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On financial markets, many \u003cbr /\u003efinancial time series exhibit changes of volatility (variance) over time. Moreover, many \u003cbr /\u003efinancial time series are well known to have non-Gaussian heavy-tailed distributions. \u003cbr /\u003eThese facts indicate that a nonlinear non-Gaussian time series analysis is needed. \u003cbr /\u003eRegarding the economy, as one example, the Japanese economy has the experience of \u003cbr /\u003ethe \"bubble economy\" in the late 1980s. After bursting of the \"bubble economy\", the \u003cbr /\u003eeconomy entered a decade o,f economic stagnation, which is often called \"the lost \u003cbr /\u003edecade\". These facts indicate that conventional linear regression based on ordinary \u003cbr /\u003eleast squares might be ineffective to analyze a non-stationary economy because the \u003cbr /\u003ecoefficients of linear regression are fixed. This paper shows several statistical \u003cbr /\u003eapproaches based on nonlinear non-Gaussian state space modeling and time-varying \u003cbr /\u003ecoefficient autoregressive modeling. These approaches are novel studies of financial \u003cbr /\u003emarkets and the economy. \u003cbr /\u003e In chapter 1, the Monte Carlo filter is introduced. It is a minimal introduction to \u003cbr /\u003enonlinear non-Gaussian state-space modeling. \u003cbr /\u003e In chapter 2, we propose a method to seek initial distributions of parameters for a \u003cbr /\u003eself-organizing state space model proposed by Kitagawa]. Our method is based on the \u003cbr /\u003esimplex Nelder-Mead algorithm for solving nonlinear and discontinuous optimization \u003cbr /\u003eproblems. We show the effectiveness of our method by applying it to a linear Gaussian \u003cbr /\u003emodel, a linear non-Gaussian Model, a nonlinear Gaussian model, and a stochastic \u003cbr /\u003evolatility model. \u003cbr /\u003e In chapter 3, we propose a smoothing algorithm based on the Monte Carlo filter and \u003cbr /\u003ethe inverse function of a system equation (an inverse system function). Our method is \u003cbr /\u003eapplicable to any nonlinear non-Gaussian state space model if an inverse system \u003cbr /\u003eequation is given analytically. Moreover, we propose a filter initialization algorithm \u003cbr /\u003ebased on a smoothing distribution obtained by our smoothing algorithm and an \u003cbr /\u003einverse system equation. \u003cbr /\u003e In chapter 4, we illustrate the effectiveness of our approach by applying it to \u003cbr /\u003estochastic volatility models and stochastic volatility models with heavy-tailed \u003cbr /\u003edistributions for the daily return of the Yen/Dollar exchange rate. \u003cbr /\u003e In chapter 5, we propose a method that estimates a time-varying linear system \u003cbr /\u003eequation based on time-varying coefficients\u0027 vector autoregressive modeling \u003cbr /\u003e(time-varying VAR), and which controls the system. In our framework, an optimal \u003cbr /\u003efeedback is determined using linear quadratic dynamic programming in each period.\u003cbr /\u003eThe coeffients of time-varying VAR are assumed to change gradually (this \u003cbr /\u003eassumption is widely known as smoothness priors of the Bayesian procedure). The \u003cbr /\u003ecoefficients are estimated using the Kalman filter. In our empirical analyses, we show \u003cbr /\u003ethe effectiveness of our approach by applying it to monetary policy, in particular, the \u003cbr /\u003einflation targeting of the United Kingdom and the nominal growth rate targeting of \u003cbr /\u003eJapan. Furthermore, we emphasize that monetary policy must be forecast-based \u003cbr /\u003ebecause transmission lags pertain from monetary policy to the economy. Our approach \u003cbr /\u003eis convenient and effective for central bank practitioners when they are unaware of \u003cbr /\u003ethe true model of the economy. Additionally, we find that the coefficients of \u003cbr /\u003etime-varying VAR change in response to changes of monetary policy. \u003cbr /\u003e In chapter 6, we estimate the β of a single factor model that is ofben used by \u003cbr /\u003efinancial practitioners. In