Estimation of short-run predictive factor for US growth using state employment data

We estimate a predictive single factor model targeted to unobserved common growth in gross domestic product and gross domestic income (GDI) using a state-space framework with select state employment data. We use likelihood-based comparison to select the states to estimate the dynamic factor. The results show improved in-sample and out-of-sample performance than threshold principal component factors and financial spreads. Out-of-sample evaluations indicate larger gains for GDI growth with 14% to 20% lower mean squared forecast errors than other alternatives. Sectoral employment factors based on selected sectors using the state-space framework also show forecasting gains. An expanded model using both sectoral and state employment data shows that their common component is the primary predictive factor.     

Investigating the relationship between gold and silver prices

This paper analyses the long-run relationship between gold and silver prices. The three main questions addressed are: the influence of a large bubble from 1979:9 to 1980:3 on the cointegration relationship, the extent to which by including error-correction terms in a non-linear way we can beat the random walk model out-of-sample, and the existence of a strong simultaneous relationship between the rates of return of gold and silver. Different efficient single-equation estimation techniques are required for each of the three questions and this is explained within a simple bivariate cointegrating system. With monthly data from 1971 to 1990, it is found that cointegration could have occurred during some periods and especially during the bubble and post-bubble periods. However, dummy variables for the intercept of the long-run relationships are needed during the full sample. For the price of gold the non-linear models perform better than the random walk in-sample and out-of-sample. In-sample non-linear models for the price of silver perform better than the random walk but this predictive capacity is lost out-of-sample, mainly due to the structural change that occurs (reduction) in the variance of the out-of-sample models. The in-sample and out-of-sample predictive capacity of the non-linear models is reduced when the variables are in logs. Clear and strong evidence is found for a simultaneous relationship between the rates of return of gold and silver. In the three type of relationships that we have analysed between the prices of gold and silver, the dependence is less out-of-sample, possibly meaning that the two markets are becoming separated. © 1998 John Wiley & Sons, Ltd.     

Financial distress prediction model: The effects of corporate governance indicators

This paper constructs a financial distress prediction model that includes not only traditional financial variables, but also several important corporate governance variables. Using data from Taiwan, the empirical results show that the best in-sample and out-of-sample prediction models should combine the financial variables with the corporate governance variables. Moreover, the prediction accuracy is higher for the models using dynamic distress threshold values than those with tradition threshold values. Most financial ratios, except for the debt ratio, are higher in financially sound companies than in financial distressed ones. With regard to the corporate governance variables, we find that the CEO/Chairman duality may not result in the outbreak of financial distress, but higher equity pledge ratios of managers (shareholding ratios by board members and insiders) positively (negatively) correlate with financial distress.     

Forecasting local currency bond risk premia of emerging markets: The role of cross-country macrofinancial linkages

In this paper, we forecast local currency debt of five major emerging market countries (Brazil, Indonesia, Mexico, South Africa, and Turkey) over the period January 2010 to January 2019 (with an in-sample period: March 2005 to December 2009). We exploit information from a large set of economic and financial time series to assess the importance not only of “own-country” factors (derived from principal component and partial least squares approaches), but also create “global” predictors by combining the country-specific variables across the five emerging economies. We find that, while information on own-country factors can outperform the historical average model, global factors tend to produce not only greater statistical and economic gains, but also enhance market timing ability of investors, especially when we use the target variable (bond premium) approach under the partial least squares method to extract our factors. Our results have important implications not only for fund managers but also for policymakers.     

A detailed look at crude oil price volatility prediction using macroeconomic variables

We investigate whether crude oil price volatility is predictable by conditioning on macroeconomic variables. We consider a large number of predictors, take into account the possibility that relative predictive performance varies over the out-of-sample period, and shed light on the economic drivers of crude oil price volatility. Results using monthly data from 1983:M1 to 2018:M12 document that variables related to crude oil production, economic uncertainty and variables that either describe the current stance or provide information about the future state of the economy forecast crude oil price volatility at the population level 1 month ahead. On the other hand, evidence of finite-sample predictability is very weak. A detailed examination of our out-of-sample results using the fluctuation test suggests that this is because relative predictive performance changes drastically over the out-of-sample period. The predictive power associated with the more successful macroeconomic variables concentrates around the Great Recession until 2015. They also generate the strongest signal of a decrease in the price of crude oil towards the end of 2008.     

