Dynamic Portfolio Optimization Using Generalized Dynamic Conditional Heteroskedastic Factor Models

Shiohama, Takayuki; Hallin, Marc; Veredas, David; Taniguchi, Masanobu

doi:10.14490/jjss.40.145

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…In order to investigate this, the APARCH(1,1), APARCH-X(1,1), and APARCH-CJ(1,1) models are compared by using 1-minute intraday high-frequency data from the Tokyo Stock Price Index (TOPIX) in Japan. A study of [10] showed that APARCH models have better performances compared with the GARCH model for the daily TOPIX Sector Indexes, which suggests the existence of an asymmetric effect. To our knowledge, there are no results for the estimation of the APARCH-CJ model.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Aparch-Type Models: Does the Continuous and Jump Components of Realized Volatility Improve the Fitting?

Nugroho,

Urosidin,

Parhusip

2024

BAREKENG: J. Math. & App.

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This study aims to extend an APARCH-X(1,1) model to the APARCH-CJ(1,1) by separating the exogenous variable X into two components: continuous and discontinuous (jump). The study was based on the application of models to 1-min intraday high-frequency data from the Tokyo Stock Price Index from 2004 to 2011, where its dependent variable is daily return and its exogenous variability is Realized Volatility. As a basic framework, the return errors follow a Normal distribution. An Adaptive Random Walk Metropolis (ARWM) method was constructed in the Markov Chain Monte Carlo algorithm to estimate model parameters so that the model fits the observed return time series. By visual inspection, the parameter trace plots showed good convergence of the Markov chains, indicating that the ARWM method is efficient in estimating the studied models. Based on the results of the Akaike Information Criterion for model fitting to data, this study found that APARCH-CJ(1,1) is inferior to APARCH(1,1).

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Section: Introductionmentioning

confidence: 99%

Comparison of Aparch-Type Models: Does the Continuous and Jump Components of Realized Volatility Improve the Fitting?

Nugroho,

Urosidin,

Parhusip

2024

BAREKENG: J. Math. & App.

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“…In these models, the market and the stock-specific components are assessed independently in an attempt to improve the covariances estimates (SHIOHAMA et al, 2010).…”

Section: A Note On the Homoscedasticity Hypothesis And Future Work Including Markov Chainsmentioning

confidence: 99%

A mean-field approach for the optimal control of discrete-time linear systems with multiplicative noises.

Barbieri¹

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In this work, we study the stochastic multi-period optimal control for discrete-time linear systems subject to multiplicative noises. Initially, we consider a multi-period mean-variance trade-off performance criterion for the finite-horizon case with and without constraints, and then, its infinite-horizon case with the long-run as well as the discount factor criteria. We adopt the mean-field approach to tackle the problems and get their solutions in terms of a set of two generalised coupled algebraic Riccati equations (GCARE for short). For the finite-horizon case, we derive the optimal control law for a general multi-period mean-variance problem and obtain the optimal control strategy for the constrained problems using the Lagrangian multipliers approach. From the general unrestricted result, we obtain a sufficient condition for a closed-form solution for one of the constrained problems considered in this work. For the infinite-horizon case, we establish sufficient conditions for the existence of the maximal solution, necessary and sufficient conditions for the existence of the mean-square stabilising solution to the GCARE, and derive the optimal control laws for the discounted and long-run problems. When particularised to the portfolio selection problem, we show that our results match some of the results available in the literature. A numerical example illustrates the obtained optimal controls for the multi-period portfolio selection problem in which is desired to optimise the sum of the mean-variance trade-off costs of a portfolio against a benchmark along the time.

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Dynamic Portfolio Optimization Using Generalized Dynamic Conditional Heteroskedastic Factor Models

Cited by 2 publications

References 49 publications

Comparison of Aparch-Type Models: Does the Continuous and Jump Components of Realized Volatility Improve the Fitting?

Comparison of Aparch-Type Models: Does the Continuous and Jump Components of Realized Volatility Improve the Fitting?

A mean-field approach for the optimal control of discrete-time linear systems with multiplicative noises.

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