this chapter, we assume that β changes \"gradually\" over \u003cbr /\u003etime; this assumption is identical to that in chapter 5. Using our approach, we can \u003cbr /\u003eestimate β, even if it is time varying. We apply our approach to the Japanese Stock \u003cbr /\u003eMarkets and show its effectiveness. Although we adopt a very restrictive method (we \u003cbr /\u003eassume smoothness priors and use the Kalman fiker, which is based on linear state \u003cbr /\u003espace modeling and the Gaussian distribution), we can obtain good estimates of β.", "subitem_description_type": "Other"}]}, "item_1_description_7": {"attribute_name": "学位記番号", "attribute_value_mlt": [{"subitem_description": "総研大甲第1043号", "subitem_description_type": "Other"}]}, "item_1_select_14": {"attribute_name": "所蔵", "attribute_value_mlt": [{"subitem_select_item": "有"}]}, "item_1_select_16": {"attribute_name": "複写", "attribute_value_mlt": [{"subitem_select_item": "application/pdf"}]}, "item_1_select_8": {"attribute_name": "研究科", "attribute_value_mlt": [{"subitem_select_item": "複合科学研究科"}]}, "item_1_select_9": {"attribute_name": "専攻", "attribute_value_mlt": [{"subitem_select_item": "15 統計科学専攻"}]}, "item_1_text_10": {"attribute_name": "学位授与年度", "attribute_value_mlt": [{"subitem_text_value": "2006"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "YANO, Koiti", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "0", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-17"}], "displaytype": "simple", "download_preview_message": "", "file_order": 0, "filename": "甲1043_要旨.pdf", "filesize": [{"value": "245.4 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 245400.0, "url": {"label": "要旨・審査要旨", "url": "https://ir.soken.ac.jp/record/781/files/甲1043_要旨.pdf"}, "version_id": "c581c9d6-1b60-4b10-b278-70297b72d204"}, {"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-17"}], "displaytype": "simple", "download_preview_message": "", "file_order": 1, "filename": "甲1043_本文.pdf", "filesize": [{"value": "1.4 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 1400000.0, "url": {"label": "本文", "url": "https://ir.soken.ac.jp/record/781/files/甲1043_本文.pdf"}, "version_id": "55f0e445-e9d9-4298-a01f-8e5a29b8695f"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series"}, {"subitem_title": "Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series", "subitem_title_language": "en"}]}, "item_type_id": "1", "owner": "1", "path": ["17"], "permalink_uri": "https://ir.soken.ac.jp/records/781", "pubdate": {"attribute_name": "公開日", "attribute_value": "2010-02-22"}, "publish_date": "2010-02-22", "publish_status": "0", "recid": "781", "relation": {}, "relation_version_is_last": true, "title": ["Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series"], "weko_shared_id": 1}
Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series
https://ir.soken.ac.jp/records/781
https://ir.soken.ac.jp/records/781defecea1-764f-43bb-bf48-d0f47f0b46da
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
要旨・審査要旨 (245.4 kB)
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本文 (1.4 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2010-02-22 | |||||
| タイトル | ||||||
| タイトル | Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series | |||||
| タイトル | ||||||
| 言語 | en | |||||
| タイトル | Nonlinear, Non-Gaussian, and Non-stationary State Space Models and Applications to Economic and Financial Time Series | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||
| 資源タイプ | thesis | |||||
| 著者名 |
矢野, 浩一
× 矢野, 浩一 |
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| フリガナ |
ヤノ, コウイチ
× ヤノ, コウイチ |
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| 著者 |
YANO, Koiti
× YANO, Koiti |
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| 学位授与機関 | ||||||
| 学位授与機関名 | 総合研究大学院大学 | |||||
| 学位名 | ||||||
| 学位名 | 博士（統計科学） | |||||
| 学位記番号 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 総研大甲第1043号 | |||||
| 研究科 | ||||||
| 値 | 複合科学研究科 | |||||
| 専攻 | ||||||
| 値 | 15 統計科学専攻 | |||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2007-03-23 | |||||
| 学位授与年度 | ||||||
| 2006 | ||||||
