Relevant bibliographies by topics / VAR-GARCH Model

Contents

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

Academic literature on the topic 'VAR-GARCH Model'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "VAR-GARCH Model"

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Indah, Novi Permata, Dian Permata Sari, I. Putu Eka Wijaya, and Madjidainun Rahma. "VaR prediction for GARCH (1,1) model with normal and student-t error distribution." Jurnal Pijar Mipa 17, no. 1 (2022): 89–93. http://dx.doi.org/10.29303/jpm.v17i1.3215.

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This study aims at determining the estimated parameters of the GARCH (1.1) model establishing the prediction of the VaR value, and defining the accuracy of the VaR prediction. In this study, the error in the GARCH (1,1) model uses a normal distribution and student-t distribution. The research method focuses on parameter calculation and the prediction of VaR value within two aspects regarding analytic and numeric aspects. Analytically, the prediction of the VaR value and the accuracy of the prediction of VaR value through the VaR coverage opportunity. It isn't easy to estimate the parameters fo
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Wang, Yuling, Yunshuang Xiang, and Huan Zhang. "Comparison and Forecasting of VaR Models for Measuring Financial Risk: Evidence from China." Discrete Dynamics in Nature and Society 2022 (March 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/5510721.

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With increasing extremal risk, VaR has been becoming a popular methodology because it is easy to interpret and calculate. For comparing the performance of extant VaR models, this paper makes an empirical analysis of five VaR models: simple VaR, VaR based on RiskMetrics, VaR based on different distributions of GARCH-N, GARCH-GED, and GARCH-t. We exploit the daily closing prices of the Shanghai Composite Index from January 4, 2010, to April 8, 2020, and divide the entire sample into two periods for empirical analysis. The rolling window is used to update the daily estimation of risk. Based on th
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Nasrudin, Muhammad, Endah Setyowati, and Shindi Shella May Wara. "Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector." Jurnal Varian 8, no. 1 (2024): 89–96. https://doi.org/10.30812/varian.v8i1.4239.

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Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one ofwhich is in stock price modeling. This research aims to model the causal relationship between stockprices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity(VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and modeldynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 toMay 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR,ADRO, and ANTM. The
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Yu, Zhi Tao. "Gold Investment Risk Analysis Model Based on Time Series." Advanced Materials Research 926-930 (May 2014): 3834–37. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3834.

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With the growing size of the gold market, all kinds of gold investment varieties are constantly emerging, namely, meet the residents needs and requirements of the investment risk, also makes the prime financial rise. This paper analyzes quantify the risk of gold market fundamentals, and has a deep research on the historical development of the global gold market, global gold market developing trends and factors affecting the gold price. This paper focuses on analysis of VAR risk management theory and VAR-GARCH model. VAR-GARCH model can be more effective on the VAR value forecast, which is a be
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Likitratcharoen, Danai, and Lucksuda Suwannamalik. "Assessing Financial Stability in Turbulent Times: A Study of Generalized Autoregressive Conditional Heteroskedasticity-Type Value-at-Risk Model Performance in Thailand’s Transportation Sector during COVID-19." Risks 12, no. 3 (2024): 51. http://dx.doi.org/10.3390/risks12030051.

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The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of VaR models frequently faces scrutiny, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and
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Nurhayati, Nurhayati, Wiwin Apriani, and Ariestha Widyastuty Bustan. "Value at Risk Prediction for the GJR-GARCH Aggregation Model." Pattimura International Journal of Mathematics (PIJMath) 1, no. 1 (2022): 01–06. http://dx.doi.org/10.30598/pijmathvol1iss1pp01-06.

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Volatility is the level of risk faced due to price fluctuations. The greater the volatility brings, the greater the risk. We need a measure such as Value at Risk (VaR) and volatility modeling to overcome this. The most frequently used volatility model in the financial sector is GARCH. However, this model is still unable to accommodate the asymmetric nature, so the GJR-GARCH model was developed. In addition, this study also used aggregation returns with two assets in them. This study aimed to determine the VaR prediction for the GJR-GARCH(1.1) aggregation model and its comparison with the GARCH
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Trimono, Trimono, Prismahardi Aji Riyantoko, and Fira Agista. "Model ARMA-GARCH dan Ensemble ARMA-GARCH untuk Prediksi Value-at-Risk pada Portofolio Saham." PROSIDING SEMINAR NASIONAL SAINS DATA 2, no. 1 (2022): 83–91. http://dx.doi.org/10.33005/senada.v2i1.52.

