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Journal articles on the topic 'VAR-GARCH Model'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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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YAO, JING, ZHONG-FEI LI, and KAI W. NG. "MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY." International Journal of Information Technology & Decision Making 05, no. 03 (2006): 503–12. http://dx.doi.org/10.1142/s021962200600209x.

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This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirica

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Naradh, Kimera, Knowledge Chinhamu, and Retius Chifurira. "Estimating the value-at-risk of JSE indices and South African exchange rate with Generalized Pareto and stable distributions." Investment Management and Financial Innovations 18, no. 3 (2021): 151–65. http://dx.doi.org/10.21511/imfi.18(3).2021.14.

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South Africa’s economy has faced many downturns in the previous decade, and to curb the spread of the novel SARS-CoV-2, the lockdown brought South African financial markets to an abrupt halt. Therefore, the implementation of risk mitigation approaches is becoming a matter of urgency in volatile markets in these unprecedented times. In this study, a hybrid generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions, namely the Generalized Pareto Distribution (GPD) and the Nolan’s S0-parameterization stable distribution (SD), were fitted

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Paul, Samit, and Prateek Sharma. "Improved VaR forecasts using extreme value theory with the Realized GARCH model." Studies in Economics and Finance 34, no. 2 (2017): 238–59. http://dx.doi.org/10.1108/sef-05-2015-0139.

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Purpose This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models. Design/methodology/approach The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated usin

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Chaiyawat, Thitivadee, and Pannarat Guayjarernpanishk. "Effective Forecasting of Insurer Capital Requirements: ARMA-GARCH, ARMA-GARCH-EVT, and DCC-GARCH Approaches." Emerging Science Journal 8, no. 6 (2024): 2173–96. https://doi.org/10.28991/esj-2024-08-06-03.

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This research paper presents a comprehensive analysis of three prominent volatility and dependence models for financial time series: ARMA-GARCH, GARCH-EVT, and DCC-GARCH. These models are employed to assess and forecast capital requirements for life and non-life insurer investments. This study evaluates the models' performance in forecasting Value-at-Risk, using daily data on key Thai financial indicators (representing permissible insurer investment assets) from March 2009 to March 2024. Specifically, 1-day and 10-day VaR forecasts are generated using the ARMA-GARCH and DCC-GARCH models, while

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MAHENDRA, I. KOMANG TRY BAYU, KOMANG DHARMAWAN, and NI KETUT TARI TASTRAWATI. "MODEL NON LINIER GARCH (NGARCH) UNTUK MENGESTIMASI NILAI VALUE at RISK (VaR) PADA IHSG." E-Jurnal Matematika 4, no. 2 (2015): 59. http://dx.doi.org/10.24843/mtk.2015.v04.i02.p090.

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In investment, risk measurement is important. One of risk measure is Value at Risk (VaR). There are many methods that can be used to estimate risk based on VaR framework. One of them Non Linier GARCH (NGARCH) model. In this research, determination of VaR used NGARCH model. NGARCH model allowed for asymetric behaviour in the volatility such that “good news” or positive return and “bad news” or negative return. Based on calculations of VaR, the higher of the confidence level and the longer the investment period, the risk was greater. Determination of VaR using NGARCH model was less than GARCH mo

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Napitupulu, Herlina, Rizki Apriva Hidayana, and Jumadil Saputra. "Value-at-Risk Estimation of Indofood (ICBP) and Gas Company (PGAS) Stocks Using the ARMA-GJR-GARCH Model." Operations Research: International Conference Series 2, no. 4 (2021): 102–8. http://dx.doi.org/10.47194/orics.v2i4.183.

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Stocks are one of the most widely used financial market instruments by investors in investing. The most important component of any investment is volatility. Volatility is a conditional measure of variance in stock returns and is important for risk management. In addition to volatility, the important things in investing are return and risk. Risk can be measured using Value-at-Risk (VaR) and can estimate the maximum loss that occurs. The purpose of this study is to determine VaR using the Autoregressive Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedast

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Jiang, Jing Jing, and Bin Ye. "Value-at-Risk Estimation of Carbon Spot Market Based on the Combined GARCH-EVT-VaR Model." Advanced Materials Research 1065-1069 (December 2014): 3250–53. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.3250.

