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Relevant bibliographies by topics / Generalised AutoRegressive Conditional Heteroscedastic (GARCH)

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Journal articles
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Book chapters

Academic literature on the topic 'Generalised AutoRegressive Conditional Heteroscedastic (GARCH)'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Generalised AutoRegressive Conditional Heteroscedastic (GARCH)"

1

Odah, Meshal Harbi. "Comparison of GARCH & ARMA Models to Forecasting Exchange Rate." Mathematical Modelling of Engineering Problems 8, no. 6 (2021): 979–83. http://dx.doi.org/10.18280/mmep.080619.

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Financial time series are defined by their fluctuations, which are characterized by instability or uncertainty, implying that there are periods of volatility followed by periods of relative calm. Therefore, time series analysis requires homogeneity of variance. In this paper, some models used in time series analysis have been studied and applied. Comparison between Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models to identify the efficient model through (MAE, MASE) measures to determine the best forecasting model is studied. The fi

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Novianti Dwi PujiAstuti and Suwanda. "Evaluasi Model Exponential Generelized Autoregressive Conditional Heteroscedastic (EGARCH)." Bandung Conference Series: Statistics 2, no. 2 (2022): 358–64. http://dx.doi.org/10.29313/bcss.v2i2.4365.

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Abstract. In time series data that has a fairly high volatility, it is possible to have an error variance that is not constant (Heteroscedasticity). This is reflected in the square of error that also follows the time series model, for example the autoregressive (AR) model and the expectation of the conditional error square is not constant, the AR model of the square of error is called the Autoregressive Conditional Heteroscedastic (ARCH). The AR model that combines time series data and squared error is called Generalized Autoregressive Conditional Heteroscedastic (GARCH). However, the GARCH mo

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Jhohura, Fatema Tuz, and Md Israt Rayhan. "An Assessment of Renewable Energy in Bangladesh through ARIMA, Holts, ARCH-GARCH Models." Dhaka University Journal of Science 60, no. 2 (2012): 159–62. http://dx.doi.org/10.3329/dujs.v60i2.11486.

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Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holts linear exponential smoothing, and the autoregressive conditional heteroscedastic (with their extensions to generalized autoregressive conditional heteroscedastic) models were fitted to the data. The goodness of fit is performed via the Akaike informat

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Iqbal, Teuku Achmad, Kusman Sadik, and I. Made Sumertajaya. "Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH)." Jurnal Penelitian Pertanian Tanaman Pangan 33, no. 1 (2014): 17. http://dx.doi.org/10.21082/jpptp.v33n1.2014.p17-26.

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This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric GARCH, exponential asymmetric GARCH, quadratic asymmetric GARCH, Threshold GARCH, and non-linear asymmetric GARCH. Those models were compared and evaluated, and then the best model was used to predict the accuracy of the national rice harvested area. The results showed that two types of GARCH had significant coefficient, indicating the validity

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Robinson Sihombing, Pardomuan, Oki Prasetia Hendarsin, Sarah Sholikhatun Risma, and Bekti Endar Susilowati. "The Application Of Autoregressive Integrated Moving Average Generalized Autoregressive Conditional Heteroscedastic (Arima - Garch)." Udayana Journal of Social Sciences and Humanities (UJoSSH) 4, no. 2 (2020): 63. http://dx.doi.org/10.24843/ujossh.2020.v04.i02.p04.

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Rice farming for Indonesia is vital. Rice farming is inseparable from the fact that rice farming is the livelihood of most of the population, while rice is the staple food of almost all Indonesians. The nature of rice that is easy to process and, following the public consumption culture, causes a very high dependence on rice. On the other hand, the price of rice is quite volatile. If the price of rice is soaring high, it can cause changes in the pattern of rice consumption. Some people want a stable supply and rice price, available at all times and evenly distributed and at affordable prices.

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Liko, Rozana. "Modeling the Behavior of Inflation Rate in Albania Using Time Series." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 3 (2017): 7257–63. http://dx.doi.org/10.24297/jam.v13i3.6196.

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In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation. Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation i

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Petrică, Andreea-Cristina, and Stelian Stancu. "The determinants of exchange rates and the movements of EUR/RON exchange rate via non-linear stochastic processes." Proceedings of the International Conference on Business Excellence 11, no. 1 (2017): 937–48. http://dx.doi.org/10.1515/picbe-2017-0099.

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Abstract Modeling exchange rate volatility became an important topic for research debate starting with 1973, when many countries switched to floating exchange rate system. In this paper, we focus on the EUR/RON exchange rate both as an economic measure and present the implied economic links, and also as a financial investment and analyze its movements and fluctuations through two volatility stochastic processes: the Standard Generalized Autoregressive Conditionally Heteroscedastic Model (GARCH) and the Exponential Generalized Autoregressive Conditionally Heteroscedastic Model (EGARCH). The obj

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Xuan, Haiyan, Lixin Song, Muhammad Amin, and Yongxia Shi. "Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models." Open Mathematics 15, no. 1 (2017): 1539–48. http://dx.doi.org/10.1515/math-2017-0131.

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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 so

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Lee, Sangyeol, Chang Kyeom Kim, and Sangjo Lee. "Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression." Entropy 22, no. 5 (2020): 578. http://dx.doi.org/10.3390/e22050578.

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This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we

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Kipriyanov, Aleksei. "Comparison of Models for Growth-at-Risk Forecasting." Russian Journal of Money and Finance 81, no. 1 (2022): 23–45. http://dx.doi.org/10.31477/rjmf.202201.23.

