|
on Forecasting |
| By: | Dumitru, Ana-Maria; Hizmeri, Rodrigo; Izzeldin, Marwan |
| Abstract: | This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons. |
| Keywords: | realized volatility,forecast,intraday periodicity,heterogeneous autoregressive models |
| JEL: | C14 C22 C58 G17 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:193631&r=all |
| By: | Voisin, Elisa; Hecq, Alain |
| Abstract: | This paper investigates one-step ahead density forecasts of mixed causal-noncausal models. We compare the sample-based and the simulations-based approaches respectively developed by Gouriéroux and Jasiak (2016) and Lanne, Luoto, and Saikkonen (2012). We focus on explosive episodes and therefore on predicting turning points of bubbles bursts. We suggest the use of both methods to construct investment strategies based on how much probabilities are induced by the assumed model and by past behaviours. We illustrate our analysis on Nickel prices series. |
| Keywords: | Noncausal models, forecasting, predictive densities, bubbles, simulations-based forecasts |
| JEL: | C22 C53 C58 |
| Date: | 2019–03–13 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:92734&r=all |
| By: | Mawuli Segnon; Stelios Bekiros |
| Abstract: | In this paper, we revisit the stylized facts of cryptocurrency markets and propose various approaches for modeling the dynamics governing the mean and variance processes. We first provide the statistical properties of our proposed models and study in detail their forecasting performance and adequacy by means of point and density forecasts. We adopt two loss functions and the model confidence set (MSC) test to evaluate the predictive ability of the models and the likelihood ratio test to assess their adequacy. Our results confirm that cryptocurrency markets are characterized by regime shifting, long memory and multifractality. We find that the Markov switching multifractal (MSM) and FIGARCH models outperform other GARCH-type models in forecasting bitcoin returns volatility. Furthermore, combined forecasts improve upon forecasts from individual models. |
| Keywords: | Bitcoin, Multifractal processes, GARCH processes, Model confidence set, Likelihood ratio test |
| JEL: | C22 C53 C58 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:7919&r=all |
| By: | María Gil (Banco de España); Danilo Leiva-Leon (Banco de España); Javier J. Pérez (Banco de España); Alberto Urtasun (Banco de España) |
| Abstract: | The goal of this paper is to propose a model to produce nowcasts of GDP growth of Spanish regions, by means of dynamic factor models. This framework is capable to incorporate in a parsimonious way the relevant information available at the time that each forecast is made. We employ a Bayesian perspective to provide robust estimation of all the ingredients involved in the model. Accordingly, we introduce the Bayesian Factor model for Regions (BayFaR), which allows for the inclusion of missing data and combines quarterly data on regional real output growth (taken from the database of the AIReF and from the individual regional statistics institutes, when available) and monthly information associated to indicators of regional real activity. We apply the BayFaR to nowcast the GDP growth of the four largest regions of Spain, and illustrate the real-time nowcasting performance of the proposed framework for each case. We also apply the model to nowcast Spanish GDP in order to be able to assess the relative growth of each region. |
| Keywords: | regional activity, nowcasting, dynamic factor model |
| JEL: | C32 E37 R13 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:bde:opaper:1904&r=all |