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Based on the analysis of the dynamics of carbon price volatility, this article proposes to develop a combined extreme value theory and conditional variance based Value-at-Risk model (GARCH-EVT-VaR) for short-term risk measurement and estimation of the carbon spot market under the European Union Emission Trading Scheme (EU ETS). The model is implied to the EUA spot market and compared with the traditional GARCH-VaR model, the empirical results show that the GARCH based model underestimates market risks by overlooking the great price shocks, but the GARCH-EVT based model has the ability to take

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Mahlindiani, Lara, Maiyastri ., and Hazmira Yozza. "PENENTUAN RESIKO INVESTASI DENGAN MODEL GARCH PADA INDEKS HARGA SAHAM PT. INDOFOOD SUKSES MAKMUR TBK." Jurnal Matematika UNAND 6, no. 1 (2017): 25. http://dx.doi.org/10.25077/jmu.6.1.25-32.2017.

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Abstrak. Ketika melakukan investasi saham, investor menginginkan return yang tingginamun dengan resiko yang rendah. Untuk mencapai tujuan investasi tersebut, dilakukanpemodelan terhadap harga saham dengan beberapa model seperti Autoregressive (AR),Moving Average (MA) dan Autoregressive Moving Average (ARMA). Aspek pentinglain yang berkaitan dengan investasi adalah pengukuran resiko dengan Value at Risk(VaR) yang merupakan pengukuran kemungkinan kerugian terburuk dalam kondisi pasaryang normal pada kurun waktu t dengan taraf kepercayaan tertentu. Salah satu modelyang dapat mengestimasi resiko a

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Febriani, Karina, Tarno Tarno, and Deby Fakhriyana. "PENENTUAN VALUE AT RISK (VAR) PADA PORTOFOLIO BIVARIAT DENGAN PENDEKATAN COPULA GUMBEL." Jurnal Gaussian 13, no. 1 (2024): 79–87. http://dx.doi.org/10.14710/j.gauss.13.1.79-87.

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One way to minimize risk in stock investment is stock portfolio. Value at Risk (VaR) is a calculation method that can be used to estimate the risk of a stock portfolio. VaR can be measured by parametric and non-parametric approaches. Calculation of VaR with Monte Carlo simulation assumes the data is normally distributed. Stock return data generally has high volatility so that the residual variance of the model is not constant (heteroscedasticity) and not normally distributed. The ARIMA-GARCH model can be used to solve heteroscedasticity problems. Copula is a tool used to model the combined dis

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Qudratullah, Mohammad Farhan. "Perbandingan Berbagai Model Conditionally Heteroscedastic Time Series Dalam Analisis Risiko Investasi Saham Syariah Dengan Metode Value At Risk." Jurnal Fourier 2, no. 1 (2013): 1. http://dx.doi.org/10.14421/fourier.2013.21.1-9.

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Value at Risk (VaR) is one of the tools recommended Bank Indonesia to gauge the risk of an investment, the VaR approach tends to be more associated with the conventional assumption of a normal distribution, while contemporary empirical findings indicate the existence of patterns of abnormality in the nature of statistical data, especially on financial data. Up to this time shares in the Jakarta Islamic Index (JII) is still heavily influenced by the dynamics of market volatility which one, so the necessary in-depth analysis to help investors make the right decisions in investing. This research

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Rizani, Nurul Fitria Fitria, Mustafid Mustafid, and Suparti Suparti. "PENERAPAN METODEEXPECTED SHORTFALLPADA PENGUKURAN RISIKO INVESTASI SAHAM DENGAN VOLATILITAS MODEL GARCH." Jurnal Gaussian 8, no. 1 (2019): 184–93. http://dx.doi.org/10.14710/j.gauss.v8i1.26644.

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One of the methods that can be used to measure stock investment risk is Expected Shortfall (ES). ES is an expectation of risk size which value is greater than Value at Risk (VaR), ES has characteristics of sub-additive and convex. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to model stock data that has high volatility. Calculating ES is done with data that shows deviations from normality using Cornish-Fisher's expansion. This researchapplies the ES at the closing stock price of PT Astra International Tbk. (ASII), PT Bank Negara Indonesia (Persero) Tbk. (

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Tarno, Tarno, Trimono Trimono, Di Asih I. Maruddani, Yuciana Wilandari, and Rianti Siwi Utami. "RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)." MEDIA STATISTIKA 14, no. 2 (2021): 125–36. http://dx.doi.org/10.14710/medstat.14.2.125-136.

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Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model

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Sun, Tieshuang. "Research on Financial Market Risk Based on GARCH-M Model." E3S Web of Conferences 251 (2021): 01106. http://dx.doi.org/10.1051/e3sconf/202125101106.

