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Forecasting limit order book liquidity supply-demand curves with functional AutoRegressive dynamics
Chen, Ying; Chua, Wee Song; Härdle, Wolfgang - 2016
in liquidity curves, with R2 values as high as 98.5 percent for insample estimation and 98.2 percent in out …Limit order book contains comprehensive information of liquidity on bid and ask sides. We propose a Vector Functional … the joint evolution of the liquidity demand and supply curves. In the VFAR framework, we derive a closed-form maximum …
Persistent link: https://www.econbiz.de/10011518802
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Combined density nowcasting in an uncertain economic environment
Aastveit, Knut Are; Ravazzolo, Francesco; Dijk, Herman … - 2014
We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...
Persistent link: https://www.econbiz.de/10010465155
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Forecasting with Approximate Dynamic Factor Models : The Role of Non-Pervasive Shocks
Luciani, Matteo - 2011
In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with...
Persistent link: https://www.econbiz.de/10013120664
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Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction
Liao, Yin - 2012
Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting...
Persistent link: https://www.econbiz.de/10013105658
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Factor GARCH-ITO Models for High-Frequency Data with Application to Large Volatility Matrix Prediction
Kim, Donggyu - 2017
Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data … matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these … a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-Ito model. We also apply it to …
Persistent link: https://www.econbiz.de/10012941598
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Multivariate Overnight GARCH-Ito Model with Applications in Large Volatility Matrix Estimation and Prediction
Kim, Donggyu; Oh, Minseog; Song, Xinyu; Wang, Yazhen - 2022
This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction … weighted least squares estimation procedure for estimating model parameters with open-to-close high-frequency and close … the proposed estimation and prediction methods.The empirical analysis is carried out to compare the performance of the …
Persistent link: https://www.econbiz.de/10013290653
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Forecasts with Single-Equation Markov-Switching Model : An Application to the Gross Domestic Product of Latvia
Buss, Ginters - 2010
The paper compares one-period ahead forecasting performance of linear vector-autoregressive (VAR) models and single-equation Markov-switching (MS) models for two cases: when leading information is available and when it is not. The results show that single-equation MS models tend to perform...
Persistent link: https://www.econbiz.de/10013147524
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Low frequency and weighted likelihood solutions for mixed frequency dynamic factor models
Blasques, Francisco; Koopman, Siem Jan; Mallee, Max I. P. - 2014
some variable equations be of more importance during the estimation process. We derive the asymptotic properties of the … the weighted estimation method in a Monte Carlo study to investigate the effect of differen t choices for the weights in …
Persistent link: https://www.econbiz.de/10010391543
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Assessing Point Forecast Bias Across Multiple Time Series : Measures and Visual Tools
Davydenko, Andrey; Goodwin, Paul - 2021
Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods...
Persistent link: https://www.econbiz.de/10013314570
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Forecasting in Factor Augmented Regressions under Structural Change
Massacci, Daniele; Kapetanios, George - 2021
recent break tends to produce more accurate forecasts than unconditional estimation methods based on expanding or rolling …
Persistent link: https://www.econbiz.de/10013322730