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on Forecasting |
| By: | Li Li; Yanfei Kang; Feng Li |
| Abstract: | In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecasting combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to add weight to the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point and density forecasts. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.02082&r= |
| By: | Han Lin Shang; Fearghal Kearney |
| Abstract: | This paper presents static and dynamic versions of univariate, multivariate, and multilevel functional time-series methods to forecast implied volatility surfaces in foreign exchange markets. We find that dynamic functional principal component analysis generally improves out-of-sample forecast accuracy. More specifically, the dynamic univariate functional time-series method shows the greatest improvement. Our models lead to multiple instances of statistically significant improvements in forecast accuracy for daily EUR-USD, EUR-GBP, and EUR-JPY implied volatility surfaces across various maturities, when benchmarked against established methods. A stylised trading strategy is also employed to demonstrate the potential economic benefits of our proposed approach. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.14026&r= |
| By: | Kajal Lahiri (University at Albany, SUNY); Huaming Peng (Rensselaer Polytechnic Institute); Xuguang Simon Sheng (American University) |
| Abstract: | From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, we find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty. |
| Keywords: | Central Bank Communication, Disagreement, Ensemble, Forecast Combination, Panel Data, Uncertainty |
| JEL: | C12 C33 E37 |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2021-005&r= |
| By: | Eghbal Rahimikia; Stefan Zohren; Ser-Huang Poon |
| Abstract: | We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple ensemble model, combining our word embedding and another machine learning model that uses limit order book data, provides the best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP (SHapley Additive exPlanations) to make the results more 'explainable' and the model comparisons more transparent. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.00480&r= |
| By: | Zihao Wang; Kun Li; Steve Q. Xia; Hongfu Liu |
| Abstract: | We investigate the effectiveness of different machine learning methodologies in predicting economic cycles. We identify the deep learning methodology of Bi-LSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S. We adopt commonly-available macro and market-condition features to compare the ability of different machine learning models to generate good predictions both in-sample and out-of-sample. The proposed model is flexible and dynamic when both predictive variables and model coefficients vary over time. It provided good out-of-sample predictions for the past two recessions and early warning about the COVID-19 recession. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10980&r= |