Modeling and forecasting the oil volatility index

The increase in oil price volatility in recent years has raised the importance of forecasting it accurately for valuing and hedging investments. The paper models and forecasts the crude oil exchange‐traded funds (ETF) volatility index, which has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. Analysis of the oil volatility index suggests that it presents features similar to those of the daily market volatility index, such as long memory, which is modeled using well‐known heterogeneous autoregressive (HAR) specifications and new extensions that are based on net and scaled measures of oil price changes. The aim is to improve the forecasting performance of the traditional HAR models by including predictors that capture the impact of oil price changes on the economy. The performance of the new proposals and benchmarks is evaluated with the model confidence set (MCS) and the Generalized‐AutoContouR (G‐ACR) tests in terms of point forecasts and density forecasting, respectively. We find that including the leverage in the conditional mean or variance of the basic HAR model increases its predictive ability. Furthermore, when considering density forecasting, the best models are a conditional heteroskedastic HAR model that includes a scaled measure of oil price changes, and a HAR model with errors following an exponential generalized autoregressive conditional heteroskedasticity specification. In both cases, we consider a flexible distribution for the errors of the conditional heteroskedastic process.     

Forecasting energy prices: Quantile-based risk models

The goal of this paper is to use a new modelling approach to extract quantile-based oil and natural gas risk measures using quantile autoregressive distributed lag mixed-frequency data sampling (QADL-MIDAS) regression models. The analysis compares this model to a standard quantile auto-regression (QAR) model and shows that it delivers better quantile forecasts at the majority of forecasting horizons. The analysis also uses the QADL-MIDAS model to construct oil and natural gas prices risk measures proxying for uncertainty, third-moment dynamics, and the risk of extreme energy realizations. The results document that these risk measures are linked to the future evolution of energy prices, while they are linked to the future evolution of US economic growth.     

Temporal aggregation and systematic sampling in structural time-series models

Given a structural time-series model specified at a basic time interval, this paper deals with the problems of forecasting efficiency and estimation accuracy generated when the data are collected at a timing interval which is a multiple of the time unit chosen to build the basic model. Results are presented for the simplest structural models, the trend plus error models, under the assumption that the parameters of the model are known. It is shown that the gains in forecasting efficiency and estimation accuracy for having data at finer intervals are considerable for both stock and flow variables with only one exception. No gain in forecasting efficiency is achieved in the case of a stock series that follows a random walk.     

An integrated bayesian vector auto regression and error correction model for forecasting electricity consumption and prices

The analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time-series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four-step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.     

Predicting opening spot prices using extended futures trading

Previous studies found that extended futures trading contains useful information in explaining subsequent overnight spot returns. This study therefore compares the performance of using the extended trading of the TAIFEX (Taiwan Futures Exchange) index futures and single‐stock futures to predict their opening underlying spot prices. Furthermore, according to the efficient market hypothesis, the share price fully reflects all the information available and should adjust to new information instantaneously. However, several studies have demonstrated that short‐sales restrictions delay the speed of price adjustment to negative information. The relevant question is whether short‐selling restrictions also slow down the speed at which the opening spot price adjusts to the new information revealed through extended futures trading, and thus reducing the price prediction function of extended futures trading. The empirical results find that using the opening futures price and the prediction method proposed in this study can more accurately predict the opening spot price on the same day. Furthermore, the performance of using the extended trading of index futures to predict the opening spot index price is superior to that of using the extended trading of single‐stock futures to predict the opening stock price. Finally, as found in previous studies, short‐selling restrictions also slow down the speed of stock price adjustment to the new information revealed through extended futures trading. Thus both the up‐tick rule and the short‐selling bans (especially the latter) negatively affect the price forecasting performance of extended futures trading.     

Forecasting price delay and future stock returns: The role of corporate social responsibility

This paper investigates the role of corporate social responsibility (CSR) performance in forecasting companys' stock prices and future returns. The forecasting analysis identifies a negative association between CSR performance and proxies of price delay. The negative CSR–delay association is weak for state‐owned enterprises (SOEs) because of their politically oriented motivation of CSR activities, but significantly strong for non‐SOEs. Furthermore, we find that forecasting delayed firms is expected to have higher future returns. In particular, the returns premium is most attributable to the CSR component of delay, compared with the non‐CSR component. Taken together, these results suggest that CSR performance plays a positive role in enhancing stock price efficiency, and a potential explanation is that CSR performance can be considered as additional information for equity predictions.     

Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models

We evaluate forecasting models of US business fixed investment spending growth over the recent 1995:1–2004:2 out‐of‐sample period. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash‐Flow models of investment spending, as well as real stock prices and excess stock return predictors. The real stock price model typically generates the most accurate forecasts, and forecast‐encompassing tests indicate that this model contains most of the information useful for forecasting investment spending growth relative to the other models at longer horizons. In a robustness check, we also evaluate the forecasting performance of the models over two alternative out‐of‐sample periods: 1975:1–1984:4 and 1985:1–1994:4. A number of different models produce the most accurate forecasts over these alternative out‐of‐sample periods, indicating that while the real stock price model appears particularly useful for forecasting the recent behavior of investment spending growth, it may not continue to perform well in future periods. Copyright © 2007 John Wiley & Sons, Ltd.     

Modeling and Forecasting Online Auction Prices: A Semiparametric Regression Analysis

Interest in online auctions has been growing in recent years. There is an extensive literature on this topic, whereas modeling online auction price process constitutes one of the most active research areas. Most of the research, however, only focuses on modeling price curves, ignoring the bidding process. In this paper, a semiparametric regression model is proposed to model the online auction process. This model captures two main features of online auction data: changing arrival rates of bidding processes and changing dynamics of prices. A new inference procedure using B‐splines is also established for parameter estimation. The proposed model is used to forecast the price of an online auction. The advantage of this proposed approach is that the price can be forecast dynamically and the prediction can be updated according to newly arriving information. The model is applied to Xbox data with satisfactory forecasting properties. Copyright © 2016 John Wiley & Sons, Ltd.     

The dynamic and stochastic instability of betas: Implications for forecasting stock returns

The purpose of this paper is to simultaneously investigate several important issues that feature the dynamic and stochastic behavior of beta coefficients for individual stocks and affect the forecasting of stock returns. The issues include randomness, nonstantionarity, and shifts in the mean and variance parameters of the beta coefficient, and are addressed within the framework of variable-mean-response (VMR) random coefficients models in which the problem of heteroscedasticity is present. Estimation is done using a four-step generalized least squares method. The hypotheses concerning randomness and nonstationarity of betas are tested. The time paths, sizes, and marginal rates of mean shifts are determined. The issue of variance shift is examined on the basis of five special tests, called T*, B, S', G and W. Then the impacts of the dynamic and stochastic instability on the estimation of betas is tested by a nonparametric procedure. Finally, the VMR models' ability of forecasting stock returns is evaluated against the standard capital asset pricing model. The empirical findings shed new light on the continuing debate as to whether the beta coefficient is random and nonstationary and have important implications for modeling and forecasting the measurement of performance and the determination of stock returns.     

The role of prices in models of innovation diffusion

When evaluating the launch of a new product or service, forecasts of the diffusion path and the effects of the marketing mix are critically important. Currently no unified framework exists to provide guidelines on the inclusion and specification of marketing mix variables into models of innovation diffusion. The objective of this research is to examine empirically the role of prices in diffusion models, in order to establish whether price can be incorporated effectively into the simpler time-series models. Unlike existing empirical research which examines the models' fit to historical data, we examine the predictive validity of alternative models. Only if the incorporation of prices improves the predictive performance of diffusion models can it be argued that these models have validity. A series of diffusion models which include prices are compared against a number of well-accepted diffusion models, including the Bass (1969) model, and more recently developed ‘flexible’ diffusion models. For short data series and long-lead time forecasting, the situation typical of practical situations, price rarely added to the forecasting capability of simpler time-series models. Copyright © 1998 John Wiley & Sons, Ltd.     

Forecasting inflation using univariate continuous-time stochastic models

In this paper we investigate the applicability of several continuous-time stochastic models to forecasting inflation rates with horizons out to 20 years. While the models are well known, new methods of parameter estimation and forecasts are supplied, leading to rigorous testing of out-of-sample inflation forecasting at short and long time horizons. Using US consumer price index data we find that over longer forecasting horizons—that is, those beyond 5 years—the log-normal index model having Ornstein–Uhlenbeck drift rate provides the best forecasts.     

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.     

Forecasting the price of crude oil via convenience yield predictions

The paper develops an oil price forecasting technique which is based on the present value model of rational commodity pricing. The approach suggests shifting the forecasting problem to the marginal convenience yield, which can be derived from the cost‐of‐carry relationship. In a recursive out‐of‐sample analysis, forecast accuracy at horizons within one year is checked by the root mean squared error as well as the mean error and the frequency of a correct direction‐of‐change prediction. For all criteria employed, the proposed forecasting tool outperforms the approach of using futures prices as direct predictors of future spot prices. Vis‐à‐vis the random‐walk model, it does not significantly improve forecast accuracy but provides valuable statements on the direction of change. Copyright © 2007 John Wiley & Sons, Ltd.     