Predictive modeling of consumer color preference: Using retail data and merchandise images

The popularity of a fashion item depends on its color, shape, texture, and price. For different items (with all attributes identical except color) of a specific product, fashion retailers need to learn consumer color preference and decide their order quantities accordingly to match their products to consumer demand. This study aims to predict consumer color preference using the knowledge learned from merchandise images, historical retail data, and fashion trends. In our work, merchandise images are analyzed to extract color features, and the retail data of a sportswear retailer are used to reveal consumer choices among items with various colors. Choice behavior is described by a multinomial logit model, whose utility function captures the relationship between color features and popularity. Both linear functions and neural networks are applied to represent the utility function, and their out-of-sample prediction performances are compared. According to the out-of-sample performance test, our model shows reasonable predictive power and can outperform order decisions made by fashion buyers.     

Forecast Combination for Euro Area Inflation: A Cure in Times of Crisis?

The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue for forecasts from a range of short‐term forecasting models. Our analysis shows that there is considerable variation of the relative performance of the different models over time. To take that into account we suggest employing performance‐based forecast combination methods—in particular, one with more weight on the recent forecast performance. We compare such an approach with equal forecast combination that has been found to outperform more sophisticated forecast combination methods in the past, and investigate whether it can improve forecast accuracy over the single best model. The time‐varying weights assign weights to the economic interpretations of the forecast stemming from different models. We also include a number of benchmark models in our analysis. The combination methods are evaluated for HICP headline inflation and HICP excluding food and energy. We investigate how forecast accuracy of the combination methods differs between pre‐crisis times, the period after the global financial crisis and the full evaluation period, including the global financial crisis with its extraordinary volatility in inflation. Overall, we find that forecast combination helps hedge against bad forecast performance and that performance‐based weighting outperforms simple averaging. Copyright © 2017 John Wiley & Sons, Ltd.     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

GPR120: a critical role in adipogenesis,inflammation, and energy metabolism in adipose tissue

It is well known that adipose tissue has a critical role in the development of obesity and metabolic diseases and that adipose tissue acts as an endocrine organ to regulate lipid and glucose metabolism. Accumulating in the adipose tissue, fatty acids serve as a primary source of essential nutrients and act on intracellular and cell surface receptors to regulate biological events. G protein-coupled receptor 120 (GPR120) represents a promising target for the treatment of obesity-related metabolic disorders for its involvement in the regulation of adipogenesis, inflammation, glucose uptake, and insulin resistance. In this review, we summarize recent studies and advances regarding the systemic role of GPR120 in adipose tissue, including both white and brown adipocytes. We offer a new perspective by comparing the different roles in a variety of homeostatic processes from adipogenic development to adipocyte metabolism, and we also discuss the effects of natural and synthetic agonists that may be potential agents for the treatment of metabolic diseases.     

A BVAR model for the connecticut economy

A Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out-of-sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out-of-sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short- and long-term out-of-sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.     

Mixed-frequency forecasting of crude oil volatility based on the information content of global economic conditions

This paper subjects six alternative indicators of global economic activity to empirically examine their relative predictive powers in the forecast of crude oil market volatility. GARCH-MIDAS approach is constructed to accommodate all the relevant series at their available data frequencies, thereby circumventing information loss and any associated bias. We find evidence in support of global economic activity as a good predictor of energy market volatility. Our forecast evaluation of the various indicators places a higher weight on the newly developed indicator of global economic activity which is based on a set of 16 variables covering multiple dimensions of the global economy, whereas other indicators do not seem to capture. Furthermore, we find that accounting for any inherent asymmetry in the global economic activity proxies improves the forecast accuracy of the GARCH-MIDAS-X model for oil volatility. The results leading to these conclusions are robust to multiple forecast horizons and consistent across alternative energy sources.     

A simple parameter-driven binary time series model

We introduce a parameter-driven, state-space model for binary time series data. The model is based on a state process with a binomial-beta dynamics, which has a Markov, endogenous switching regime representation. The model allows for recursive prediction and filtering formulas with extremely low computational cost, and hence avoids the use of computational intensive simulation-based filtering algorithms. Case studies illustrate the advantage of our model over popular intensity-based observation-driven models, both in terms of fit and out-of-sample forecast.     

Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models

Agricultural productivity highly depends on the cost of energy required for cultivation. Thus prior knowledge of energy consumption is an important step for energy planning and policy development in agriculture. The aim of the present study is to evaluate the application potential of multiple linear regression (MLR) and machine learning tools such as support vector regression (SVR) and Gaussian process regression (GPR) to forecast the agricultural energy consumption of Turkey. In the development of the models, widespread indicators such as agricultural value-added, total arable land, gross domestic product share of agriculture, and population data were used as input parameters. Twenty-eight-year historical data from 1990 to 2017 were utilized for the training and testing stages of the models. A Bayesian optimization method was applied to improve the prediction capability of SVR and GPR models. The performance of the models was measured by various statistical tools. The results indicated that the Bayesian optimized GPR (BGPR) model with exponential kernel function showed a superior prediction capability over MLR and Bayesian optimized SVR model. The root mean square error, mean absolute deviation, mean absolute percentage error, and coefficient of determination (R²) values for the BGPR model were determined as 0.0022, 0.0005, 0.2041, and 0.9999 in the training phase and 0.0452, 0.0310, 7.7152, and 0.9677 in the testing phase, respectively. As a result, it can be concluded that the proposed BGPR model is an efficient technique and has the potential to predict agricultural energy consumption with high accuracy.     

