample properties of several evaluation measures for comparing recursive forecasts when one of the competing models is nonlinear. Finally, we run a forecasting race for the post-Bretton Woods era between the nonlinear real exchange rate model, the random walk, and the linear autoregressive model. The nonlinear model outperforms all rival models in the dollar–sterling case but cannot beat the linear autoregressive in the franc–sterling. Copyright © 2011 John Wiley & Sons, Ltd.

Density forecasts contain a complete description of the uncertainty associated with a point forecast and are therefore important measures of financial risk. This paper aims to examine whether the new more complicated models for financial returns that allow for time variation in higher moments lead to better out-of-sample density forecasts. Using two decades of daily Standard & Poor's 500 index returns I find that a model with time-varying conditional variance, skewness and kurtosis produces significantly better density forecasts than the competing models. Copyright © 2011 John Wiley & Sons, Ltd.

Forecasting for inventory items with lumpy demand is difficult because of infrequent nonzero demands with high variability. This article developed two methods to forecast lumpy demand: an optimally weighted moving average method and an intelligent pattern-seeking method. We compare them with a number of well-referenced methods typically applied over the last 30 years in forecasting intermittent or lumpy demand. The comparison is conducted over about 200,000 forecasts (using 1-day-ahead and 5-day-ahead review periods) for 24 series of actual product demands across four different error measures. One of the most important findings of our study is that the two non-traditional methods perform better overall than the traditional methods. We summarize results and discuss managerial implications. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a method approach. We use six international stock price indices and three hypothetical portfolios formed by these indices. The sample was observed daily from 1 January 1996 to 31 December 2006. Confirmed by the failure rates and backtesting developed by Kupiec (Technique for verifying the accuracy of risk measurement models. Journal of Derivatives 1995; 3: 73–84) and Christoffersen (Evaluating interval forecasts. International Economic Review 1998; 39: 841–862), the empirical results show that our method can considerably improve the estimation accuracy of value-at-risk. Thus the study establishes an effective alternative model for risk prediction and hence also provides a reliable tool for the management of portfolios.Copyright © 2011 John Wiley & Sons, Ltd.

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra-day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH-type models with these proxies lead to improved forecasts across a range of intra-day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper we compare the in-sample fit and out-of-sample forecasting performance of no-arbitrage quadratic, essentially affine and dynamic Nelson–Siegel term structure models. In total, 11 model variants are evaluated, comprising five quadratic, four affine and two Nelson–Siegel models. Recursive re-estimation and out-of-sample 1-, 6- and 12-month-ahead forecasts are generated and evaluated using monthly US data for yields observed at maturities of 1, 6, 12, 24, 60 and 120 months. Our results indicate that quadratic models provide the best in-sample fit, while the best out-of-sample performance is generated by three-factor affine models and the dynamic Nelson–Siegel model variants. Statistical tests fail to identify one single best forecasting model class. Copyright © 2011 John Wiley & Sons, Ltd.

It has been suggested that a major problem for window selection when we estimate models for forecasting is to empirically determine the timing of the break. However, if the window choice between post-break or full sample is based on mean square forecast error ratios, it is difficult to understand why such a problem arises since break detectability and these ratios seem to have the same determinants. This paper analyses this issue first for the expected values in conditional models and then by Monte Carlo simulations for more general cases. Results show similar behaviour between rejection frequencies and the ratios but only for break tests that do not take into account forecasting error covariances, as is the case with mean square forecast error measures. Moreover, the asymmetric shape of the frequency distribution of the ratios could help us to better grasp empirical problems. An illustration using actual data is given. Copyright © 2011 John Wiley & Sons, Ltd.

We examine consistency properties of the exchange rate expectation formation process of short-run and long-run forecasts in the dollar/euro and yen/dollar market. Applying nonlinear consistency restrictions we show that in a simple expectation formation structure short-run forecasts are indeed inconsistent with long-run predictions. Moreover, we establish a ‘twist’ in the dollar/euro expectation formation process, i.e. market participants expect bandwagon effects in the short run, while they have stabilizing expectations in their long-run forecasts. Applying a panel probit analysis we find that this twisting behavior is more likely to occur in periods of excess exchange rate volatility. Copyright © 2011 John Wiley & Sons, Ltd.

This paper explains the unpredictability of exchange rate movements at short horizons and provides a plausible answer on the exchange rate disconnect puzzle. By generalizing Chaboud and Wright's (Journal of International Economics 2005; 66: 349–362) work, it is shown that exchange rates follow a martingale process at short horizons but over long horizons may contain some predictable structure. The empirical results applied to several major currencies of the US dollar support our hypothesis. This evidence is not coincided with the explanation of the inefficient market hypothesis under which exchange rate movements can be predictable in both short and long horizons. Copyright © 2011 John Wiley & Sons, Ltd.