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During the past several decades, the importance of assessing the risk of GDP growth downturns has increased tremendously. The financial crisis of 2008–2009 and the global lockdown caused by the COVID-19 pandemic demonstrated the vulnerability of the modern economy. As a result, a new framework (Growth-at-Risk) has been developed which allows the estimation of the size of the potential downfall of future GDP growth. However, most of the research focuses on the performance of quantile regression. I apply different approaches to forecasting growth-at-risk, including quantile regression, quantile

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Dissertations / Theses on the topic "Generalised AutoRegressive Conditional Heteroscedastic (GARCH)"

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Szczygielski, Jan Jakub. "An ARCH/GARCH arbitrage pricing theory approach to modelling the return generating process of South African stock returns." Thesis, 2013. http://hdl.handle.net/10539/13035.

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This study investigates the return generating process underlying the South African stock market. The investigation of the return generating process is framed within the Arbitrage Pricing Theory (APT) framework with the APT reinterpreted so as to provide a conceptual framework within which the return generating process can be investigated. In modelling the return generating process, the properties of South African stock returns are taken into consideration and an appropriate econometric framework in the form of Autoregressive Conditional Heteroscedastic (ARCH) and Generalized Autoregress

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Oliver, Barry Ross. "Issues in financial risk management in Australia." Phd thesis, 2001. http://hdl.handle.net/1885/12472.

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This thesis involves a theoretical and empirical examination of issues in financial risk management with a focus on the Australian environment. The primary aim of the thesis is to contribute to the understanding of the use and impact of derivative financial instruments for financial risk management. The majority of published work in this area is from the U.S.A. Therefore, the analysis and results contained in this thesis are of interest to an international audience. The results provide new evidence, in addition to confirmatory evidence, in relation to a number of issues. The thesis is divided

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Book chapters on the topic "Generalised AutoRegressive Conditional Heteroscedastic (GARCH)"

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Ari, Yakup. "The Impact of USD-TRY Forex Rate Volatility on Imports to Turkey from Central Asia." In Economic, Educational, and Touristic Development in Asia. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2239-4.ch004.

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The purpose of this study is to put out the impact of volatility of the USD-TRY forex rate on imports to Turkey from Central Asia. The volatility of the USD/TRY exchange rate is analysed with a conditional variance model which is Generalised Autoregressive Conditional Heteroscedastic (GARCH) model and its extensions. The other section of the methodology is an application of Autoregressive Distributed Lag (ARDL) bounds test which is an efficient approach to determine the cointegration, long-term and short-term relations between macroeconomic variables. The exponential GARCH volatility of the exchange rate and the monthly trade data between the years 2005 and 2018 are used in the ARDL bounds test.

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Osagie Adenomon, Monday. "Financial Time Series Analysis via Backtesting Approach." In Linked Open Data - Applications, Trends and Future Developments. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94112.

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This book chapter investigated the place of backtesting approach in financial time series analysis in choosing a reliable Generalized Auto-Regressive Conditional Heteroscedastic (GARCH) Model to analyze stock returns in Nigeria. To achieve this, The chapter used a secondary data that was collected from www.cashcraft.com under stock trend and analysis. Daily stock price was collected on Zenith bank stock price from October 21st 2004 to May 8th 2017. The chapter used nine different GARCH models (standard GARCH (sGARCH), Glosten-Jagannathan-Runkle GARCH (gjrGARCH), Exponential GARCH (Egarch), Integrated GARCH (iGARCH), Asymmetric Power Autoregressive Conditional Heteroskedasticity (ARCH) (apARCH), Threshold GARCH (TGARCH), Non-linear GARCH (NGARCH), Nonlinear (Asymmetric) GARCH (NAGARCH) and The Absolute Value GARCH (AVGARCH) with maximum lag of 2. Most the information criteria for the sGARCH model were not available due to lack of convergence. The lowest information criteria were associated with apARCH (2,2) with Student t-distribution followed by NGARCH(2,1) with skewed student t-distribution. The backtesting result of the apARCH (2,2) was not available while eGARCH(1,1) with Skewed student t-distribution, NGARCH(1,1), NGARCH(2,1), and TGARCH (2,1) failed the backtesting but eGARCH (1,1) with student t-distribution passed the backtesting approach. Therefore with the backtesting approach, eGARCH(1,1) with student distribution emerged the superior model for modeling Zenith Bank stock returns in Nigeria. This chapter recommended the backtesting approach to selecting reliable GARCH model.

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Mary Bello, Kehinde, David Oluseun Olayungbo, and Benjamin Ayodele Folorunso. "Exchange Rate Volatility and Macroeconomic Performance in Nigeria." In Macroeconomic Analysis for Economic Growth. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100444.

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The study examined the asymmetric relationship between exchange rate volatility and macroeconomic performance in Nigeria covering the period between 1986Q1 and 2019Q4. The Non-linear Generalised Autoregressive Distributive Conditional Heteroscedasticity (GARCH) model was employed. The study was motivated as a result of periodic increase in exchange rate of naira to a dollar and instability of macroeconomic variables in the economy. The presence of Autoregressive Distributive Conditional Heteroscedasticity (ARCH) effect established the use of non-linear GARCH models which showed that volatility was persistent over the period of study. Consequently, the result revealed that exchange rate volatility exhibited a positive relationship with trade balance, industrial output and inflation in the study period. Thus, good news prevailed more over bad news in the foreign exchange market. The study therefore recommended that monetary authorities in Nigeria should regulate exchange rate and macroeconomic variables in order to control the general price level in the economy.

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