Time series models for realized covariance matrices based on the matrix-F distribution

Zhou, Jiayuan; Jiang, Feiyu; Zhu, Ke; Li, Wai Keung

doi:10.5705/ss.202019.0424

Cited by 4 publications

(8 citation statements)

References 43 publications

(25 reference statements)

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“…To explore the superiority of our model, we first conduct simulation studies in this section. Firstly, we generate a dataset based on the Wishart distribution in (12) and DCAW model in (13). Secondly, we change the underlying Wishart distribution to matrix-F distribtion [13] and generate another dataset.…”

Section: Simulationsmentioning

confidence: 99%

CNN-based Realized Covariance Matrix Forecasting

Fang¹,

Yu²,

Tang³

2021

Preprint

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It is well known that modeling and forecasting realized covariance matrices of asset returns play a crucial role in the field of finance. The availability of high frequency intraday data enables the modeling of the realized covariance matrices directly. However, most of the models available in the literature depend on strong structural assumptions and they often suffer from the curse of dimensionality. We propose an end-to-end trainable model built on the CNN and Convolutional LSTM (ConvLSTM) which does not require to make any distributional or structural assumption but could handle highdimensional realized covariance matrices consistently. The proposed model focuses on local structures and spatiotemporal correlations. It learns a nonlinear mapping that connect the historical realized covariance matrices to the future one. Our empirical studies on synthetic and real-world datasets demonstrate its excellent forecasting ability compared with several advanced volatility models.

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Section: Simulationsmentioning

confidence: 99%

CNN-based Realized Covariance Matrix Forecasting

Fang¹,

Yu²,

Tang³

2021

Preprint

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“…In addition, Asai and So (2013) considered the inverse Wishart distribution for U t motivated by Bayesian analysis, whereas Zhou et al . (2018) suggested using the matrix‐ F distribution.…”

Section: The Conditional Autoregressive Wishart Modelmentioning

confidence: 99%

“…(2012), and Zhou et al . (2018) suggested the BEKK‐type specification (see Baba et al . (1985) and Engle and Kroner (1995)).…”

Section: Introductionmentioning

confidence: 99%

“…(2018), and Zhou et al . (2018), among others, developed dynamic models for a realized covariance matrix measure. To guarantee the positive definiteness of the forecasts, Asai and So (2013), Golosnoy et al .…”

Section: Introductionmentioning

confidence: 99%

“…(2018), and Zhou et al . (2018) accommodated the RV errors in the covariance matrix with a multiplicative form. Especially, Golosnoy et al .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models

2020

Journal Time Series Analysis

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In this article, we consider a quasi‐maximum likelihood (QML) estimation of conditional autoregressive Wishart models, which generalize the conditional autoregressive Wishart models by not restricting the conditional distribution of covariances to follow the Wishart distribution. Strong consistency is established under the existence of the expectation of the log of the determinant. Sufficient conditions for asymptotic normality of the QML estimator are derived. Monte Carlo experiments show an inefficiency caused by using non‐Wishart distributions, which are negligible for the sample size T = 500. We use the daily covariance matrix of the returns of the Nikkei 225 index and its futures for the QML estimation of the conditional autoregressive Wishart model. The results indicate its appropriateness for the QML estimation.

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The Linkage between Sustainable Development Goals 9 and 11: Examining the Association between Sustainable Urbanization and Intellectual Property Rights Protection

Gao

Zhu

2022

Advanced Sustainable Systems

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United Nations Sustainable Development Goals (SDGs) 9 and 11 advocate industrial innovation and sustainable cities, respectively, although how sustainable urban development ensures the protection of innovative achievements remains understudied. Employing the Spatial Durbin Model, this study examines how the protection of intellectual property rights (IPRs) has affected sustainable urbanization using the case of the Huaihai economic zone (HEZ), China. Adopting a theoretical and empirical approach, the study seeks to understand how sustainable urbanization affects the protection of IPRs, and explores the spatial spillover from the perspectives of economic growth structure, technological progress, economic agglomeration and social welfare enhancement, taking into account also the protection of IPRs at a sub‐national level. The results indicate that sustainable urbanization is positively related to the protection of IPRs (as can be understood from the four abovementioned aspects of sustainable urbanization), that the spatial spillover effects of sustainable urbanization on the protection of IPRs and distance from the central city are significant, and that foreign direct investment (FDI)—a significant driving force in China's urbanization—has a negative impact.

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Time series models for realized covariance matrices based on the matrix-F distribution

Cited by 4 publications

References 43 publications

CNN-based Realized Covariance Matrix Forecasting

CNN-based Realized Covariance Matrix Forecasting

Quasi‐maximum likelihood estimation of conditional autoregressive Wishart models

The Linkage between Sustainable Development Goals 9 and 11: Examining the Association between Sustainable Urbanization and Intellectual Property Rights Protection

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