政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/86333

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/86333

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/86333

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/86333

Title:	波動度預測模型之探討 The research on forecast models of volatility
Authors:	吳佳貞 Wu, Chia-Chen
Contributors:	陳松男 Chen, Son-Nan 吳佳貞 Wu, Chia-Chen
Keywords:	波動度預測模型金融資產 Volatility Forecast models Asset pricing
Date:	1997
Issue Date:	2016-04-27 11:17:34 (UTC+8)
Abstract:	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.
Description:	碩士國立政治大學金融研究所 85352009
Source URI:	http://thesis.lib.nccu.edu.tw/record/#B2002002068
Data Type:	thesis
Appears in Collections:	[Department of Money and Banking] Theses

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