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Portofolio saham merupakan salah satu bentuk investasi yang dapat digunakan meminimumkan risiko kerugian. Pada portofolio saham, nilai risiko kerugian dapat diprediksi melalui nilai return. Apabila variansi return portofolio bersifat heteroskedastik, prediksi risiko dapat digunakan menggunakan model VaR ARCH/ GARCH atau VaR ARCH/GARCH Kombinasi. Selanjutnya, keakuratan VaR dalam memprediksi nilai risiko kerugian diuji melalui uji Backtesting. Pada penelitian ini, portofolio saham disusun atas saham PT. Indofood Tbk (INDF) dan saham PT. Astra Agro Lestari (Tbk) periode 2 Agustus 2012 sampai den
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Shahidi, Ali, Yousef Ramezani, Mohammad Nazeri-Tahroudi, and Saeedeh Mohammadi. "Application of vector autoregressive models to estimate pan evaporation values at the Salt Lake Basin, Iran." Időjárás 124, no. 4 (2020): 463–82. http://dx.doi.org/10.28974/idojaras.2020.4.3.

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Thousands of billions of cubic meters of fresh water collected at great expense are evaporated annually from dams, and salts of evaporating water reduces water quality. In this study, the efficiency of the vector autoregressive model called VAR model has been examined on an annual scale using pan evaporation data in the salt lake basin, Iran, during the statistical period of 1996–2015. Since hydrologic modeling is concerned with the accuracy and efficiency of the model, therefore, we must try to evolve and improve the results of the models. In this study, VAR multivariable time series and nonl
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Kusumaningtyas, Alfi Reny, and Abdul Aziz. "METODE HISTORIS UNTUK PERHITUNGAN VALUE AT RISK PADA MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDACITY IN MEAN." Jurnal Matematika "MANTIK" 2, no. 1 (2016): 1. http://dx.doi.org/10.15642/mantik.2016.2.1.1-6.

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Investment is a commitment of the placement of the data on an object or a few investments with expectations will benefit in the future. The main motive is to seek investment gain or profit in a certain amount, but behind the good side there is one side that can harm or the risk of, for it required a measurement of risk where methods of value at risk (VaR) is very popular is widely used by the financial industry worldwide. Three main method on calculation of VaR historical method, parametric method and Monte Carlo method. So, the selected calculation of VaR GARCH-M model with historical simulat
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Klepáč, Václav, and David Hampel. "Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1287–95. http://dx.doi.org/10.11118/actaun201563041287.

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The article points out the possibilities of using static D-Vine copula ARMA-GARCH model for estimation of 1 day ahead market Value at Risk. For the illustration we use data of the four companies listed on Prague Stock Exchange in range from 2010 to 2014. Vine copula approach allows us to construct high-dimensional copula from both elliptical and Archimedean bivariate copulas, i.e. multivariate probability distribution, created from process innovations. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we backtested D-Vin
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Dissertations / Theses on the topic "VAR-GARCH Model"

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Andersson, Oscar, and Erik Haglund. "Financial Econometrics: A Comparison of GARCH type Model Performances when Forecasting VaR." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243245.

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This essay investigates three different GARCH-models (GARCH, EGARCH and GJR-GARCH) along with two distributions (Normal and Student’s t), which are used to forecast the Value at Risk (VaR) for different return series. Seven major international equity indices are examined. The purpose of the essay is to answer which of the three models that is better at forecasting the VaR and which distribution is more appropriate.  The results show that the EGARCH(1,1)  is preferred for all indices included in the study.
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Telmoudi, Fedya. "Estimation and misspecification Risks in VaR estimation." Thesis, Lille 3, 2014. http://www.theses.fr/2014LIL30061/document.

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Dans cette thèse, nous étudions l'estimation de la valeur à risque conditionnelle (VaR) en tenant compte du risque d'estimation et du risque de modèle. Tout d'abord, nous considérons une méthode en deux étapes pour estimer la VaR. La première étape évalue le paramètre de volatilité en utilisant un estimateur quasi maximum de vraisemblance généralisé (gQMLE) fondé sur une densité instrumentale h. La seconde étape estime un quantile des innovations à partir du quantile empirique des résidus obtenus dans la première étape. Nous donnons des conditions sous lesquelles l'estimateur en deux étapes de
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Alsaedi, Yasir H. "An Investigation of the Effects of Solar and Wind Prices on the Australia Electricity Spot and Options Markets: A Time Series Analysis." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410472.

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Electricity pricing is recognised as being among the most important contemporary policy issues in Australia, and it also represents a critical component of current discussions concerning energy and climate-change policies. Attempts to move forward with energy and climate-change policies have been mostly stymied by concerns regarding potential increases in electricity prices. In relation to such policy discussions, renewable electricity generation is currently considered to be a fundamental factor influencing electricity prices. Due to the increasing penetration of both wind and solar power gen
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Santos, Julio Cesar Grimalt dos. "Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/13521.