Value-at-risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models is compared, including standard, threshold nonlinear and Markov switching generalized autoregressive conditional heteroskedasticity (GARCH) specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia–Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models outperformed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre crisis, while at the 1% level during and post crisis, for a 1-day horizon, models with skewed-t errors ranked best, while integrated GARCH models were favoured at the 5% level; (iii) all models forecast VaR less accurately and anti-conservatively post crisis. Copyright © 2011 John Wiley & Sons, Ltd.

This paper first shows that survey-based expectations (SBE) outperform standard time series models in US quarterly inflation out-of-sample prediction and that the term structure of survey-based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey-based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper we extend the works of Baillie and Baltagi (1999, in Analysis of Panels and Limited Dependent Variables Models, Hsiao C et al. (eds). Cambridge University Press: Cambridge, UK; 255–267) and generalize certain results from the Baltagi and Li (1992, Journal of Forecasting11: 561–567) paper accounting for AR(1) errors in the disturbance term. In particular, we derive six predictors for the one-way error components model, as well as their associated asymptotic mean squared error of multi-step prediction in the presence of AR(1) errors in the disturbance term. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods and indicates that the ordinary optimal predictor performs well for various accuracy criteria. Copyright © 2011 John Wiley & Sons, Ltd.

A new clustered correlation multivariate generalized autoregressive conditional heteroskedasticity (CC-MGARCH) model that allows conditional correlations to form clusters is proposed. This model generalizes the time-varying correlation structure of Tse and Tsui (2002, Journal of Business and Economic Statistics20: 351–361) by classifying the correlations among the series into groups. To estimate the proposed model, Markov chain Monte Carlo methods are adopted. Two efficient sampling schemes for drawing discrete indicators are also developed. Simulations show that these efficient sampling schemes can lead to substantial savings in computation time in Monte Carlo procedures involving discrete indicators. Empirical examples using stock market and exchange rate data are presented in which two-cluster and three-cluster models are selected using posterior probabilities. This implies that the conditional correlation equation is likely to be governed by more than one set of decaying parameters. Copyright © 2011 John Wiley & Sons, Ltd.

In this study we evaluate the forecast performance of model-averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B57: 247–262) with a hold-out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.

A long-standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out-of-sample contests. Using data from the USA over the period of 1872–2007, this paper re-examines the out-of-sample predictability of real stock prices based on price–dividend (PD) ratios. The current research focuses on the significance of the time-varying mean and nonlinear dynamics of PD ratios in the empirical analysis. Empirical results support the proposed nonlinear model of the PD ratio and the stationarity of the trend-adjusted PD ratio. Furthermore, this paper rejects the non-predictability hypothesis of stock prices statistically based on in- and out-of-sample tests and economically based on the criteria of expected real return per unit of risk. Copyright © 2011 John Wiley & Sons, Ltd.

In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi-expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non-bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non-bankrupt firms and outperform the models developed using other methods. Copyright © 2011 John Wiley & Sons, Ltd.

This paper concerns Long-term forecasts for cointegrated processes. First, it considers the case where the parameters of the model are known. The paper analytically shows that neither cointegration nor integration constraint matters in Long-term forecasts. It is an alternative implication of Long-term forecasts for cointegrated processes, extending the results of previous influential studies. The appropriate Mote Carlo experiment supports our analytical result. Secondly, and more importantly, it considers the case where the parameters of the model are estimated. The paper shows that accuracy of the estimation of the drift term is crucial in Long-term forecasts. Namely, the relative accuracy of various Long-term forecasts depends upon the relative magnitude of variances of estimators of the drift term. It further experimentally shows that in finite samples the univariate ARIMA forecast, whose drift term is estimated by the simple time average of differenced data, is better than the cointegrated system forecast, whose parameters are estimated by the well-known Johansen's ML method. Based upon finite sample experiments, it recommends the univariate ARIMA forecast rather than the conventional cointegrated system forecast in finite samples for its practical usefulness and robustness against model misspecifications. Copyright © 2011 John Wiley & Sons, Ltd.

