| 研究課題/領域番号 |
22K13377
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| 研究機関 | 大阪大学 |
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研究代表者 |
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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| キーワード | Copula / Factor model / High dimension / Sparsity |
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| 研究実績の概要 |
The paper "Sparse M-estimators in semi-parametric copula models", co-authored with Prof Fermanian, has been accepted for publication at Bernoulli in 2023 and is forthcoming in 2024. The paper answered the issues inherent to copula models: pseudo-observations; unbounded copula-based objective functions; explosive number of parameters. We specified a suitable penalized M-estimator framework for copulas and derived the asymptotic properties.
The paper "Sparse factor models of high dimension", co-authored with Prof Terada, is currently submitted at an econometrics journal: we devised a sparsity-based estimation framework for the factor loading matrix taking into account the rotational indeterminacy and derived the asymptotic properties.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The research is moving forward: one paper published in Bernoulli; one paper currently submitted at an econometrics journals; one research project on stochastic volatility models is about to be completed.
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| 今後の研究の推進方策 |
The project "Factor Multivariate Stochastic Volatility Models", co-authored with Prof Asai, is about to be completed and will be submitted at an econometrics journal. The key idea is to integrate factors in the Multivariate Stochastic Volatility (MSV) model. We propose to estimate the latent factors using the estimators of the factor decomposition and then specify a multivariate state space representation of the latent volatility of the factors (not the observed random vector, which can be high-dimensional). Theoretical analysis of the proposed method: asymptotic properties derived under moment conditions.
The replication package will be made publicly available for the sake of transparancy.
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