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Submitted by JULIO CESAR GRIMALT DOS SANTOS (grimbil@hotmail.com) on 2015-02-23T21:08:49Z No. of bitstreams: 1 Dissertação Final.pdf: 1416129 bytes, checksum: fcbac3f948355bac6f5b59569bf2610a (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-03-04T16:04:21Z (GMT) No. of bitstreams: 1 Dissertação Final.pdf: 1416129 bytes, checksum: fcbac3f948355bac6f5b59569bf2610a (MD5)<br>Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-03-12T19:06:25Z (GMT) No. of bitstreams: 1 Dissertação Final.pdf: 1416129 bytes, checksum
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Bisová, Sára. "Modely vývoje inflace a její volatility v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-73484.

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This paper focuses on analysing and modelling inflation and its dynamics in Czech Republic applying a special kind of econometric models. Firstly economic theory of inflation is mentioned - fundamental terms, measuring methods of inflation, the way Czech national bank is monitoring the inflation and obviously a short summary of historical evolution of inflation in Czech economy. In the second part of this paper two econometric concepts of modelling time series are introduced - vector autoregression models (VAR models) and volatility models, concretely ARCH and GARCH models. In connection with
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Alshogeathri, Mofleh Ali Mofleh. "Macroeconomic determinants of the stock market movements: empirical evidence from the Saudi stock market." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/11989.

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Doctor of Philosophy<br>Department of Economics<br>Lance J. Bachmeier<br>This dissertation investigates the long run and short run relationships between Saudi stock market returns and eight macroeconomic variables. We investigate the ability of these variables to predict the level and volatility of Saudi stock market returns. A wide range of Vector autoregression (VAR) and generalized autoregressive conditional heteroskedasticity (GARCH) models estimated and interpreted. A Johansen-Juselius cointegration test indicates a positive long run relationship between the Saudi stock price index and
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Melcher, Ota. "Spekulační aktivita na trhu s ropou a její vliv na cenu komodity." Doctoral thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-261935.

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This study aims to analyse the precrisis period on the oil markets with a primary objective of assessing the role of speculation in the commodity's price development and its volatility. First it depicts the rapidly increasing speculative activity on the futures market together with the parallel oil price surge. The speculation is initially proxied by non-commercial traders' positions and subsequently quantified by Working's T-index. The paper then uses speculative traders' positions and both spot and futures prices to test for Granger causality within the framework of VAR models. For the sake
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DELLA, NOCE MATTEO. "Un modello VAR-GARCH multivariato per il mercato elettrico italiano." Doctoral thesis, Università Cattolica del Sacro Cuore, 2011. http://hdl.handle.net/10280/1108.

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E’ stato estesamente appurato che i mercati dell'elettricità mostrano mean-reversion e elevata volatilità dei prezzi. Questo lavoro utilizza un modello VAR-MGARCH al fine di cogliere queste caratteristiche presenti sul mercato dell'energia elettrica italiana (IPEX) e analizzare le interrelazioni esistenti tra le diverse regioni in cui il mercato è suddiviso. L’analisi è condotta sui prezzi giornalieri dal 1 ° gennaio 2006 al 31 dicembre 2008. I coefficienti stimati dalle equazioni condizionali indicano che i mercati regionali sono abbastanza integrati e i prezzi regionali dell'energia elettric
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DELLA, NOCE MATTEO. "Un modello VAR-GARCH multivariato per il mercato elettrico italiano." Doctoral thesis, Università Cattolica del Sacro Cuore, 2011. http://hdl.handle.net/10280/1108.

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E’ stato estesamente appurato che i mercati dell'elettricità mostrano mean-reversion e elevata volatilità dei prezzi. Questo lavoro utilizza un modello VAR-MGARCH al fine di cogliere queste caratteristiche presenti sul mercato dell'energia elettrica italiana (IPEX) e analizzare le interrelazioni esistenti tra le diverse regioni in cui il mercato è suddiviso. L’analisi è condotta sui prezzi giornalieri dal 1 ° gennaio 2006 al 31 dicembre 2008. I coefficienti stimati dalle equazioni condizionali indicano che i mercati regionali sono abbastanza integrati e i prezzi regionali dell'energia elettric
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Zhou, Dongya. "VALUE-AT-RISK ESTIMATION USING GARCH MODELS FOR THE CHINESE MAINLAND STOCK MARKET." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412997.

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With the acceleration of economic globalization, the immature Chinese mainland stock market is gradually associated with the stock markets of other countries. This paper predict the return rate of Chinese mainland stock market using several models from GARCH family, test the predictability by calculating Value-at-Risk, also capture the dynamic correlation between other fifive countries or region and mainland China by DCC-GARCH model. The results indicate that E-ARMA-GARCH model fifits the best due to the signifificant heteroscedasticity and leverage effect of Chinese mainland stock market. It
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Book chapters on the topic "VAR-GARCH Model"

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Kannan, K. Senthamarai, and V. Parimyndhan. "Modelling and Estimating of VaR Through the GARCH Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50920-9_25.

