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  1. ディスカッション・ペーパー
  2. 2024年度

Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility

https://doi.org/10.24545/0002000095
https://doi.org/10.24545/0002000095
29726960-aa63-49d2-aabc-e0384dc38aee
名前 / ファイル ライセンス アクション
DP24-2.pdf DP24-2.pdf
Item type ディスカッションペーパー / Discussion Paper(1)
公開日 2024-04-15
タイトル
タイトル Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility
言語 en
言語
言語 eng
資源タイプ
資源タイプ technical report
ID登録
ID登録 (DOI) 10.24545/0002000095
ID登録タイプ JaLC
著者 LEON-GONZALEZ, Roberto

× LEON-GONZALEZ, Roberto

en LEON-GONZALEZ, Roberto

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MAJONI, Blessings

× MAJONI, Blessings

en MAJONI, Blessings

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著者所属
値 政策研究大学院大学 / National Graduate Institute for Policy Studies
著者所属
値 政策研究大学院大学 / National Graduate Institute for Policy Studies
抄録
内容記述タイプ Abstract
内容記述 Common factor stochastic volatility (CSV) models capture the commonality that is often observed in volatility patterns. However, they assume that all the time variation in volatility is driven by a single multiplicative factor. This paper has two contributions. Firstly we develop a novel CSV model in which the volatility follows an inverse gamma process (CSV-IG), which implies fat Student’s t tails for the observed data. We obtain an analytic expression for the likelihood of this CSV model, which facilitates the numerical calculation of the marginal and predictive likelihood for model comparison. We also show that it is possible to simulate exactly from the posterior distribution of the volatilities using mixtures of gammas. Secondly, we generalize this CSV-IG model by parsimoniously substituting conditionally homoscedastic shocks with heteroscedastic factors which interact multiplicatively with the common factor in an approximate factor model (CSV-IG-AF). In empirical applications we compare these models to other multivariate stochastic volatility models, including different types of CSV models and exact factor stochastic volatility (FSV) models. The models are estimated using daily exchange rate returns of 8 currencies. A second application estimates the models using 20 macroeconomic variables for each of four countries: US, UK, Japan and Brazil. The comparison method is based on the predictive likelihood. In the application to exchange rate data we find strong evidence of CSV and that the best model is the IG-CSV-AF. In the Macro application we find that 1) the CSV-IG model performs better than all other CSV models, 2) the CSV-IG-AF is the best model for the US, 3) the CSV-IG is the best model for Brazil and 4) exact factor SV models are the best for UK and JP.
言語 en
内容記述
内容記述タイプ Other
内容記述 We gratefully acknowledge financial support from JSPS (category C, 19K01588) and from GRIPS Policy Research Center (grant G241RP208).
発行年
値 2024-04
書誌情報 en : GRIPS Discussion Papers

Report No. 24-2, 発行日 2024-04-15
出版者
出版者 GRIPS Policy Research Center
言語 en
著者情報
内容記述タイプ Other
内容記述 https://www.grips.ac.jp/list/facultyinfo/leon_gonzalez_roberto/
著者版フラグ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
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