We present a new approach to generalised autoregressive conditional heteroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nan-cial time series, for example, asymmet...

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. Th...

The paper aims to show methodology of parameter estimation of the stable GARCH(1,1) model. There are represented and compared 3 methods of finding estimates of their parameters. We assume that we have a stable GARCH(1,1) model with the stable symmetric innovation. We search for the estimates of parameters of the stable GARCH model under assumption that we don’t know anything about parameters, t...

This paper proposes volatility and spectral based methods for cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed pri...

It is well known in the literature that the joint parameter estimation of the Smooth Autoregressive – Generalized Autoregressive Conditional Heteroskedasticity (STAR-GARCH) models poses many numerical challenges with unknown causes. This paper aims to uncover the root of the numerical difficulties in obtaining stable parameter estimates for a class of three-regime STAR-GARCH models using Quasi-...

Yingfu Xie. Maximum Likelihood Estimation and Forecasting for GARCH, Markov Switching, and Locally Stationary Wavelet Processes. Doctoral Thesis. ISSN 1652-6880, ISBN 978-91-85913-06-0. Financial time series are frequently met both in daily life and the scientific world. It is clearly of importance to study the financial time series, to understand the mechanism giving rise to the data, and/or p...

Recently, there has been a growing interest in the methods addressing volatility in computational finance and econometrics. Peiris et al. [8] have introduced doubly stochastic volatility models with GARCH innovations. Random coefficient autoregressive sequences are special case of doubly stochastic time series. In this paper, we consider some doubly stochastic stationary time series with GARCH ...

این پژوهش با الهام گرفتن از نتایج یک طرح مطالعاتی کاربردی، رویکرد سلسله­‌مراتبی جهت پیگیری فرایند توسعه تأمین‌کنندگان و حمایت تصمیمات موجود در هر مراحل آن ارائه‌ می‌کند. ابتدا، زمینه‌های تأمین نیازمند سپس واجد شرایط هریک زمینه‌ها به کمک تصمیم‌گیری چندشاخصه بهترین-بدترین مشخص می‌گردند. معیارهای شناسایی نیز مرور مطالعات پیشین بهره‌گیری نظرات خبرگان حوزه‌ی خرید استخراج‌شده‌اند. درنهایت، مدل ریاضی ...