Journal of Forecasting最新文献

This paper presents an efficient heuristic to generate multi-stage scenario trees for portfolio selection problems. In the case of two or more risky assets, investors need to account for the complex multivariate dependence among different assets. The dependence patterns have shown not only asymmetric and fat tails but also time-varying, and the upper and lower tails have different effect on portfolio management. In this paper, we design a new scenario generation method by combining the GARCH-type model and vine copula model to properly reflect these complex dependence patterns in multiple assets in a flexible way. A multi-stage scenario tree is generated sequentially from this model by simultaneously utilizing the simulation and clustering methods. The scenarios' nodal probabilities are determined by solving an improved moment matching model, whose objective is to maintain the central moments and lower tails of the original distribution. The resulting scenario trees are then tested on a multi-stage portfolio selection model. The experimental results prove the efficiency and advantages of our proposed scenario generation method over other existing models or methods and the positive influence of moment matching on our method.

This systematic literature review examines cryptocurrency forecasting trends, influential sources, and research themes. Following PRISMA guidelines, 168 articles from Q1 or A-tier journals in the Scopus database were analyzed using bibliometric techniques. The findings reveal a significant increase in cryptocurrency forecasting research output since 2017, particularly in 2021. “Finance Research Letters” emerges as the most productive journal, whereas “Economics Letters” receives the highest number of citations. Elie Bouri is identified as the most prolific author, and China is the top contributor country. Key research themes include bitcoin, cryptocurrency, volatility, forecasting, machine learning, investments, and blockchain. Future research directions involve utilizing internet search-based measures, time-varying mixture models, economic policy uncertainty, expert predictions, machine learning algorithms, and analyzing cryptocurrency risk. This review contributes unique insights into the field's growth, influential sources, and collaborative structures and offers a foundation for advancing methodology and enhancing cryptocurrency forecasting models.

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high-frequency electricity consumption data from industrial firms for the second-largest German state, the Free State of Bavaria, we conduct a pseudo out-of-sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high-frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity.

In this paper, we propose a correlation-based test for the evaluation of two competing forecasts. Under the null hypothesis of equal correlations with the target variable, we derive the asymptotic distribution of our test using the Delta method. This null hypothesis is not necessarily equivalent to the null of equal Mean Squared Prediction Errors (MSPE). Specifically, it might be the case that the forecast displaying the lowest MSPE also exhibits the lowest correlation with the target variable: this is known as “The MSPE paradox.” In this sense, our approach should be seen as complementary to traditional tests of equality in MSPE. Monte Carlo simulations indicate that our test has good size and power. Finally, we illustrate the use of our test in an empirical exercise in which we compare two different inflation forecasts for a sample of OECD economies. We find more rejections of the null of equal correlations than rejections of the null of equality in MSPE.

Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered.

Peak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non-convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave-convex programming algorithm is used to solve the non-convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non-convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non-convex support vector regression in robustness and generalization ability.

Predicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be observed in batches, each of which is based on a large number of such trials and can be modeled via multinomial distributions. We postulate that the multinomial probabilities of the categories vary randomly depending on batches. The challenge is to predict accurately on cumulative data based on data up to a few batches as early as possible. On the theoretical front, we first derive sufficient conditions of asymptotic normality of the estimates of the multinomial cell probabilities and present corresponding suitable transformations. Then, in a Bayesian framework, we consider hierarchical priors using multivariate normal and inverse Wishart distributions and establish the posterior convergence. The desired inference is arrived at using these results and ensuing Gibbs sampling. The methodology is demonstrated with election data from two different settings—one from India and the other from the United States. Additional insights of the effectiveness of the proposed methodology are attained through a simulation study.

The Federal Open Market Committee (FOMC) is a component of the Federal Reserve System responsible for overseeing open market operations. The FOMC meets roughly eight or more times per year to assess the economy of the United States. After each meeting, the FOMC releases a statement to the press outlining its assessment of the US economy and its monetary policy stance. The sentiment of these statements may have an influence on the US economy and financial markets. Using sentiment and correlational analyses, this research examines how the sentiment of these statements affects the US economy and financial markets by analyzing how FOMC statement sentiment is correlated with the Consumer Price Index (CPI), the National Financial Conditions Index (NFCI), and the Adjusted National Financial Conditions Index (ANFCI). We find evidence to suggest that there is a moderate negative correlation between an FOMC statement's sentiment and the US City Average CPI value associated with the month before and the month after the statement's release. We also find that there is no evidence to suggest there exists a correlation between an FOMC statement's sentiment and the NFCI value associated with the week before or the week after the statement's release. However, we do find evidence to suggest that there is a moderate negative correlation between an FOMC statement's sentiment and the ANFCI value associated with the week before and the week after the statement's release. We also found that out of the three models we tested (linear regression, vine copula regression, and Gaussian copula regression), the Gaussian copula regression model performs the best when forecasting the CPI and the ANFCI. Additionally, we find that when forecasting CPI values, the models that include FOMC statement sentiment are more accurate than the models that exclude FOMC statement sentiment.

This paper proposes a cross-section long short-term memory (CS-LSTM) factor model to explore the possibility of estimating expected returns in the Chinese stock market. In contrast to previous machine-learning-based asset pricing models that make predictions directly on equity returns, CS-LSTM estimates are based on predictions of slope terms from Fama–MacBeth cross-section regressions using 16 stock characteristics as factor loadings. In line with previous studies in the context of the Chinese market, we find illiquidity and short-term momentum to be the most important factors in describing asset returns. By using 274 value-weighted portfolios as test assets, we systematically compare the performances of CS-LSTM and three other candidate models. Our CS-LSTM model consistently delivers better performance than the candidate models and beats the market at all different levels of transaction costs. In addition, we observe that assets with smaller cap are favored by the model. By repeating the empirical analysis based on the top 70% of stocks, our CS-LSTM model remains robust and consistently provides significant market-beating performance. Our findings from the CS-LSTM model have practical implications for the future development of the Chinese stock market and other emerging markets.

In this study, we propose the application of the GARCH-EVT-Copula model in estimating liquidity-adjusted value-at-risk (L-VaR) of energy stocks while modeling nonlinear dependence between return and bid-ask spread. Using the L-VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non-zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student-t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH-EVT-Clayton copula is superior and most consistent in forecasting L-VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.