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on Forecasting |
| By: | Andrea Carriero; Todd E. Clark; Massimiliano Marcellino |
| Abstract: | Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related to how and to what extent to include shrinkage, and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, using for evaluation both quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles spanning from the left to right tail. We find that shrinkage is generally helpful to tail forecast accuracy, with gains that are particularly large for GDP applications featuring large sets of predictors and unemployment and inflation applications, and with gains that increase with the forecast horizon. |
| Keywords: | Quantile regression; tail forecasting; shrinkage; Bayesian methods; quantile scores |
| JEL: | C53 E17 E37 F47 |
| Date: | 2022–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:94690&r= |
| By: | Byron Botha (Codera Analytics); Rulof Burger (Department of Economics, University of Stellenbosch, Stellenbosch, 7601, South Africa.); Kevin Kotze (School of Economics, University of Cape Town); Neil Rankin (Predictive Insights, 3 Meson Street, Techno Park, Stellenbosch, 7600, South Africa.); Daan Steenkamp (Codera Analytics and Research Fellow, Department of Economics, Stellenbosch University.) |
| Abstract: | We investigate whether the use of statistical learning techniques and big data can enhance the accuracy of inflation forecasts. We make use of a large dataset for the disaggregated prices of consumption goods and services, which we partially reconstruct, and a large suite of different statistical learning and traditional time series models. We find that the statistical learning models are able to compete with most benchmarks over medium to longer horizons, despite the fact that we only have a relatively small sample of available data, but are usually inferior over shorter horizons. Our findings suggest that this result may be attributed to the ability of these models to make use of relevant information, when it is available, and may be particularly useful during periods of crisis, when deviations from the steady state are more persistent. We find that the accuracy of the central bank's near-term inflation forecasts compare favourably with those of other models, while the inclusion of off-model information, such as electricity tariff adjustments and other sources of within-month data, provides these models with a competitive advantage. Lastly, we also investigate the relative performance of the different models as we experienced the effects of the pandemic. |
| JEL: | C10 C11 C52 C55 E31 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ctn:dpaper:2022-03&r= |
| By: | Zheng Cao; Wenyu Du; Kirill V. Golubnichiy |
| Abstract: | This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.14385&r= |