P. C. B. Phillips (1998) demonstrated that deterministic trends are a valid representation of an otherwise stochastic trending mechanism; he remained skeptic, however, about the predictive power of such representations. In this paper we prove that forecasts built upon spurious regression may perform (asymptotically) as well as those issued from a correctly specified regression. We derive the order in probability of several in-sample and out-of-sample predictability criteria ( test, root mean square error, Theil's U-statistics and R²) using forecasts based upon a least squares-estimated regression between independent variables generated by a variety of empirically relevant data-generating processes. It is demonstrated that, when the variables are mean stationary or trend stationary, the order in probability of these criteria is the same whether the regression is spurious or not. Simulation experiments confirm our asymptotic results. Copyright © 2011 John Wiley & Sons, Ltd.

Volatility forecasting remains an active area of research with no current consensus as to the model that provides the most accurate forecasts, though Hansen and Lunde (2005) have argued that in the context of daily exchange rate returns nothing can beat a GARCH(1,1) model. This paper extends that line of research by utilizing intra-day data and obtaining daily volatility forecasts from a range of models based upon the higher-frequency data. The volatility forecasts are appraised using four different measures of ‘true’ volatility and further evaluated using regression tests of predictive power, forecast encompassing and forecast combination. Our results show that the daily GARCH(1,1) model is largely inferior to all other models, whereas the intra-day unadjusted-data GARCH(1,1) model generally provides superior forecasts compared to all other models. Hence, while it appears that a daily GARCH(1,1) model can be beaten in obtaining accurate daily volatility forecasts, an intra-day GARCH(1,1) model cannot be. Copyright © 2011 John Wiley & Sons, Ltd.

Do long-run equilibrium relations suggested by economic theory help to improve the forecasting performance of a cointegrated vector error correction model (VECM)? In this paper we try to answer this question in the context of a two-country model developed for the Canadian and US economies. We compare the forecasting performance of the exactly identified cointegrated VECMs to the performance of the over-identified VECMs with the long-run theory restrictions imposed. We allow for model uncertainty and conduct this comparison for every possible combination of the cointegration ranks of the Canadian and US models. We show that the over-identified structural cointegrated models generally outperform the exactly identified models in forecasting Canadian macroeconomic variables. We also show that the pooled forecasts generated from the over-identified models beat most of the individual exactly identified and over-identified models as well as the VARs in levels and in differences. Copyright © 2011 John Wiley & Sons, Ltd.

We present and apply singular spectrum analysis (SSA), a relatively new, non-parametric and data-driven method for signal extraction (trends, seasonal and business cycle components) and forecasting of UK tourism income. Our results show that SSA slightly outperforms SARIMA and time-varying-parameter state space models in terms of root mean square error, mean absolute error and mean absolute percentage error forecasting criteria. Copyright © 2011 John Wiley & Sons, Ltd.

This research forecasts peak call volume of a centralized after-hours call center for rural electric cooperatives to help the call center determine staffing levels. A Gaussian copula is used to capture the dependence among non-normal distributions. Using a centralized call center reduces costs by approximately 75% compared to having individual call centers at each cooperative. Adding cooperatives to the centralized call center is projected to further decrease costs per member. An out-of-sample forecasting exercise after the call center expanded validated the model's forecast that additional cooperatives could be added without a proportional increase in the peak number of calls. Copyright © 2011 John Wiley & Sons, Ltd.

We use an investment strategy based on firm-level capital structures. Investing in low-leverage firms yields abnormal returns of 4.43% per annum. If an investor holds a portfolio of low-leverage and low-market-to-book-ratio firms, abnormal returns increase to 16.18% per annum. A portfolio of low leverage and low market risk yields abnormal returns of 6.67% and a portfolio of small firms with low leverage earns 5.37% per annum. We use the Fama-Macbeth (1973) methodology with modifications. We confirm that portfolios based on low leverage earn higher returns in longer investment horizons. Our results are robust to other risk factors and the risk class of the firm. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we investigate the performance of a class of M-estimators for both symmetric and asymmetric conditional heteroscedastic models in the prediction of value-at-risk. The class of estimators includes the least absolute deviation (LAD), Huber's, Cauchy and B-estimator, as well as the well-known quasi maximum likelihood estimator (QMLE). We use a wide range of summary statistics to compare both the in-sample and out-of-sample VaR estimates of three well-known stock indices. Our empirical study suggests that in general Cauchy, Huber and B-estimator have better performance in predicting one-step-ahead VaR than the commonly used QMLE. Copyright © 2011 John Wiley & Sons, Ltd.

Initial applications of prediction markets (PMs) indicate that they provide good forecasting instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these ‘first-generation’ (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, ‘second-generation’ (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in the existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior. We find that G2 payoff mechanisms perform almost as well as their G1 counterpart, and trading behavior is very similar in both markets (i.e. trading prices and trading volume), except during the very last trading hours of the market. These results indicate that G2 PMs are valid instruments and support their applicability shown in previous studies for developing new product ideas or evaluating new product concepts. Copyright © 2011 John Wiley & Sons, Ltd.

