Journal of Forecasting
- Publisher:
- Wiley
- Publication date:
- 2021-02-01
- ISBN:
- 0277-6693
Issue Number
- No. 39-7, November 2020
- No. 39-6, September 2020
- No. 39-5, August 2020
- No. 39-4, July 2020
- No. 39-3, April 2020
- No. 39-2, March 2020
- No. 39-1, January 2020
- No. 38-8, December 2019
- No. 38-7, November 2019
- No. 38-6, September 2019
- No. 38-5, August 2019
- No. 38-4, July 2019
- No. 38-3, April 2019
- No. 38-2, March 2019
- No. 38-1, January 2019
- No. 37-8, December 2018
- No. 37-7, November 2018
- No. 37-6, September 2018
- No. 37-5, August 2018
- No. 37-4, July 2018
Latest documents
- 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.
- Forecasting Australia's real house price index: A comparison of time series and machine learning methods
We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out‐of‐sample predictions. The algorithms, which are specified in both single‐ and multi‐equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one‐ and two‐quarter‐ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons × 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne‐quarter‐ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons.
- Professional forecasters' expectations, consistency, and international spillovers
This paper focuses on the expectation formation process of professional forecasters by relying on survey data on forecasts regarding gross domestic product growth, consumer price index inflation and 3‐month interest rates for a broad set of countries. We examine the interrelation between macroeconomic forecasts and also the impact of uncertainty on forecasts by allowing for cross‐country interdependencies and time variation in the coefficients. We find that professional forecasts are often in line with the Taylor rule and identify significant expectation spillovers from monetary policy in the USA.
- The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR
As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first‐class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.
- On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning
Through empirical research, it is found that the traditional autoregressive integrated moving average (ARIMA) model has a large deviation for the forecasting of high‐frequency financial time series. With the improvement in storage capacity and computing power of high‐frequency financial time series, this paper combines the traditional ARIMA model with the deep learning model to forecast high‐frequency financial time series. It not only preserves the theoretical basis of the traditional model and characterizes the linear relationship, but also can characterize the nonlinear relationship of the error term according to the deep learning model. The empirical study of Monte Carlo numerical simulation and CSI 300 index in China show that, compared with ARIMA, support vector machine (SVM), long short‐term memory (LSTM) and ARIMA‐SVM models, the improved ARIMA model based on LSTM not only improves the forecasting accuracy of the single ARIMA model in both fitting and forecasting, but also reduces the computational complexity of only a single deep learning model. The improved ARIMA model based on deep learning not only enriches the models for the forecasting of time series, but also provides effective tools for high‐frequency strategy design to reduce the investment risks of stock index.
- Sparse Bayesian vector autoregressions in huge dimensions
We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced‐form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation‐by‐equation estimation. Second, we apply recently developed global–local shrinkage priors to the VAR coefficients to cure the curse of dimensionality. Third, we utilize recent innovations to sample efficiently from high‐dimensional multivariate Gaussian distributions. This makes simulation‐based fully Bayesian inference feasible when the dimensionality is large but the time series length is moderate. We demonstrate the merits of our approach in an extensive simulation study and apply the model to US macroeconomic data to evaluate its forecasting capabilities.
- 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.
- Issue Information
No abstract is available for this article.
- Forecasting models in the manufacturing processes and operations management: Systematic literature review
The purpose of this paper is to present the result of a systematic literature review regarding the application and development of forecasting models in the industrial context, especially the context of manufacturing processes and operations management. The study was conducted considering the preparation of an established research protocol to know, discuss, and analyze the main approaches adopted by researchers in the field. To achieve this objective, we analyzed 354 recent papers published in periodicals between 2008 and 2018. This paper makes three main contributions to the field: (i) it presents an updated portfolio of prediction models in the industrial context, providing a reference point for researchers and industrial managers; (ii) it presents a characterization of the field of study through the identification of publication vehicles, frequency, and the principal authors and countries related to the development of research on the theme; (iii) it proposes a unified framework, listing the characteristics of the prediction models with their respective application contexts, identifying the current research directions to provide theoretical aids for the development of new approaches to forecasting in industry. The results of this study provide an empirical base for further discussions on studies that focus on forecasting in the industrial context.
- A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility
In a conditional predictive ability test framework, we investigate whether market factors influence the relative conditional predictive ability of realized measures (RMs) and implied volatility (IV), which is able to examine the asynchronism in their forecasting accuracy, and further analyze their unconditional forecasting performance for volatility forecast. Our results show that the asynchronism can be detected significantly and is strongly related to certain market factors, and the comparison between RMs and IV on average forecast performance is more efficient than previous studies. Finally, we use the factors to extend the empirical similarity (ES) approach for combination of forecasts derived from RMs and IV.
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- 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...
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