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1.
通过专项力量训练后进行助跑摸高的实验研究，深入探讨助跑摸高训练对跳高成绩的影响．结果表明：力量训练中增加助跑摸高练习，不但能有效地解决发展力量与技术练习相脱节的矛盾，而且有助于提高跳高成绩． 相似文献
2.
Xiaohang Ren;Wenting Jiang;Qiang Ji;Pengxiang Zhai; 《Journal of forecasting》2024,43(7):2809-2821
In this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image-based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low-frequency models for high-frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends. 相似文献
3.
Philip Hans Franses;Jiahui Zou;Wendun Wang; 《Journal of forecasting》2024,43(8):3194-3202
This paper puts forward a new and simple method to combine forecasts, which is particularly useful when the forecasts are strongly correlated. It is based on the Mincer Zarnowitz regression, and a subsequent determination using Shapley values of the weights of the forecasts in a new combination. For a stylized case, it is proved that such a Shapley-value-based combination improves upon an equal-weight combination. Simulation experiments and a detailed illustration show the merits of the Shapley-value-based forecast combination. 相似文献
4.
Gongyue Jiang;Gaoxiu Qiao;Lu Wang;Feng Ma; 《Journal of forecasting》2024,43(6):2378-2398
From the cross-market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data-driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH-type models with jumps, and the forecasting effects of five non-parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross-market research. 相似文献
5.
Partha Sengupta;Christopher H. Wheeler; 《Journal of forecasting》2024,43(7):2448-2477
Models developed by banks to forecast losses in their credit card portfolios have generally performed poorly during the COVID-19 pandemic, particularly in 2020, when large forecast errors were observed at many banks. In this study, we attempt to understand the source of this error and explore ways to improve model fit. We use account-level monthly performance data from the largest credit card banks in the U.S. between 2008 and 2018 to build models that mimic the typical model design employed by large banks to forecast credit card losses. We then fit these on data from 2019 to 2021. We find that COVID-period model errors can be reduced significantly through two simple modifications: (1) including measures of the macroeconomic environment beyond indicators of the labor market, which served as the primary macro drivers used in many pre-pandemic models and (2) adjusting macro drivers to capture persistent/sustained changes, as opposed to temporary volatility in these variables. These model improvements, we find, can be achieved without a significant reduction in model performance for the pre-COVID period, including the Great Recession. Moreover, in broadening the set of macro influences and capturing sustained changes, we believe models can be made more robust to future downturns, which may bear little resemblance to past recessions. 相似文献
6.
This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy. 相似文献
7.
This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting. 相似文献
8.
In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies. 相似文献
9.
The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms. 相似文献
10.
Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons. 相似文献