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Mokrzycka, Justyna. "VaR and ES Calculation with a Bayesian Dynamic tCopula-GARCH Model." In Advances in Cross-Section Data Methods in Applied Economic Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38253-7_46.

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Li, Ganqiong, Shiwei Xu, Shengwei Wang, and Haipeng Yu. "Study on Extreme Risk Measurement of Chinese Soybean Futures Market—VaR Based on GARCH Model." In Proceedings of 2013 World Agricultural Outlook Conference. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54389-0_14.

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Büyükkara, Göknur, Onur Enginar, and Hüseyin Temiz. "Volatility Spillovers Between Oil and Stock Market Returns in G7 Countries: A VAR-DCC-GARCH Model." In Regulations in the Energy Industry. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32296-0_10.

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Jiang, Feifan, and Yucan Liu. "The analysis of inter-industry VaR on China stock markets–based on the GARCH-EVT-copula model." In Economic and Business Management. CRC Press, 2022. http://dx.doi.org/10.1201/9781003203704-12.

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Zhao, Ming, and Meiling Du. "Comparative Analysis of FinTech Risks in China and the U.S.: An Application of the GARCH-VaR Model." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-96653-8_28.

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Xia, Shi. "A Dynamic Computing Research for Value at Risk (VaR) of Shanghai Stock Market Based on the GARCH Model." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28466-3_41.

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Chen, Xuezhong, Yang Liu, and Anran Chen. "Price Risk Measurement Model of Pledge Financing of Lending Institution in Natural Rubber Supply Chain Based on VaR-GARCH Method." In LISS2019. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5682-1_29.

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Polasek, Wolfgang, and Ren Lei. "Generalized Impulse Response Functions for VAR-GARCH-M Models." In Data Analysis. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_24.

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Cao, Guangxi, Jianping Guo, and Lin Xu. "Comparative Analysis of VaR Estimation of Double Long-Memory GARCH Models: Empirical Analysis of China’s Stock Market." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02298-2_63.

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Conference papers on the topic "VAR-GARCH Model"

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Liu, Yan-chun, and Ming Li. "A Calculation of VaR Based on GARCH Model." In 2006 International Conference on Service Systems and Service Management. IEEE, 2006. http://dx.doi.org/10.1109/icsssm.2006.320538.

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Ren, Yangfan. "Simulation of VaR Based on Monte Carlo-Copula - GARCH Model." In 2017 2nd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icesame-17.2017.421.

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Li, Jun, Yun-Qi Zhang, and Qi-Fa Xu. "Garch and SV Model Based VaR Modeling for Energy Risk Management." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370149.

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Deng, Yalin, Jingyuan Wang, Sheng Ge, Chao Li, and Fei Yang. "Forecasting Portfolio Risks in Futures Markets Using the GARCH-VaR Model." In 2018 15th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2018. http://dx.doi.org/10.1109/icsssm.2018.8465028.

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Nafisi-Moghadam, Maryam, and Shahram Fattahi. "A Hybrid Model of VAR-DCC-GARCH and Wavelet Analysis for Forecasting Volatility." In ITISE 2022. MDPI, 2022. http://dx.doi.org/10.3390/engproc2022018006.

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Zhu, Xuemin, Sheng Liu, and Xuelin Zhu. "Risk Quantification and Dynamic Guarantee Proportion Optimization Based on Improved Var-GARCH Model." In Proceedings of the 3rd International Conference on Big Data Economy and Information Management, BDEIM 2022, December 2-3, 2022, Zhengzhou, China. EAI, 2023. http://dx.doi.org/10.4108/eai.2-12-2022.2332277.

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Yang, Jie, and Shaozong Zhang. "VaR Model Measure Exchange Rate Risk Based on GARCH Approach and EVT Theory." In 2nd International Conference on Contemporary Education, Social Sciences and Humanities (ICCESSH 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/iccessh-17.2017.186.

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Liu, Jiayi. "A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models for Volatility and Var Forecast." In Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Changsha, China. EAI, 2023. http://dx.doi.org/10.4108/eai.6-1-2023.2330313.

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Gao, Yanqun. "Empirical Study on the Investment Risk of SSE 50 Index Based on GARCH-VaR Model." In Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023, Beijing, China. EAI, 2024. http://dx.doi.org/10.4108/eai.17-11-2023.2342664.

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Yang, Bing, Chenggang Li, Di Wang, and Xin He. "Research on the risk of Shanghai Composite Index based on VaR and GARCH model." In 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/essaeme-17.2017.375.

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