Relevant bibliographies by topics / Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)'

Author: Grafiati
Published: 25 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)"

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Hanifa, Rezky Dwi, Mustafid Mustafid, and Arief Rachman Hakim. "PEMODELAN AUTOREGRESSIVE FRACTIONALLY INTEGRATED MOVING AVERAGE DENGAN EFEK EXPONENTIAL GARCH (ARFIMA-EGARCH) UNTUK PREDIKSI HARGA BERAS DI KOTA SEMARANG." Jurnal Gaussian 10, no. 2 (2021): 279–92. http://dx.doi.org/10.14710/j.gauss.v10i2.29933.

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Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away. This phenomenon can be overcome by modeling time series data using the Autoregressive Fractional Integrated Moving Average (ARFIMA) model. This model is characterized by a fractional difference value. ARFIMA (Autoregressive Fractional Integrated Moving Average) model assumes that the residuals are normally distributed, mutually independent, an
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Akhtar, Sohail, Maham Ramzan, Sajid Shah, et al. "Forecasting Exchange Rate of Pakistan Using Time Series Analysis." Mathematical Problems in Engineering 2022 (August 24, 2022): 1–11. http://dx.doi.org/10.1155/2022/9108580.

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Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of
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Rossetti, Nara, Marcelo Seido Nagano, and Jorge Luis Faria Meirelles. "A behavioral analysis of the volatility of interbank interest rates in developed and emerging countries." Journal of Economics, Finance and Administrative Science 22, no. 42 (2017): 99–128. http://dx.doi.org/10.1108/jefas-02-2017-0033.

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Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive condition
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Komal Batool, Mirza Faizan Ahmed, and Muhammad Ali Ismail. "A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index." Reviews of Management Sciences 4, no. 1 (2022): 225–39. http://dx.doi.org/10.53909/rms.04.01.0125.

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Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways. Methodology: Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR. Fin
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Jiang, Haoqing. "The Application of the ARIMA-GARCH Hybrid Model for Forecasting the Apple Stock Price." Journal of Intelligence and Knowledge Engineering 1, no. 1 (2023): 12–15. http://dx.doi.org/10.62517/jike.202304102.

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Modeling and forecasting stock prices is a meaningful task and one of the methods for forecasting is the classic ARIMA models. However, when the data exhibits clustering effects and heteroscedasticity, the generalized auto regressive conditional heteroskedatic (GARCH) model must be used for modeling and forecasting. In this paper, as the object of data analysis, the combination of ARIMA model and GARCH model shows a very good ability to predict the stock price with a very good description of the clustering effect of volatility.
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Zainal, Putri, Yenni Angraini, and Akbar Rizki. "Penerapan Metode Generalized Auto-Regressive Conditional Heteroscedasticity untuk Peramalan Harga Minyak Mentah Dunia." Xplore: Journal of Statistics 12, no. 1 (2023): 12–21. http://dx.doi.org/10.29244/xplore.v12i1.1096.

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Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Ge
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Atahau, Apriani, Robiyanto Robiyanto, and Andrian Huruta. "Predicting Co-Movement of Banking Stocks Using Orthogonal GARCH." Risks 10, no. 8 (2022): 158. http://dx.doi.org/10.3390/risks10080158.

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This study investigates the application of orthogonal generalized auto-regressive conditional heteroscedasticity (OGARCH) in predicting the co-movement of banking sector stocks in Indonesia. All state-owned banking sector stocks in Indonesia were studied using daily data from January 2013 to December 2019. The findings indicate that the OGARCH method can simplify the covariance matrix. Most state-owned banking stocks in the banking sector have a similar principal component influencing their conditional variance. Nonetheless, one stock has different principal components. The findings imply that
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Cheng, Cong, Ling Yu, and Liu Jie Chen. "Structural Nonlinear Damage Detection Based on ARMA-GARCH Model." Applied Mechanics and Materials 204-208 (October 2012): 2891–96. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.2891.

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Two economic models, i.e. auto-regressive and moving average model (ARMA) and generalized auto-regressive conditional heteroscedasticity model (GARCH), are adopted to assess the conditions of structures and to detect structural nonlinear damage based on time series analysis in this study. To improve the reliability of the method for nonlinear damage detection, a new damage sensitive feature (DSF) for the ARMA-GARCH model is defined as a ratio of the standard deviation of the variance time series of ARMA-GARCH model residual errors in test condition to ones in reference condition. Compared to t
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Tolulope, Jerumeh. "Nature, Trends and Drivers of Food Price Volatility in Nigeria." European Journal of Agriculture and Food Sciences 4, no. 6 (2022): 109–17. http://dx.doi.org/10.24018/ejfood.2022.4.6.619.