On the Predictive Information of Futures' Prices: A Wavelet‐Based Assessment

While in speculative markets forward prices could be regarded as natural predictors for future spot rates, empirically, forward prices often fail to indicate ex ante the direction of price movements. In terms of forecasting, the random walk approximation of speculative prices has been established to provide ‘naive’ predictors that are most difficult to outperform by both purely backward‐looking time series models and more structural approaches processing information from forward markets. We empirically assess the implicit predictive content of forward prices by means of wavelet‐based prediction of two foreign exchange (FX) rates and the price of Brent oil quoted either in US dollars or euros. Essentially, wavelet‐based predictors are smoothed auxiliary (padded) time series quotes that are added to the sample information beyond the forecast origin. We compare wavelet predictors obtained from padding with constant prices (i.e. random walk predictors) and forward prices. For the case of FX markets, padding with forward prices is more effective than padding with constant prices, and, moreover, respective wavelet‐based predictors outperform purely backward‐looking time series approaches (ARIMA). For the case of Brent oil quoted in US dollars, wavelet‐based predictors do not signal predictive content of forward prices for future spot prices. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting volatility in commodity markets

This paper uses recent advances in time-series modeling to derive long-horizon forecasts of commodity price volatility which incorporate investors' expectations of volatility. Our results are promising. We compare several different forecasts of commodity price volatility, which we divide into three categories: (1) forecasts using only expectations derived from options prices; (2) forecasts using only time-series modeling; and (3) forecasts which combine market expectations and time-series methods. The forecasts in (1) and (2) are used extensively in the literature, while those in (3) are new in this paper. On comparing these different forecasts, we find that our proposed forecasts from category (3) outperform both market expectations forecasts and time-series forecasts. This result holds both in and out of sample for virtually all commodities considered.     

Term Structure Forecasting: No‐Arbitrage Restrictions versus Large Information Set

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.     

Integrating time-series and end-use methods to forecast electricity sales

Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward. The stock levels of energy-consuming equipment are forecast, as well as the energy consumption characteristics of the equipment. The final forecast is the product of the stock and usage characteristics. This approach is well suited to forecasting long time periods when technological change, equipment depletion and replacement, and other structural changes are evident. For time periods of shorter duration, these factors are static and variations are more likely to result from shocks to the environment. The shocks influence the usage of the equipment. A second forecasting approach using time-series analysis has been demonstrated to be superior for these applications. This paper discusses the integration of the two methods into a unified system. The result is a time-series model whose parameter effects become dynamic in character. An example of the models being used at the Georgia Power Company is presented. It is demonstrated that a time-series model which incorporates end-use stock and usage information is superior—even in short-term forecasting situations—to a similar time-series model which excludes the information.     

Forecasting with balanced state space representations of multivariate distributed lag models

A procedure for estimating state space models for multivariate distributed lag processes is described. It involves singular value decomposition techniques and yields an internally balanced state space representation which has attractive properties. Following the specifications of a forecasting competition, the approach is applied to generate ex-post forecasts for US real GNP growth rates. The forecasts of the estimated state space model are compared to those of twelve econometric models and an ARIMA model.     

Time series forecasting using functional partial least square regression with stochastic volatility,GARCH, and exponential smoothing

We propose a method for improving the predictive ability of standard forecasting models used in financial economics. Our approach is based on the functional partial least squares (FPLS) model, which is capable of avoiding multicollinearity in regression by efficiently extracting information from the high‐dimensional market data. By using its well‐known ability, we can incorporate auxiliary variables that improve the predictive accuracy. We provide an empirical application of our proposed methodology in terms of its ability to predict the conditional average log return and the volatility of crude oil prices via exponential smoothing, Bayesian stochastic volatility, and GARCH (generalized autoregressive conditional heteroskedasticity) models, respectively. In particular, what we call functional data analysis (FDA) traces in this article are obtained via the FPLS regression from both the crude oil returns and auxiliary variables of the exchange rates of major currencies. For forecast performance evaluation, we compare out‐of‐sample forecasting accuracy of the standard models with FDA traces to the accuracy of the same forecasting models with the observed crude oil returns, principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) models. We find evidence that the standard models with FDA traces significantly outperform our competing models. Finally, they are also compared with the test for superior predictive ability and the reality check for data snooping. Our empirical results show that our new methodology significantly improves predictive ability of standard models in forecasting the latent average log return and the volatility of financial time series.     

Forecasting for business planning: A case study of IBM product sales

This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time-series methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.