Explanipedia

Maximum Likelihood Estimation for Singular Wishart Distribution Open

Manabu Asai · 2025

Dynamic Network Poisson Autoregression with Application to COVID-19 Count Data Open

Manabu Asai, Amanda M. Y. Chu, Mike K. P. So · 2024

There is growing interest in accommodating network structure in panel data models. We consider dynamic network Poisson autoregressive (DN-PAR) models for panel count data, enabling their use in regard to a time-varying network structure. W…

Factor multivariate stochastic volatility models of high dimension Open

Benjamin Poignard, Manabu Asai · 2024

Building upon the pertinence of the factor decomposition to break the curse of dimensionality inherent to multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework that relies on…

Estimation of Realized Asymmetric Stochastic Volatility Models Using Kalman Filter Open

Manabu Asai · 2023

Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrat…

Realized BEKK-CAW Models Open

Manabu Asai, Mike K. P. So · 2022

Estimating time-varying conditional covariance matrices of financial returns play important role in portfolio analysis, risk management, and financial econometrics research. The availability of high-frequency financial data can provide an …

High-Dimensional Sparse Multivariate Stochastic Volatility Models Open

Benjamin Poignard, Manabu Asai · 2022

Although multivariate stochastic volatility models usually produce more accurate forecasts compared to the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fas…

Multivariate Hyper-Rotated GARCH-BEKK Open

Manabu Asai, Michael McAleer · 2022

For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (20…

Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers Open

Manabu Asai, Chia‐Lin Chang, Michael McAleer · 2021

Asymptotic and Finite Sample Properties for Multivariate Rotated GARCH Models Open

Manabu Asai, Chia‐Lin Chang, Michael McAleer, Laurent L. Pauwels · 2021

This paper derives the statistical properties of a two-step approach to estimating multivariate rotated GARCH-BEKK (RBEKK) models. From the definition of RBEKK, the unconditional covariance matrix is estimated in the first step to rotate t…

A Penalised OLS Framework for High-Dimensional Multivariate Stochastic Volatility Models Open

Benjamin Poignard, Manabu Asai · 2020

Although multivariate stochastic volatility (MSV) models usually produce more accurate forecasts compared to multivariate GARCH models, their estimation techniques such as Monte Carlo likelihood or Bayesian Markov Chain Monte Carlo are com…

The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures Open

Manabu Asai, Rangan Gupta, Michael McAleer · 2019

This paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects for daily crude oil and gold futures. We use a modified version of the jump-robust covariance estimator of Koike (2016), such that…

The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures Open

Manabu Asai, Rangan Gupta, Michael McAleer · 2019

The paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects. We modify the jump-robust covariance estimator of Koike (2016), such that the estimated matrix is positive definite. Using this ap…

Realized stochastic volatility models with generalized Gegenbauer long memory Open

Manabu Asai, Michael McAleer, Shelton Peiris · 2019

Asymptotic Theory for Rotated Multivariate GARCH Models Open

Manabu Asai, Chia‐Lin Chang, Michael McAleer, Laurent L. Pauwels · 2018

In this paper, we derive the statistical properties of a two step approach to estimating multivariate GARCH rotated BEKK (RBEKK) models. By the definition of rotated BEKK, we estimate the unconditional covariance matrix in the first step i…

Cointegrated Dynamics for A Generalized Long Memory Process: An Application to Interest Rates Open

Manabu Asai, Shelton Peiris, Michael McAleer, David E. Allen · 2018

markdownabstractRecent developments in econometric methods enable estimation and testing of general long memory process, which include the general Gegenbauer process. This paper considers the error correction model for a vector general lon…

Cointegrated Dynamics for A Generalized Long Memory Process Open

Manabu Asai, Shelton Peiris, Michael McAleer, David E. Allen · 2018

Recent developments in econometric methods enable estimation and testing of general long memory process, which include the general Gegenbauer process. This paper considers the error correction model for a vector general long memory process…

Bayesian Analysis of Realized Matrix-Exponential GARCH Models Open

Manabu Asai, Michael McAleer · 2018

Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models Open

Shelton Peiris, Manabu Asai, Michael McAleer · 2017

This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine…

Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory Open

Manabu Asai, Michael McAleer, Shelton Peiris · 2017

Fractionally differenced processes have received a great deal of attention due to their flexibility in financial applications with long memory. In this paper, new realized stochastic volatility (RSV) models are developed: one is a RSV mode…

Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes Open