A GMM procedure for combining volatility forecasts

نویسندگان

  • Alessandra Amendola
  • Giuseppe Storti
چکیده

A novel approach to the combination of volatility forecasts is discussed. The proposed procedure makes use of the generalized method of moments (GMM) for estimating the combination weights. The asymptotic properties of the GMM estimator are derived while its finite sample properties are assessed by means of a simulation study. The results of an application to a time series of daily returns on the S&P500 are presented. © 2007 Elsevier B.V. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Forecasting Crude Oil prices Volatility and Value at Risk: Single and Switching Regime GARCH Models

Forecasting crude oil price volatility is an important issues in risk management. The historical course of oil price volatility indicates the existence of a cluster pattern. Therefore, GARCH models are used to model and more accurately predict oil price fluctuations. The purpose of this study is to identify the best GARCH model with the best performance in different time horizons. To achieve th...

متن کامل

Comparing the performance of GARCH (p,q) models with different methods of estimation for forecasting crude oil market volatility

The use of GARCH models to characterize crude oil price volatility is widely observed in the empirical literature. In this paper the efficiency of six univariate GARCH models and two methods of estimation the parameters for forecasting oil price volatility are examined and the best method for forecasting crude oil price volatility of Brent market is determined. All the examined models in this p...

متن کامل

The Multi-Fractal Model of Asset Returns: Its Estimation via GMM and Its Use for Volatility Forecasting

Multi-fractal processes have been proposed as a new formalism for modeling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns a feature that has been found to characterize virtually all financial prices. Furthermore, elementary variants of multi-fractal models are very parsimon...

متن کامل

Forecasting volatility under fractality, regime-switching, long memory and student-t innovations

We examine the performance of volatility models that incorporate features such as long (short) memory, regime-switching and multifractality along with two competing distributional assumptions of the error component, i.e. Normal vs Student-t. Our precise contribution is twofold. First, we introduce a new model to the family of Markov-Switching Multifractal models of asset returns (MSM), namely, ...

متن کامل

Improving GARCH Volatility Forecasts with Regime-Switching GARCH

Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Computational Statistics & Data Analysis

دوره 52  شماره 

صفحات  -

تاریخ انتشار 2008