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Since 1970, with the gradual acceleration of economic globalization and the rapid development of information technology, the financial market has become increasingly unstable. Therefore, we must enhance our competitiveness in the financial market, enhance our ability to resist risks, and master effective measures such as measuring risks. In this paper, GARCH-M model and VAR method are used to study the value at risk of financial market and make an empirical analysis. Firstly, the VAR value calculation method based on GARCH-M model under generalized error distribution is given. Secondly, the cl

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Jeon, Chan-Soo. "Value-at-Risk Forecasting using Realized Volatility Models and GARCH-type Models." Journal of Derivatives and Quantitative Studies 21, no. 2 (2013): 135–67. http://dx.doi.org/10.1108/jdqs-02-2013-b0001.

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The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more

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Summinga-Sonagadu, Ravi, and Jason Narsoo. "Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH." Risks 7, no. 1 (2019): 10. http://dx.doi.org/10.3390/risks7010010.

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In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five distributional assumptions for the innovation process are used to analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in terms of in-sample fit based on various statistical and graphical tests. A more rigorous validation procedure involves testi

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Wang, Jying-Nan, Lu-Jui Chen, Hung-Chun Liu, and Yuan-Teng Hsu. "Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?" International Journal of Economics and Finance 8, no. 1 (2015): 1. http://dx.doi.org/10.5539/ijef.v8n1p1.

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This paper aims to propose the augmented GJR-GARCH (GJR-GARCH<sub>M</sub>) model that extends the GJR-GARCH model by comprising overnight returns volatility (ONV), daily high-low prices range (PK), and fear index (VIX) as explanatory variables for the GJR’s variance equation, respectively. The proposed models are used to estimate the daily value-at-risk values and evaluate their downside risk management performance for the SPDRs covering the period from 2009 to 2014. Empirical results show that the GJR-GARCH<sub>M</sub> model outperforms the GJR-GARCH model for most cas

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Wang, Liang, Tingjia Xu, Longhao Qin, and Chenge Liu. "Research on the Value at Risk of Basis for Stock Index Futures Hedging in China Based on Two-State Markov Process and Semiparametric RS-GARCH Model." Discrete Dynamics in Nature and Society 2019 (June 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/8904162.

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This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS-GARCH model can effectively describe the state transition of variance in VaR and the two-state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS-GARCH models based on two-state Markov process. Furthermore, the logarithm likelihood function method and the kernel estimation with invariable bandwidth method are used for VaR estimation and empirical analysis. It is found that the three fitting errors (MSE, MAD, and QLIKE)

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Pradhan, Kailash. "The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India." South East European Journal of Economics and Business 6, no. 1 (2011): 111–23. http://dx.doi.org/10.2478/v10033-011-0010-2.

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The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms

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R, Adellara Mutya, Maiyastri Maiyastri, and Yudiantri Asdi. "PENENTUAN PORTOFOLIO DAN VALUE AT RISK MENGGUNAKAN MODEL ARMA-GARCH." Jurnal Matematika UNAND 8, no. 1 (2019): 1. http://dx.doi.org/10.25077/jmu.8.1.1-8.2019.

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Dalam dunia investasi saham merupakan bentuk yang paling populer di kalangan masyarakat. Pada saham terdapat nilai risiko dan nilai ekspektasi return yang perlu dipertimbangkan oleh investor. Nilai Ekspektasi return dapat dihitung menggunakan model analisis deret waktu yaitu ARMA, sedangkan nilai risiko dapat diukur menggunakan beberapa metode salah satunya adalah metode Value at Risk (VaR). Untuk menghitung VaR diperlukan komponen volatilitas. Volatilitas dapat diestimasi menggunakan analisis deret waktu yaitu GARCH. Pada penelitian ini, peramalan dilakukan menggunakan data harga penutupan sa

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Soleymani, Fazlollah, Qiang Ma, and Tao Liu. "Managing the Risk via the Chi-Squared Distribution in VaR and CVaR with the Use in Generalized Autoregressive Conditional Heteroskedasticity Model." Mathematics 13, no. 9 (2025): 1410. https://doi.org/10.3390/math13091410.

