مطالب مرتبط با کلیدواژه

GARCH models


۱.

Inflation and Inflation Uncertainty in Iran: An Application of GARCH-in-Mean Model with FIML Method of Estimation(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Business and Development Studies Vol. ۲, No. ۱, (۲۰۱۰)

کلیدواژه‌ها: Iran Inflation Uncertainty GARCH models FIML

حوزه‌های تخصصی:
تعداد بازدید : ۱۳۰۱ تعداد دانلود : ۸۱۸
This paper investigates the relationship between inflation and inflation uncertainty for the period of 1990-2009 by using monthly data in the Iranian economy. The results of a two-step procedure such as Granger causality test which uses generated variables from the first stage as regressors in the second stage, suggests a positive relation between the mean and the variance of inflation. However, Pagan (1984) criticizes this two-step procedure for its misspecifications due to the use of generated variables from the first stage as regressors in the second stage. This paper uses the Full Information Maximum Likelihood (FIML) method to address this issue. The estimates we gathered with the new set of specifications suggest that inflation causes inflation uncertainty, supporting the Friedman–Ball hypothesis.
۲.

Evaluating and Forecasting Conventional Gasoline Price Fluctuations Using Garch Models with Two Distributions and Machine Learning Methods

نویسنده:
منبع: Transactions on Quantitative Finance and Beyond, Vol. ۲, No. ۱ (۲۰۲۵) 30-42

کلیدواژه‌ها: GARCH models Machine Learning Gasoline price volatility Distribution

حوزه‌های تخصصی:
تعداد بازدید : ۱۰ تعداد دانلود : ۸
Conventional gasoline price can affect the government and society as a strategic commodity in the community. Conventional gasoline price fluctuations have economic, political, social, cultural, and environmental effects. Thus, the prediction of its volatility is essential but there is not any study to examine the price fluctuations. This study aims to hybridize and propose different Garch models based on two distributions and various algorithms in machine learning, such as random forest, ridge regression, Support Vector Regression (SVR), and elastic-net for predicting weekly gasoline price volatility. The results depict Garch and GJRgarch models based on t-student distribution can predict volatility. The combination of ridge regression and GJRgarch model can better predict volatility for the seven-step-ahead. The RMSE scale has been used to compare results that the scale value is 0.01475 in the hybrid method. In fact, combining the ridge regression with t-student-GJRgarch model has the slightest error prediction or the most accuracy among different Garch models and machine learning algorithms