學術產出-學位論文
| 題名 | 波動度預測模型之探討 The research on forecast models of volatility |
| 作者 | 吳佳貞 Wu, Chia-Chen |
| 貢獻者 | 陳松男 Chen, Son-Nan 吳佳貞 Wu, Chia-Chen |
| 關鍵詞 | 波動度 預測模型 金融資產 Volatility Forecast models Asset pricing |
| 日期 | 1997 |
| 上傳時間 | 27-四月-2016 11:17:34 (UTC+8) |
| 摘要 | 期望波動度在投資組合的選擇、避險策略、資產管理，以及金融資產的評價上是關鍵性因素，因此，在波動度變化甚巨的金融市場中，找出具有良好預測波動度能力的模型，是絕對必要的。過去從事資產價格行為的相關研究都假設資產的價格過程是隨機的，且呈對數常態分配、變異數固定。然而實證結果一再顯示：變異數是隨時間而變動的（如 Mandelbrot(1963)、 Fama(1965)）。為預測波動度（或變異數），Eagle(1982)首先提出了 ARCH 模型，允許預期條件變異數作為過去殘差的函數，因此變異數能隨時間而改變。此後 Bollerslve(1986)提出 GARCH 模型，修正ARCH 模型線性遞減遞延結構，將過去的殘差及變異數同時納入條件變異數方程式中。 Nelson(1991)則提出 EGARCH 模型以改進 GARCH 模型的三大缺點，此模型對具有高度波動性的金融資產提供更成功的另一估計模式。除上列之 ARCH-type 模型外，Hull and White(1987)提出連續型隨機波動模型(continuous time stochastic volatility model)，用以評價股價選擇權，此模型不僅將過去的變異數納入條件變異數的方程式中，同時該條件變異數也會因隨機噪音(random noise)而變動。近年來，上述模型均被廣泛運用在模擬金融資產的波動性，均是相當實用的模型。 Volatility forecast is extremely important factor in portfolio chice, hedging strategies, asset management, asset pricing and option pricing. Identifying a good forecast model of volatility is absolutely necessary, especially for the highly volatile Taiwan stock marek. Due to increasing attention to the impact of marke risk on asset returns, academic researchers and practicians have developed ways to control risk and methodologies to forecast return volatility. Past researches on asset price behavior usually assumed that asset price behavior follows random walk, and its probability distribution is a log-normal distribution with a constant variance (or constant volatility). This assumption is in fact in violation of empirical evidence showing that volatility tends to vary over time (e.g., Mandelbrot﹝1963﹞ and Fama﹝1965﹞). To forecast volatility (or variance), Engle(1982) is the first scholar to propose a forecast model, now well-known as ARCH, whose conditional variance is a funtion of past squared returns residuals. Accordingly, the forecast variance(or volatility) varies over time. Bollerslev(1986) proposed a generalized model, called GARCH, which allows the current conditional variance depends not only on past squared residuals, but also on past conditional variances. However, Nelson(1991) has recently proposed a new model, called EGARCH, which attempts to remove the weakness of the GARCH model. The EGARCH model has been shown to be successful to forecast volatility and to describe successful stock price behavior. In addition, Hull and white(1987) employed a continuous-time stochastic volatility model to develop in option pricing model. Their stochastic volatility model not only admits the past variance, but also depends on random noise of volatility. The above-mentioned models have been widely implemented in practice to simulate and to forecast asset return volatility. |
| 描述 | 碩士 國立政治大學 金融研究所 85352009 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#B2002002068 |
| 資料類型 | thesis |
| dc.contributor.advisor | 陳松男 | zh_TW |
| dc.contributor.advisor | Chen, Son-Nan | en_US |
| dc.contributor.author (作者) | 吳佳貞 | zh_TW |
| dc.contributor.author (作者) | Wu, Chia-Chen | en_US |
| dc.creator (作者) | 吳佳貞 | zh_TW |
| dc.creator (作者) | Wu, Chia-Chen | en_US |
| dc.date (日期) | 1997 | en_US |
| dc.date.accessioned | 27-四月-2016 11:17:34 (UTC+8) | - |
| dc.date.available | 27-四月-2016 11:17:34 (UTC+8) | - |
| dc.date.issued (上傳時間) | 27-四月-2016 11:17:34 (UTC+8) | - |
| dc.identifier (其他 識別碼) | B2002002068 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/86333 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融研究所 | zh_TW |
| dc.description (描述) | 85352009 | zh_TW |
