کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1154984 958426 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Nonparametric estimation of volatility models with serially dependent innovations
ترجمه فارسی عنوان
برآورد غیر پارامتری مدل های نوسان با نوآوری های وابسته به سریال
کلمات کلیدی
مدل های نوسان فرم ضعیف، تخمین غیر پارامتری / نیمه پارامتریک، همبستگی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
چکیده انگلیسی

We are interested in modelling the time series process yt=σ(xt)εtyt=σ(xt)εt, where εt=φ0εt-1+vtεt=φ0εt-1+vt. This model is of interest as it provides a plausible linkage between risk and expected return of financial assets. Further, the model can serve as a vehicle for testing the martingale difference sequence hypothesis, which is typically uncritically adopted in financial time series models. When xtxt has a fixed design, we provide a novel nonparametric estimator of the variance function based on the difference approach and establish its limiting properties. When xtxt is strictly stationary on a strongly mixing base (hereby allowing for ARCH effects) the nonparametric variance function estimator by Fan and Yao [1998. Efficient estimation of conditional variance functions in stochastic regression. Biometrika 85, 645–660] can be applied and seems very promising. We propose a semiparametric estimator of φ0φ0 that is T-consistent, adaptive, and asymptotic normally distributed under very general conditions on xtxt.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Statistics & Probability Letters - Volume 76, Issue 18, 1 December 2006, Pages 2007–2016
نویسندگان
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