| 要旨 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Financial markets and the economy are changing rapidly. On financial markets, many <br />financial time series exhibit changes of volatility (variance) over time. Moreover, many <br />financial time series are well known to have non-Gaussian heavy-tailed distributions. <br />These facts indicate that a nonlinear non-Gaussian time series analysis is needed. <br />Regarding the economy, as one example, the Japanese economy has the experience of <br />the "bubble economy" in the late 1980s. After bursting of the "bubble economy", the <br />economy entered a decade o,f economic stagnation, which is often called "the lost <br />decade". These facts indicate that conventional linear regression based on ordinary <br />least squares might be ineffective to analyze a non-stationary economy because the <br />coefficients of linear regression are fixed. This paper shows several statistical <br />approaches based on nonlinear non-Gaussian state space modeling and time-varying <br />coefficient autoregressive modeling. These approaches are novel studies of financial <br />markets and the economy. <br /> In chapter 1, the Monte Carlo filter is introduced. It is a minimal introduction to <br />nonlinear non-Gaussian state-space modeling. <br /> In chapter 2, we propose a method to seek initial distributions of parameters for a <br />self-organizing state space model proposed by Kitagawa]. Our method is based on the <br />simplex Nelder-Mead algorithm for solving nonlinear and discontinuous optimization <br />problems. We show the effectiveness of our method by applying it to a linear Gaussian <br />model, a linear non-Gaussian Model, a nonlinear Gaussian model, and a stochastic <br />volatility model. <br /> In chapter 3, we propose a smoothing algorithm based on the Monte Carlo filter and <br />the inverse function of a system equation (an inverse system function). Our method is <br />applicable to any nonlinear non-Gaussian state space model if an inverse system <br />equation is given analytically. Moreover, we propose a filter initialization algorithm <br />based on a smoothing distribution obtained by our smoothing algorithm and an <br />inverse system equation. <br /> In chapter 4, we illustrate the effectiveness of our approach by applying it to <br />stochastic volatility models and stochastic volatility models with heavy-tailed <br />distributions for the daily return of the Yen/Dollar exchange rate. <br /> In chapter 5, we propose a method that estimates a time-varying linear system <br />equation based on time-varying coefficients' vector autoregressive modeling <br />(time-varying VAR), and which controls the system. In our framework, an optimal <br />feedback is determined using linear quadratic dynamic programming in each period.<br />The coeffients of time-varying VAR are assumed to change gradually (this <br />assumption is widely known as smoothness priors of the Bayesian procedure). The <br />coefficients are estimated using the Kalman filter. In our empirical analyses, we show <br />the effectiveness of our approach by applying it to monetary policy, in particular, the <br />inflation targeting of the United Kingdom and the nominal growth rate targeting of <br />Japan. Furthermore, we emphasize that monetary policy must be forecast-based <br />because transmission lags pertain from monetary policy to the economy. Our approach <br />is convenient and effective for central bank practitioners when they are unaware of <br />the true model of the economy. Additionally, we find that the coefficients of <br />time-varying VAR change in response to changes of monetary policy. <br /> In chapter 6, we estimate the β of a single factor model that is ofben used by <br />financial practitioners. In this chapter, we assume that β changes "gradually" over <br />time; this assumption is identical to that in chapter 5. Using our approach, we can <br />estimate β, even if it is time varying. We apply our approach to the Japanese Stock <br />Markets and show its effectiveness. Although we adopt a very restrictive method (we <br />assume smoothness priors and use the Kalman fiker, which is based on linear state <br />space modeling and the Gaussian distribution), we can obtain good estimates of β. | |||||
| 所蔵 | ||||||
| 値 | 有 | |||||