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The volatility of food prices is an important risk factor which constitutes serious threat to the welfare of millions of people around the world, particularly in developing countries like Nigeria. The study therefore investigated the pattern and drivers of food price volatility in Nigeria using annual and monthly time series data from January,2000 to December, 2020. Data analysis was done using descriptive statistics, Coefficient of Variation, Auto-Regressive Conditional Heteroscedasticity (ARCH) model, Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) model, and Exponential G
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ARIF HUSSAIN, AHMAD BILAL HUSSAIN, and SHAHID ALI. "The Impact of Interest Rate Volatility on Stock Returns Volatility: Empirical Evidence from Pakistan Stock Exchange." Journal of Business & Tourism 3, no. 2 (2021): 53–58. http://dx.doi.org/10.34260/jbt.v3i2.71.

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Apprehension pertaining to Stock return volatility always has been producing the appreciable significance in the various current research works and it has been lucrative to many researchers for forecasting stock market volatility. This study is about the forecasting of stock returns volatility on the basis of interest rate volatility in the well established Pakistan Stock Exchange (PSX). The stock returns are calculated on the basis of KSE 100 index and interest rate volatility is calculated on the basis of monthly treasury bills rate during a period of 1994 to 2016. Various volatility models
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Dissertations / Theses on the topic "Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)"

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Edberg, Christopher, and Oliver Kjellander. "Calendar Anomalies in the Nordic Stock Markets : A quantitative study of the Sell in May effect, January effect & Monthly Anomalies." Thesis, Linnéuniversitetet, Institutionen för ekonomistyrning och logistik (ELO), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105272.

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This study has applied a geographical perspective with the ambition of evaluating the presence of the Sell in May effect, January effect and monthly anomalies in the Nordic stock markets. In extension the study examines the relationship between corporate size and the returns of calendar anomalies. The study has conducted statistical tests based on Newey-West regressions as well as a Generalized Auto-Regressive Conditional Heteroscedasticity model. The findings suggest that the Sell in May and January are present in the Nordic region and partially abide by theory and results of previous researc
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Duarte, Felipe Machado. "Acurácia de previsões para vazão em redes: um comparativo entre ARIMA, GARCH e RNA." Universidade Federal de Pernambuco, 2014. https://repositorio.ufpe.br/handle/123456789/16238.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-03-31T15:28:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Felipe Machado Duarte.pdf: 1439236 bytes, checksum: 970d1a4b49da9d4541eb167aa39a82fa (MD5)<br>Made available in DSpace on 2016-03-31T15:28:39Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Felipe Machado Duarte.pdf: 1439236 bytes, checksum: 970d1a4b49da9d4541eb167aa39a82fa (MD5) Previous issue date: 2014-08-29<br>Em consequência da evolução da internet
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Book chapters on the topic "Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)"

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Day, Theodore E., and Craig M. Lewis. "Stock Market Volatility and the Information Content of Stock Index Options." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0016.

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Abstract Previous studies of the information content of the implied volatilities from the prices of call options have used a cross-sectional regression approach. This paper compares the information content of the implied volatilities from call options on the S&amp;P 100 index to GARCH (Generalized Auto regressive Conditional Heteroscedasticity) and Exponential GARCH models of conditional volatility. By adding the implied volatility to GARCH and EGARCH models as an exogenous variable, the within sample incremental information content of implied volatilities can be examined using a likelihood ra
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Boongasame, Laor. "Factors Affecting Gold Price Prediction and the Use of Deep Learning Techniques for Gold Price Prediction." In Handbook of Research on Artificial Intelligence and Knowledge Management in Asia’s Digital Economy. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5849-5.ch016.

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“Gold” is a term that refers to a collection of precious metals. Gold's bright, shiny yellow color is its defining characteristic. Due to the availability of sophisticated computational techniques such as generalized auto-regressive conditional heteroskedasticity (GARCH), it is possible to perform a more accurate analysis of gold price expression data using deep learning. In this chapter, techniques for predicting the gold price using deep learning are presented. Also talked about were problems that have come up in the field and possible directions for future work.
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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), Int
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Attri, Shradha, Sanjeev Gupta, and Sachin Singh. "Risk Forecasting Using Artificial Intelligence and Machine Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1200-2.ch009.

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The financial market is where physical or virtual assets like foreign exchange, stock, cryptocurrency, and derivatives can be bought and sold. The study examined the role of artificial intelligence and machine learning techniques, mainly focusing on the stock and cryptocurrency markets, which represent physical and virtual assets. Due to the high volatility in the stock and cryptocurrency market, traditional statistical tools like Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) families, Autoregressive Integrated Movin
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Conference papers on the topic "Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH)"

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Li, Qianru, Christophe Tricaud, Rongtao Sun, and YangQuan Chen. "Great Salt Lake Surface Level Forecasting Using FIGARCH Model." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34909.

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In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedast
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Ilbeigi, Mohammad, Alireza Joukar, and Baabak Ashuri. "Modeling and Forecasting the Price of Asphalt Cement Using Generalized Auto Regressive Conditional Heteroscedasticity." In Construction Research Congress 2016. American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479827.071.

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Sheng, Hu, and YangQuan Chen. "The Modeling of Great Salt Lake Elevation Time Series Based on ARFIMA With Stable Innovations." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86864.

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Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourth-largest terminal lake in the world. The elevation of Great Salt Lake has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as FARIMA or ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedast
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