|
on Forecasting |
| By: | Veiga, Helena; Ruiz, Esther; González-Rivera, Gloria; Gonçalves Mazzeu, Joao Henrique |
| Abstract: | We propose an extension of the Generalized Autocontour (G-ACR) tests (Gonzàlez-Rivera and Sun, 2015) for dynamic specifications of conditional densities (in-sample) and of forecast densities (out-of-sample). The new tests are based on probability integral transforms (PITs) computed from bootstrap conditional densities so that no assumption on the functional form of the density is needed. The proposed bootstrap procedure generates predictive densities that incorporate parameter uncertainty. In addition, the bootstrapped G-ACR tests enjoy standard asymptotic distributions. This approach is particularly useful to evaluate multi-step predictive densities whose functional form is unknown or difficult to obtain even in cases where the conditional density of the model is known. |
| Keywords: | PIT; Parameter Uncertainty; Model Evaluation; Distribution Uncertainty |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:23457&r=for |
| By: | Rachidi Kotchoni; Dalibor Stevanovic |
| Abstract: | This paper proposes a framework to produce multi-horizon forecasts of business cycle turning points, average forecasts of economic activity as well as conditional forecasts that depend on whether the horizon of interest belongs to a recession episode or not. Our forecasting models take the form of an autoregression (AR) of order one that is augmented with either a probability of recession or an inverse Mills ratio. Our empirical results suggest that a static Probit model that uses only the TS as regressor provides comparable fit to the data as more sophisticated non-static Probit models. We also find that the dynamic patterns of the term structure of recession probabilities are quite informative about business cycle turning points. Our most parsimonious AAR model delivers better out-of-sample forecasts of GDP growth than the benchmark models considered. We construct term structures of recession probabilities since the last oficial NBER turning point. The results suggest that there has been no harbinger of a recession for the US economy since 2010Q4 and that there is none to fear at least until 2018Q1. GDP growth is expected to rise steadily between 2016Q3 and 2018Q1 in the range [2.5%,3.5%]. |
| Keywords: | Augmented Autoregressive Model, Conditional Forecasts, Economic Activity, Inverse Mills Ratio, Probit, Recession, |
| JEL: | C35 C53 E27 E37 |
| Date: | 2016–08–05 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-36&r=for |
| By: | Ying Chen; Wolfgang K. Härdle; Wee Song Chua |
| Abstract: | Limit order book contains comprehensive information of liquidity on bid and ask sides. We propose a Vector Functional AutoRegressive (VFAR) model to describe the dynamics of the limit order book and demand curves and utilize the fitted model to predict the joint evolution of the liquidity demand and supply curves. In the VFAR framework, we derive a closed-form maximum likelihood estimator under sieves and provide the asymptotic consistency of the estimator. In application to limit order book records of 12 stocks in NASDAQ traded from 2 Jan 2015 to 6 Mar 2015, it shows the VAR model presents a strong predictability in liquidity curves, with R2 values as high as 98.5 percent for insample estimation and 98.2 percent in out-of-sample forecast experiments. It produces accurate 5䀀; 25䀀 and 50䀀-inute forecasts, with root mean squared error as low as 0.09 to 0.58 and mean absolute percentage error as low as 0.3 to 4.5 percent. |
| Keywords: | Limit order book, Liquidity risk, multiple functional time series |
| JEL: | C13 C32 C53 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2016-025&r=for |
| By: | Davide Pettenuzzo (Brandeis University); Konstantinos Metaxoglou (Carleton University); Aaron Smith (University of California, Davis) |
| Abstract: | We propose a new method to improve density forecasts of the equity premium us- ing information from options markets. We obtain predictive densities from a stae-of-the-art stochastic volatility (SV) model, which we then tilt towards the second moment of the risk-neutral distribution implied by options prices, while imposing a non-negativity constraint on the equity premium. By combining the backward-looking information contained in the SV model with the forward-looking information from options prices, our procedure delivers sharper predictive densities. Using density forecasts of the U.S. equity premium from January 1990 to December 2014, we find that tilting leads to more accurate predictions, both in terms of statistical and economic criteria. |
| Keywords: | entropic tilting, density forecasts, variance risk premium, equity premium, options. |
| JEL: | C11 C22 G11 G12 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:brd:wpaper:99r&r=for |