Combining multivariate volatility forecasts using weighted losses

The ability to improve out-of-sample forecasting performance by combining forecasts is well established in the literature. This paper advances this literature in the area of multivariate volatility forecasts by developing two combination weighting schemes that exploit volatility persistence to emphasise certain losses within the combination estimation period. A comprehensive empirical analysis of the out-of-sample forecast performance across varying dimensions, loss functions, sub-samples and forecast horizons show that new approaches significantly outperform their counterparts in terms of statistical accuracy. Within the financial applications considered, significant benefits from combination forecasts relative to the individual candidate models are observed. Although the more sophisticated combination approaches consistently rank higher relative to the equally weighted approach, their performance is statistically indistinguishable given the relatively low power of these loss functions. Finally, within the applications, further analysis highlights how combination forecasts dramatically reduce the variability in the parameter of interest, namely the portfolio weight or beta.     

Bayesian forecasts for cointegrated models

This paper investigates Bayesian forecasts for some cointegrated time series data. Suppose data are derived from some cointegrated model, but, an unrestricted vector autoregressive model, without including cointegrated conditions, is fitted; the implication of using an incorrect model will be investigated from the Bayesian forecasting viewpoint. For some special cointegrated data and under the diffuse prior assumption, it can be analytically proven that the posterior predictive distributions for both the true model and the fitted model are asymptotically the same for any future step. For a more general cointegrated model, examinations are performed via simulations. Some simulated results reveal that a reasonably unrestricted model will still provide a rather accurate forecast as long as the sample size is large enough or the forecasting period is not too far in the future. For a small sample size or for long‐term forecasting, more accurate forecasts are expected if the correct cointegrated model is actually applied. Copyright © 2002 John Wiley & Sons, Ltd.     

Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes

We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies—small samples, limited models, and nonholistic validations—by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Using a new composite indicator, the integrated forecast score, we show that risk‐neutral densities outperform historical‐based predictions in terms of information content. We find that the variance gamma model generates the highest out‐of‐sample likelihood of observed prices and the lowest predictive errors, whereas the GARCH‐based GJR‐FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model, or the nonparametric Breeden–Litzenberger formula yield biased predictions and are rejected in statistical tests.     

Forecasting Stock Returns: Do Commodity Prices Help?

This paper examines the relationship between stock prices and commodity prices and whether this can be used to forecast stock returns. As both prices are linked to expected future economic performance they should exhibit a long‐run relationship. Moreover, changes in sentiment towards commodity investing may affect the nature of the response to disequilibrium. Results support cointegration between stock and commodity prices, while Bai–Perron tests identify breaks in the forecast regression. Forecasts are computed using a standard fixed (static) in‐sample/out‐of‐sample approach and by both recursive and rolling regressions, which incorporate the effects of changing forecast parameter values. A range of model specifications and forecast metrics are used. The historical mean model outperforms the forecast models in both the static and recursive approaches. However, in the rolling forecasts, those models that incorporate information from the long‐run stock price/commodity price relationship outperform both the historical mean and other forecast models. Of note, the historical mean still performs relatively well compared to standard forecast models that include the dividend yield and short‐term interest rates but not the stock/commodity price ratio. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting Stock Market Volatility in Central and Eastern European Countries

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 Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

Using the yield curve to forecast economic growth

This paper finds the yield curve to have a well-performing ability to forecast the real gross domestic product growth in the USA, compared to professional forecasters and time series models. Past studies have different arguments concerning growth lags, structural breaks, and ultimately the ability of the yield curve to forecast economic growth. This paper finds such results to be dependent on the estimation and forecasting techniques employed. By allowing various interest rates to act as explanatory variables and various window sizes for the out-of-sample forecasts, significant forecasts from many window sizes can be found. These seemingly good forecasts may face issues, including persistent forecasting errors. However, by using statistical learning algorithms, such issues can be cured to some extent. The overall result suggests, by scientifically deciding the window sizes, interest rate data, and learning algorithms, many outperforming forecasts can be produced for all lags from one quarter to 3 years, although some may be worse than the others due to the irreducible noise of the data.