| dc.description.abstract (摘要) | 期望波動度在投資組合的選擇、避險策略、資產管理，以及金融資產的評價上是關鍵性因素，因此，在波動度變化甚巨的金融市場中，找出具有良好預測波動度能力的模型，是絕對必要的。過去從事資產價格行為的相關研究都假設資產的價格過程是隨機的，且呈對數常態分配、變異數固定。然而實證結果一再顯示：變異數是隨時間而變動的（如 Mandelbrot(1963)、 Fama(1965)）。為預測波動度（或變異數），Eagle(1982)首先提出了 ARCH 模型，允許預期條件變異數作為過去殘差的函數，因此變異數能隨時間而改變。此後 Bollerslve(1986)提出 GARCH 模型，修正ARCH 模型線性遞減遞延結構，將過去的殘差及變異數同時納入條件變異數方程式中。 Nelson(1991)則提出 EGARCH 模型以改進 GARCH 模型的三大缺點，此模型對具有高度波動性的金融資產提供更成功的另一估計模式。除上列之 ARCH-type 模型外，Hull and White(1987)提出連續型隨機波動模型(continuous time stochastic volatility model)，用以評價股價選擇權，此模型不僅將過去的變異數納入條件變異數的方程式中，同時該條件變異數也會因隨機噪音(random noise)而變動。近年來，上述模型均被廣泛運用在模擬金融資產的波動性，均是相當實用的模型。 | zh_TW |
| dc.description.abstract (摘要) | Volatility forecast is extremely important factor in portfolio chice, hedging strategies, asset management, asset pricing and option pricing. Identifying a good forecast model of volatility is absolutely necessary, especially for the highly volatile Taiwan stock marek. Due to increasing attention to the impact of marke risk on asset returns, academic researchers and practicians have developed ways to control risk and methodologies to forecast return volatility. Past researches on asset price behavior usually assumed that asset price behavior follows random walk, and its probability distribution is a log-normal distribution with a constant variance (or constant volatility). This assumption is in fact in violation of empirical evidence showing that volatility tends to vary over time (e.g., Mandelbrot﹝1963﹞ and Fama﹝1965﹞). To forecast volatility (or variance), Engle(1982) is the first scholar to propose a forecast model, now well-known as ARCH, whose conditional variance is a funtion of past squared returns residuals. Accordingly, the forecast variance(or volatility) varies over time. Bollerslev(1986) proposed a generalized model, called GARCH, which allows the current conditional variance depends not only on past squared residuals, but also on past conditional variances. However, Nelson(1991) has recently proposed a new model, called EGARCH, which attempts to remove the weakness of the GARCH model. The EGARCH model has been shown to be successful to forecast volatility and to describe successful stock price behavior. In addition, Hull and white(1987) employed a continuous-time stochastic volatility model to develop in option pricing model. Their stochastic volatility model not only admits the past variance, but also depends on random noise of volatility. The above-mentioned models have been widely implemented in practice to simulate and to forecast asset return volatility. | en_US |
| dc.description.tableofcontents | 目錄 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 本文貢獻 3 第四節 研究限制 4 第五節 研究流程 4 第二章 文獻回顧 6 第三章 研究方法 10 第一節 隨機漫步模型 10 第二節 GARCH(1,1)模型 12 第三節 EGARCH(1,1)模型 16 第四節 隨機波動模型 19 第四章 實證結果 22 第一節 資料描述與處理 22 第二節 進行步驟 29 第三節 模型估計 29 第四節 適切度的衡量 32 第五節 各模型預測能力比較 34 第五章 結論與建議 42 第一節 結論 42 第二節 建議 43 參考文獻 45 附錄A GARCH(1,1)期間波動度預測公式之推導 50 附錄B EGARCH(1,1)期間波動度預測公式之推導 52 表次 表4-1 資料描述 22 表4-2 股價指數之報酬率特性 26 表4-3 以股價指數報酬率日資料估計各模型參數表 30 表4-4 以外匯報酬率日資料估計各模型參數表 31 表4-5 各模型的非條件標準差 32 表4-6 各模型的SBC 33 表4-7 在不同預測期間下，各模型對各國股價指數波動度預測的中位預測誤差平方根 36 表4-8 在不同預測期間下，各模型對各國匯率波動度預測的中位預測誤差平方根 37 表4-9 各模型在不同資產的預測能力排名 40 表4-10 各模型在長短期的預測能力排名 40 表4-11 各模型在各國金融市場的預測能力排名 40 圖次 圖1-1 研究流程圖 5 圖3-1 1988.1.1~1994.12.31 台股指數日報酬率分配圖 14 圖3-2 1988.1.1~1994.12.31 台股指數日報酬率變動情形 14 圖3-3 以GARCH(1,1)模擬1988至1994年台股指數日波動度 15 圖3-4 新訊息對波動度的的不對稱影響 17 圖3-5 以EGARCH(1,1)模擬1988至1994年台股指數日波動度 18 圖3-6 以隨機波動模型模擬1988至1994年台股指數日波動度 20 圖4-1 1988.1.1~1997.12.31 各國股價指數走勢圖 23 圖4-2 1988.1.1~1997.12.31 各國匯率走勢圖 24 圖4-3 各國股價指數1988.1.~1994.12.31 日報酬率分配圖 27 圖4-4 各國匯率1988.1.~1994.12.31 日報酬率分配圖 28 圖4-5 期間預測流圖 34 圖4-6 在不同預測期間下，各模型對各國股價指數波動度預測的RMSE比較圖 38 圖4-7 在不同預測期間下，各模型對各國匯率波動度預測預測的RMSE比較圖 39 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#B2002002068 | en_US |
| dc.subject (關鍵詞) | 波動度 | zh_TW |
| dc.subject (關鍵詞) | 預測模型 | zh_TW |
| dc.subject (關鍵詞) | 金融資產 | zh_TW |
| dc.subject (關鍵詞) | Volatility | en_US |
| dc.subject (關鍵詞) | Forecast models | en_US |
| dc.subject (關鍵詞) | Asset pricing | en_US |
| dc.title (題名) | 波動度預測模型之探討 | zh_TW |
| dc.title (題名) | The research on forecast models of volatility | en_US |
| dc.type (資料類型) | thesis | en_US |