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on Forecasting |
| By: | Udai Nagpal; Krishan Nagpal |
| Abstract: | Though various approaches have been considered, forecasting near-term market changes of equities and similar market data remains quite difficult. In this paper we introduce an approach to forecast near-term market changes for equity indices as well as portfolios using variational inference (VI). VI is a machine learning approach which uses optimization techniques to estimate complex probability densities. In the proposed approach, clusters of explanatory variables are identified and market changes are forecast based on cluster-specific linear regression. Apart from the expected value of changes, the proposed approach can also be used to obtain the distribution of possible outcomes, which can be used to estimate confidence levels of forecasts and risk measures such as VaR (Value at Risk) for the portfolio. Another advantage of the proposed approach is the clear model interpretation, as clusters of explanatory variables (or market regimes) are identified for which the future changes follow similar relationships. Knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. Illustrative examples of equity and bond indices are considered to demonstrate forecasts of the proposed approach during Covid-related volatility in early 2020 and subsequent benign market conditions. For the portfolios considered, it is shown that the proposed approach provides useful forecasts in both normal and volatile markets even with only a few explanatory variables. Additionally the predicted estimate and distribution adapt quickly to changing market conditions and thus may also be useful in obtaining better real-time estimates of risk measures such as VaR compared to traditional approaches. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.00605&r= |
| By: | Reisenhofer, Rafael; Bayer, Xandro; Hautsch, Nikolaus |
| Abstract: | Despite the impressive success of deep neural networks in many application areas, neural network models have so far not been widely adopted in the context of volatility forecasting. In this work, we aim to bridge the conceptual gap between established time series approaches, such as the Heterogeneous Autoregressive (HAR) model (Corsi, 2009), and state-of-the-art deep neural network models. The newly introduced HARNet is based on a hierarchy of dilated convolutional layers, which facilitates an exponential growth of the receptive field of the model in the number of model parameters. HARNets allow for an explicit initialization scheme such that before optimization, a HARNet yields identical predictions as the respective baseline HAR model. Particularly when considering the QLIKE error as a loss function, we find that this approach significantly stabilizes the optimization of HARNets. We evaluate the performance of HARNets with respect to three different stock market indexes. Based on this evaluation, we formulate clear guidelines for the optimization of HARNets and show that HARNets can substantially improve upon the forecasting accuracy of their respective HAR baseline models. In a qualitative analysis of the filter weights learnt by a HARNet, we report clear patterns regarding the predictive power of past information. Among information from the previous week, yesterday and the day before, yesterday's volatility makes by far the most contribution to today's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly when moving further into the past. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:680&r= |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Shahnaz Parsaeian (University of Kansas); Aman Ullah (University of California Riverside) |
| Abstract: | In forecasting a time series containing a structural break, it is important to determine how much weight can be given to the observations prior to the time when the break occurred. In this context, Pesaran et al. (2013) (PPP) proposed a weighted least squares estimator by giving different weights to observations before and after a break point for forecasting out-of-sample. We revisit their approach by introducing an improved weighted generalized least squares estimator (WGLS) using a weight (kernel) function to give different weights to observations before and after a break. The kernel weight is estimated by cross-validation rather than analytically derived from a parametric model as in PPP. Therefore, the WGLS estimator facilitates implementation of the PPP method for the optimal use of the pre-break and post-break sample observations without having to derive the parametric weights which may be misspecified. We show that the kernel weight estimated by cross-validation is asymptotically optimal in the sense of Li (1987). Monte Carlo simulations and an empirical application to forecasting equity premium are provided for verification and illustration. |
| Keywords: | Forecasting, Cross-validation, Kernel, Structural breaks, Model averaging |
| JEL: | C14 C22 C53 |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:202210&r= |
| By: | James Mitchell; Aubrey Poon; Dan Zhu |
| Abstract: | Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the 'data speak.' Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions. |
| Keywords: | Density Forecasts; Quantile Regressions; Financial Conditions |
| JEL: | C53 E32 E37 E44 |
| Date: | 2022–05–09 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:94160&r= |