In recent years, considerable attention has focused on modelling and forecasting stock market volatility. Stock market volatility matters because stock markets are an integral part of the financial architecture in market economies and play a key role in channelling funds from savers to investors. The focus of this paper is on forecasting stock market volatility in Central and East European (CEE) countries. The obvious question to pose, therefore, is how volatility can be forecast and whether one technique consistently outperforms other techniques. Over the years a variety of techniques have been developed, ranging from the relatively simple to the more complex conditional heteroscedastic models of the GARCH family. In this paper we test the predictive power of 12 models to forecast volatility in the CEE countries. Our results confirm that models which allow for asymmetric volatility consistently outperform all other models considered. Copyright © 2011 John Wiley & Sons, Ltd.

A forecasting model for unemployment is constructed that exploits the time series properties of unemployment while satisfying the economic relationships specified by Okun's law and the Phillips curve. In deriving the model, we jointly consider the problem of obtaining estimates of the unobserved potential rate of unemployment consistent with Okun's law and the Phillips curve, and associating the potential rate of unemployment with actual unemployment. The empirical example shows that the model clearly outperforms alternative forecasting procedures typically used to forecast unemployment. Copyright © 2011 John Wiley & Sons, Ltd.

Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour with an approach relying on Markov-switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long-term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill. Copyright © 2010 John Wiley & Sons, Ltd.

Consider forecasting the economic variable Y_t+h with predictors X_t, where h is the forecast horizon. This paper introduces a semiparametric method that generates forecast intervals of Y_t+h|X_t from point forecast models. First, the point forecast model is estimated, thereby taking advantage of its predictive power. Then, nonparametric estimation of the conditional distribution function (CDF) of the forecast error conditional on X_t builds the rest of the forecast distribution around the point forecast, from which symmetric and minimum-length forecast intervals for Y_t+h|X_t can be constructed. Under mild regularity conditions, asymptotic analysis shows that (1) regardless of the quality of the point forecast model (i.e., it may be misspecified), forecast quantiles are consistent and asymptotically normal; (2) minimum length forecast intervals are consistent. Proposals for bandwidth selection and dimension reduction are made. Three sets of simulations show that for reasonable point forecast models the method has significant advantages over two existing approaches to interval forecasting: one that requires the point forecast model to be correctly specified, and one that is based on fully nonparametric CDF estimate of Y_t+h|X_t. An application to exchange rate forecasting is presented. Copyright © 2010 John Wiley & Sons, Ltd.

This paper estimates, using stochastic simulation and a multi-country macroeconometric model, the fraction of the forecast error variance of output changes and the fraction of the forecast error variance of inflation that are due to unpredictable asset price changes. The results suggest that between about 25% and 37% of the forecast error variance of output growth over eight quarters is due to asset price changes and between about 33% and 60% of the forecast error variance of inflation over eight quarters is due to asset price changes. These estimates provide limits to the accuracy that can be expected from macroeconomic forecasting. Copyright © 2011 John Wiley & Sons, Ltd.

The state space model is widely used to handle time series data driven by related latent processes in many fields. In this article, we suggest a framework to examine the relationship between state space models and autoregressive integrated moving average (ARIMA) models by examining the existence and positive-definiteness conditions implied by auto-covariance structures. This study covers broad types of state space models frequently used in previous studies. We also suggest a simple statistical test to check whether a certain state space model is appropriate for the specific data. For illustration, we apply the suggested procedure in the analysis of the United States real gross domestic product data. Copyright © 2011 John Wiley & Sons, Ltd.

This paper addresses the issue of forecasting term structure. We provide a unified state-space modeling framework that encompasses different existing discrete-time yield curve models. Within such a framework we analyze the impact of two modeling choices, namely the imposition of no-arbitrage restrictions and the size of the information set used to extract factors, on forecasting performance. Using US yield curve data, we find that both no-arbitrage and large information sets help in forecasting but no model uniformly dominates the other. No-arbitrage models are more useful at shorter horizons for shorter maturities. Large information sets are more useful at longer horizons and longer maturities. We also find evidence for a significant feedback from yield curve models to macroeconomic variables that could be exploited for macroeconomic forecasting. Copyright © 2010 John Wiley & Sons, Ltd.

This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewedHyphen;t or compound Poisson (jump) distribution based upon a nonlinear and asymmetric GARCH (NGARCH) model framework. The empirical results for the S&P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the nonHyphen;normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change. Copyright © 2011 John Wiley & Sons, Ltd.

We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.