مشخصات مقاله | |
عنوان مقاله | A simple model for now-casting volatility series |
ترجمه عنوان مقاله | یک مدل ساده برای سری نوسانات پیش بینی حال حاضر |
فرمت مقاله | |
نوع مقاله | ISI |
نوع نگارش مقاله | مقاله پژوهشی (Research article) |
سال انتشار | |
تعداد صفحات مقاله | 9 صفحه |
رشته های مرتبط | آمار |
مجله | مجله بین المللی پیش بینی – International Journal of Forecasting |
دانشگاه | دانشگاه کلن، آلمان |
کلمات کلیدی | EGARCH ،نوسانات تصادفی، ARMA ، نوسانات تحقق یافته، قدرت نفوذ |
کد محصول | E4013 |
نشریه | نشریه الزویر |
لینک مقاله در سایت مرجع | لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
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1. Introduction
The literature on volatility models continues to grow steadily, driven mainly by the success of these models at modelling financial time series, but also by the fact that we do not yet understand some of their properties and estimators fully. The main benchmark remains the classical GARCH model that was introduced by Bollerslev (1986) and Engle (1982), due to its simplicity in estimation and widespread availability in software packages. The GARCH model is essentially a model for predicting the volatility for today, given past observations. It does this quite well, as was demonstrated by Andersen and Bollerslev (1998) using a realized volatility target instead of the commonlyused daily squared returns. However, the GARCH model does not offer the possibility of updating a prediction using today’s observed data. In other words, nowcasting volatility in the GARCH model corresponds to using the predictedvolatility, ignoring today’s observation. Following Andersen and Bollerslev (1998), we consider a continuous time process where the instantaneous returns are generated by the martingale dp(t) = σ (t) · dWp(t), (1) where Wp(t) is a Wiener process with E[Wp(t) − Wp(t − 1)] 2 = 1. In discrete time with t = 1, 2, . . . , T , the variance is σ 2 t ≡ E[p(t) − p(t − 1)] 2 = t t−1 σ (s) 2 ds. (2) For concreteness, let us consider the diffusion limit of the GARCH(1,1) process given by dσ (t) = a1[a2 − σ (t) 2 ] · dt + 2a3a1 σ (t) · dWσ (t), (3) where a1, a2, a3 are positive parameters and the standard Wiener process Wσ (t) is independent of Wp(t) (see also Andersen & Bollerslev, 1998). |