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Bayesian predictive distributions of oil returns using mixed data sampling volatility models
Virbickaite, Audrone; Nguyen, Hoang; Tran, Minh-Ngoc - 2023
such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and … relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative … inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV …
Persistent link: https://www.econbiz.de/10014331159
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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction
Duong, Diep; Swanson, Norman R. - 2013
Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...
Persistent link: https://www.econbiz.de/10010334248
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Prediction and simulation using simple models characterized by nonstationarity and seasonality
Swanson, Norman; Urbach, Richard - 2013
measures. Our empirical analysis centers around the implementation of a series of simulation and prediction experiments, as … well as a discussion of the stochastic properties of seasonal unit root models. Our prediction experiments are based on … of greater than one-step ahead, our SUR model perform poorly when used for prediction, suggesting that parameter …
Persistent link: https://www.econbiz.de/10010334249
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Predictive inference for integrated volatility
Corradi, Valentina; Distaso, Walter; Swanson, Norman R. - 2011
In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned...
Persistent link: https://www.econbiz.de/10010282869
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Bayesian semiparametric multivariate GARCH modeling
Jensen, Mark J.; Maheu, John M. - 2012
This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given...
Persistent link: https://www.econbiz.de/10010292242
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Estimating a semiparametric asymmetric stochastic volatility model with a dirichlet process mixture
Jensen, Mark J.; Maheu, John M. - 2012
In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...
Persistent link: https://www.econbiz.de/10010292350
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Economic value of modeling the joint distribution of returns and volatility: Leverage timing
Çakmaklı, Cem; Öztürk, Verda - 2021
We propose a joint modeling strategy for timing the joint distribution of the returns and their volatility. We do this by incorporating the potentially asymmetric links into the system of 'independent' predictive regressions of returns and volatility, allowing for asymmetric cross-correlations,...
Persistent link: https://www.econbiz.de/10012628462
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The random walk of high frequency trading
Aldrich, Eric M.; Heckenbach, Indra; Laughlin, Gregory - 2015
This paper builds a model of high-frequency equity returns by separately modeling the dynamics of trade-time returns and trade arrivals. Our main contributions are threefold. First, we characterize the distributional behavior of high-frequency asset returns both in ordinary clock time and in...
Persistent link: https://www.econbiz.de/10011406341
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Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends
McCloskey, Adam - 2012
I provide conditions under which the trimmed FDQML estimator, advanced by McCloskey (2010) in the context of fully parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of additive low-frequency contamination in log-squared...
Persistent link: https://www.econbiz.de/10010420267
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Empirical evidence on jumps and large fluctuations in individual stocks
Doung, Diep; Swanson, Norman - 2011
our prediction experiments using the class of linear and nonlinear HAR-RV, HAR-RV-J and HAR-RV-CJ models proposed by …
Persistent link: https://www.econbiz.de/10010282828