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on Forecasting |
| By: | Francesco Audrino; Jonathan Chassot |
| Abstract: | We investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1, 455 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re-estimation frequency, in determining the HAR model's performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set by HAR when utilizing a refined fitting approach for the latter. Moreover, the simplicity of HAR allows for an interpretable model with drastically lower computational costs. We assess performance using QLIKE, MSE, and realized utility metrics, finding that HAR consistently outperforms its ML counterparts when both rely solely on realized volatility and VIX as predictors. Our results underscore the importance of a correctly specified fitting scheme. They suggest that properly fitted HAR models provide superior forecasting accuracy, establishing robust guidelines for their practical application and use as a benchmark. This study not only reaffirms the efficacy of the HAR model but also provides a critical perspective on the practical limitations of ML approaches in realized volatility forecasting. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.08041&r= |
| By: | Milen Arro-Cannarsa; Dr. Rolf Scheufele |
| Abstract: | We compare several machine learning methods for nowcasting GDP. A large mixed-frequency data set is used to investigate different algorithms such as regression based methods (LASSO, ridge, elastic net), regression trees (bagging, random forest, gradient boosting), and SVR. As benchmarks, we use univariate models, a simple forward selection algorithm, and a principal components regression. The analysis accounts for publication lags and treats monthly indicators as quarterly variables combined via blocking. Our data set consists of more than 1, 100 time series. For the period after the Great Recession, which is particularly challenging in terms of nowcasting, we find that all considered machine learning techniques beat the univariate benchmark up to 28 % in terms of out-of-sample RMSE. Ridge, elastic net, and SVR are the most promising algorithms in our analysis, significantly outperforming principal components regression. |
| Keywords: | Nowcasting, Forecasting, Machine learning, Rridge, LASSO, Elastic net, Random forest, Bagging, Boosting, SVM, SVR, Large data sets |
| JEL: | C53 C55 C32 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2024-06&r= |
| By: | Barrio Castro, Tomás del; Escribano, Álvaro; Sibbertsen, Philipp |
| Abstract: | This paper identifies and estimates the relevant cycles in paleoclimate data of earth temperature, ice volume and CO2. Cyclical cointegration analysis is used to connect these cycles to the earth eccentricity and obliquity and to see that the earth surface temperature and ice volume are closely connected. These findings are used to build a forecasting model including the cyclical component as well as the relevant earth and climate variables which outperforms models ignoring the cyclical behaviour of the data. Especially the turning points can be predicted accurately using the proposed approach. Out of sample forecasts for the turning points of earth temperature, ice volume and CO2 are derived. |
| Keywords: | Paleoclimate Cycles; Cyclical Fractional Cointegration; Forecasting Climate Data |
| Date: | 2024–06–17 |
| URL: | https://d.repec.org/n?u=RePEc:cte:werepe:43987&r= |
| By: | Bivas Dinda |
| Abstract: | The recent advancement of deep learning architectures, neural networks, and the combination of abundant financial data and powerful computers are transforming finance, leading us to develop an advanced method for predicting future stock prices. However, the accessibility of investment and trading at everyone's fingertips made the stock markets increasingly intricate and prone to volatility. The increased complexity and volatility of the stock market have driven demand for more models, which would effectively capture high volatility and non-linear behavior of the different stock prices. This study explored gated recurrent neural network (GRNN) algorithms such as LSTM (long short-term memory), GRU (gated recurrent unit), and hybrid models like GRU-LSTM, LSTM-GRU, with Tree-structured Parzen Estimator (TPE) Bayesian optimization for hyperparameter optimization (TPE-GRNN). The aim is to improve the prediction accuracy of the next day's closing price of the NIFTY 50 index, a prominent Indian stock market index, using TPE-GRNN. A combination of eight influential factors is carefully chosen from fundamental stock data, technical indicators, crude oil price, and macroeconomic data to train the models for capturing the changes in the price of the index with the factors of the broader economy. Single-layer and multi-layer TPE-GRNN models have been developed. The models' performance is evaluated using standard matrices like R2, MAPE, and RMSE. The analysis of models' performance reveals the impact of feature selection and hyperparameter optimization (HPO) in enhancing stock index price prediction accuracy. The results show that the MAPE of our proposed TPE-LSTM method is the lowest (best) with respect to all the previous models for stock index price prediction. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.02604&r= |
| By: | Seulki Chung |
| Abstract: | This paper presents a comparative analysis of univariate and multivariate GARCH-family models and machine learning algorithms in modeling and forecasting the volatility of major energy commodities: crude oil, gasoline, heating oil, and natural gas. It uses a comprehensive dataset incorporating financial, macroeconomic, and environmental variables to assess predictive performance and discusses volatility persistence and transmission across these commodities. Aspects of volatility persistence and transmission, traditionally examined by GARCH-class models, are jointly explored using the SHAP (Shapley Additive exPlanations) method. The findings reveal that machine learning models demonstrate superior out-of-sample forecasting performance compared to traditional GARCH models. Machine learning models tend to underpredict, while GARCH models tend to overpredict energy market volatility, suggesting a hybrid use of both types of models. There is volatility transmission from crude oil to the gasoline and heating oil markets. The volatility transmission in the natural gas market is less prevalent. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.19849&r= |