scholarly journals Randomised Pseudolikelihood Ratio Change Point Estimator in Garch Models

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
George Awiakye-Marfo ◽  
Joseph Mung’atu ◽  
Patrick O. Weke
Keyword(s):  
Change Point ◽  
Garch Model ◽  
Garch Models ◽  
Variance Change ◽  
Point Estimator ◽  
Ratio Change ◽  
Simulation Results ◽  
The Garch Model

In this paper, a randomised pseudolikelihood ratio change point estimator for GARCH model is presented. Derivation of a randomised change point estimator for the GARCH model and its consistency are given. Simulation results that support the validity of the estimator are also presented. It was observed that the randomised estimator outperforms the ordinary CUSUM of squares test, and it is optimal with large variance change ratios.

scholarly journals Convergence of a Randomised Change Point Estimator in GARCH Models

2021 ◽  
Vol 11 (02) ◽  
pp. 234-245
Author(s):  
George Awiakye-Marfo ◽  
Joseph Mung’atu ◽  
Patrick Weke
Keyword(s):  
Change Point ◽  
Garch Models ◽  
Point Estimator

scholarly journals Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models

2017 ◽  
Vol 15 (1) ◽  
pp. 1539-1548
Author(s):  
Haiyan Xuan ◽  
Lixin Song ◽  
Muhammad Amin ◽  
Yongxia Shi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Garch Model ◽  
Garch Models ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Comparison Results ◽  
Quasi Maximum Likelihood ◽  
The Garch Model

Abstract This paper studies the quasi-maximum likelihood estimator (QMLE) for the generalized autoregressive conditional heteroscedastic (GARCH) model based on the Laplace (1,1) residuals. The QMLE is proposed to the parameter vector of the GARCH model with the Laplace (1,1) firstly. Under some certain conditions, the strong consistency and asymptotic normality of QMLE are then established. In what follows, a real example with Laplace and normal distribution is analyzed to evaluate the performance of the QMLE and some comparison results on the performance are given. In the end the proofs of some theorem are presented.

scholarly journals PERBANDINGAN MODEL GARCH SIMETRIS DAN ASIMETRIS PADA DATA KURS HARIAN

2020 ◽  
Vol 4 (4) ◽  
pp. 627-637
Author(s):  
Isna Shofia Mubarokah ◽  
Anwar Fitrianto ◽  
Farit M Affendi
Keyword(s):  
Exchange Rates ◽  
Garch Model ◽  
Financial Data ◽  
Garch Models ◽  
Data Series ◽  
Asymmetric Effect ◽  
Daily Return ◽  
Volatility Model ◽  
Asymmetric Garch ◽  
The Garch Model

ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.

scholarly journals Limit Theory of Model Order Change-Point Estimator for GARCH Models

2018 ◽  
Vol 08 (02) ◽  
pp. 426-445
Author(s):  
Irene W. Irungu ◽  
Peter N. Mwita ◽  
Antony G. Waititu
Keyword(s):  
Change Point ◽  
Garch Models ◽  
Model Order ◽  
Limit Theory ◽  
Point Estimator ◽  
Order Change

scholarly journals Consistency of the Model Order Change-Point Estimator for GARCH Models

2018 ◽  
Vol 08 (02) ◽  
pp. 266-282
Author(s):  
Irene W. Irungu ◽  
Peter N. Mwita ◽  
Antony G. Waititu
Keyword(s):  
Change Point ◽  
Garch Models ◽  
Model Order ◽  
Point Estimator ◽  
Order Change

scholarly journals Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

2021 ◽  
Vol 14 (7) ◽  
pp. 314
Author(s):  
Najam Iqbal ◽  
Muhammad Saqib Manzoor ◽  
Muhammad Ishaq Bhatti
Keyword(s):  
Stock Returns ◽  
Garch Model ◽  
Bad News ◽  
Good News ◽  
Fiscal Policies ◽  
News Shocks ◽  
Australian Health ◽  
Egarch Model ◽  
Negative Effect ◽  
The Garch Model

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

scholarly journals Ramadan Effect and Volatility Risk by Garch Model: Evidence in Indonesia Stock Market

2021 ◽  
pp. 73-82
Author(s):  
Dery Westryananda Putra ◽  
Sri Hasnawati ◽  
Muslimin Muslimin
Keyword(s):  
Stock Market ◽  
Sampling Method ◽  
Stock Exchange ◽  
Garch Model ◽  
Analytical Tool ◽  
Volatility Risk ◽  
Purposive Sampling ◽  
Model Evidence ◽  
The Garch Model

This study aims to analyze the effect of the Ramadan effect and volatility risk on the Indonesian stock market using the GARCH model. The population in this study are companies listed on the LQ45 index on the Indonesia Stock Exchange during 2019. There are 42 companies used as samples in this study. The research sample was taken using purposive sampling method. This study uses the GARCH model as an analytical tool. The results of this study indicate that there is no Ramadan effect on the LQ45 index, but the volatility in the month of Ramadan affects the volatility in the LQ45 index. Keywords: Ramadan Effect, Volatility Risk, GARCH Model Abstrak Penelitian ini bertujuan untuk menganalisis pengaruh Ramadhan effect dan risiko volatilitas terhadap pasar saham Indonesia dengan menggunakan model GARCH. Populasi dalam penelitian ini adalah perusahaan yang terdaftar pada indeks LQ45 di Bursa Efek Indonesia selama tahun 2019. Terdapat 42 perusahaan yang dijadikan sampel dalam penelitian ini. Sampel penelitian diambil dengan menggunakan metode purposive sampling. Penelitian ini menggunakan model GARCH sebagai alat analisis. Hasil penelitian ini menunjukkan bahwa tidak ada pengaruh Ramadhan terhadap indeks LQ45, namun volatilitas pada bulan Ramadhan berpengaruh terhadap volatilitas pada indeks LQ45. Kata Kunci: Ramadhan Effect, Risiko Volatilitas, Model GARCH

scholarly journals Change Point Estimation in Panel Data without Boundary Issue

2016 ◽  
Author(s):  
Barbora Peštová ◽  
Michal Pešta
Keyword(s):  
Panel Data ◽  
Change Point ◽  
Structural Breaks ◽  
Break Point ◽  
Point Estimation ◽  
The Novel ◽  
Structure Estimation ◽  
Change Point Estimation ◽  
Point Estimator ◽  
First Time

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

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