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This paper develops a framework for quantifying risk by integrating analytical derivations of Value at Risk (VaR) and Conditional VaR (CVaR) under the chi-squared distribution with empirical modeling via Generalized Autoregressive Conditional Heteroskedasticity (GARCH) processes. We first establish closed-form expressions for VaR and CVaR under the chi-squared distribution, leveraging properties of the inverse regularized gamma function and its connection to the quantile of the distribution. We evaluate the proposed framework across multiple time windows to assess its stability and sensitivity

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Buczyński, Mateusz, and Marcin Chlebus. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels." e-Finanse 14, no. 2 (2018): 67–82. http://dx.doi.org/10.2478/fiqf-2018-0013.

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AbstractIn the literature, there is no consensus as to which Value-at-Risk forecasting model is the best for measuring market risk in banks. In the study an analysis of Value-at-Risk forecasting model quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan –

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Yang, Lu, and Shigeyuki Hamori. "Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach." Energies 13, no. 14 (2020): 3700. http://dx.doi.org/10.3390/en13143700.

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We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three nonparametric, distribution-based, generalized, autoregressive, conditional, heteroskedasticity (GARCH) models, and three rolling-window models. We found that the GARCH model estimated by the Fissler and Ziegel (FZ) zero loss minimization (GARCH-FZ) model performs the best at forecasting the VaR and ES in the short term, whereas the hybrid model

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Hidayana, Rizki Apriva, Herlina Napitupulu, and Jumadil Saputra. "Determination of Risk Value Using the ARMA-GJR-GARCH Model on BCA Stocks and BNI Stocks." Operations Research: International Conference Series 2, no. 3 (2021): 62–66. http://dx.doi.org/10.47194/orics.v2i3.176.

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Stocks are common investments that are in great demand by investors. Stocks are also an investment instrument that provides returns but tends to be riskier. The return time series is easier to handle than the price time series. In investment activities, there are the most important components, namely volatility and risk. All financial evaluations require accurate volatility predictions. Volatility is identical to the conditional standard deviation of stock price returns. The most frequently used risk calculation is Value-at-Risk (VaR). Mathematical models can be used to predict future stock pr

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Pradewita, Wella Cintya, Nur Karomah Dwidayati, and Sugiman Sugiman. "Peramalan Volatilitas Risiko Berinvestasi Saham Menggunakan Metode GARCH–M dan ARIMAX–GARCH." Indonesian Journal of Mathematics and Natural Sciences 44, no. 1 (2021): 12–21. http://dx.doi.org/10.15294/ijmns.v44i1.32701.

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Model GARCH–M merupakan pengembangan model GARCH yang dimasukkan variansi bersyarat ke dalam persamaan mean. Model ARIMAX–GARCH merupakan penggabungan model ARIMAX dan GARCH. Kedua model tersebut dapat digunakan untuk mengatasi masalah heteroskedastisitas pada data. Penelitian ini bertujuan menemukan model terbaik untuk peramalan volatilitas risiko berinvestasi saham. Penelitian ini menggunakan literature dengan tahapan perumusan masalah, pengumpulan data, pengolahan dan analisis data, serta penarikan kesimpulan. Dalam analisis dan pembahasan meliputi statistika deskriptif, uji stasioneritas,

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Arfaoui, Mongi, and Aymen Ben Rejeb. "Return Dynamics and Volatility Spillovers Between FOREX and Stock Markets in MENA Countries: What to Remember for Portfolio Choice?" International Journal of Management and Economics 46, no. 1 (2015): 72–100. http://dx.doi.org/10.1515/ijme-2015-0022.

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Abstract This article investigates the interdependence of stock-forex markets in MENA (Middle East and North Africa) countries for the February 26, 1999 to June 30, 2014 period. The analysis has been performed through three competing models: the VAR-CCC-GARCH model of Bollerslev [1990]; the VAR-BEKK-GARCH model of Engle and Kroner [1995]; and the VAR-DCC-GARCH model of Engle [2002]. Our findings confirm that both markets are interdependent and corroborate the stock and flow oriented approaches. We also find that, comparing to optimal weights, hedge ratios are typically low, denoting that hedgi

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Rusyda, Hasna Afifah, Fajar Indrayatna, and Lienda Noviyanti. "Estimation of value at risk by using gjr-garch copula based on block maxima." Indonesian Journal of Statistics and Its Applications 5, no. 2 (2021): 405–14. http://dx.doi.org/10.29244/ijsa.v5i2p405-414.

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This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show

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Kuswanto, Heri, and Endy Norma Chyntia Damayanti. "Analisis Risiko Pada Return Saham Perusahaan Asuransi Menggunakan Metode VaR dengan Pendekatan ARMA-GARCH." Jurnal Matematika, Statistika dan Komputasi 16, no. 1 (2019): 40. http://dx.doi.org/10.20956/jmsk.v16i1.6197.

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Pasar modal Indonesia merupakan salah satu negara tujuan investasi bagi investor di negara-negara maju (developed markets) yang dikenal sebagai emerging market. Perkembangan kondisi perekonomian di Indonesia sendiri dianggap baik bagi para investor untuk menanamkan dana. Saham sektor keuangan menjadi salah satu sektor yang ikut berkembang di sepanjang tahun ini. Tiga dari tujuh saham yang menunjukkan bertumbuh dengan baik adalah PT Asuransi Multi Artha Guna Tbk (AMAG), PT Paninvest Tbk (PNIN), dan PT Lippo General Insurance Tbk (LPGI). Terdapat dua hal penting yaitu tingkat pengembalian atau i

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Wu, Xiaofei, Shuzhen Zhu, and Junjie Zhou. "Research on RMB Exchange Rate Volatility Risk Based on MSGARCH-VaR Model." Discrete Dynamics in Nature and Society 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8719574.

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This paper captures the RMB exchange rate volatility using the Markov-switching GARCH (MSGARCH) models and traditional single-regime GARCH models. Through the Markov Chain Monte Carlo (MCMC) method, the model parameters are estimated to study the volatility dynamics of the RMB exchange rate. Furthermore, we compare the MSGARCH models to the single-regime GARCH specifications in terms of Value-at-Risk (VaR) prediction accuracy. According to the Deviance information criterion method, the research shows that MSGARCH models outperform the single-regime specifications in capturing the complexity of

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Konderla, Tomáš, and Václav Klepáč. "Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, no. 5 (2017): 1687–94. http://dx.doi.org/10.11118/actaun201765051687.

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The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA‑GARCH model based VaR estimates. The common testing via Kup

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Badaye, Hemant Kumar, and Jason Narsoo. "Forecasting multivariate VaR and ES using MC-GARCH-Copula model." Journal of Risk Finance 21, no. 5 (2020): 493–516. http://dx.doi.org/10.1108/jrf-06-2019-0114.

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Purpose This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY. Design/methodology/approach By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent i

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Ramayanti, Rosi, Dodi Devianto, and Delvia Alhusna. "PEMODELAN ARIMA-GARCH UNTUK VOLATILIAS DAN VALUE AT RISK PADA SAHAM PT. GUDANG GARAM TBK." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 4, no. 2 (2023): 1029–40. http://dx.doi.org/10.46306/lb.v4i2.373.

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Investment is one of the development factors in economic activity, there are two basic things that investor must know before making investment decisions, namely: returns and risk. One of the statistical methods to calculate the maximum loss in investment is Value at Risk (VaR). this study aims to calculate VaR on the closing stock price data of Pt. Gudang Garam TBK for the daily period starting from 4 January 2021 to 30 December 2021. Log return data is model by ARIMA. The ARIMA model contains a heteroscedasticity effect so it is inadequate for modelling, one of the models that can overcome th

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Trimono, Trimono, I. Gede Susrama Mas Diyasa, Kartika Maulida Hindrayani, and Mohammad Idhom. "Model ARIMA-ARCH/GARCH dan Ensemble ARIMA-ARCH/GARCH untuk Prediksi Kerugian pada Harga Komoditas Pertanian." PROSIDING SEMINAR NASIONAL SAINS DATA 1, no. 01 (2021): 1–11. http://dx.doi.org/10.33005/senada.v1i01.11.

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Bawang merah dan cabai merah merupakan komoditas tanaman pertanian yang jumlah konsumsinya meningkat setiap tahun. Keadaan ini merupakan peluang yang sangat baik bagi investor untuk berinvestasi pada komoditas tersebut. Meningkatnya jumlah konsumsi akan membuat potensi keuntungan yang diterima investor akan semakin besar. Perkiraan keuntungan investasi dapat dilakukan menggunakan model ARIMA-GARCH dan Ensemble ARIMA-GARCH dengan memanfaatkan nilai return historis. Meskipun potensi keuntungan yang diperoleh cukup besar, berinvestasi pada komoditas tersebut bukan berarti tanpa risiko. Adanya kea

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Gao, Feng, and Fengming Song. "ESTIMATION RISK IN GARCH VaR AND ES ESTIMATES." Econometric Theory 24, no. 5 (2008): 1404–24. http://dx.doi.org/10.1017/s0266466608080559.

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Value-at-risk (VaR) and expected shortfall (ES) are now both widely used risk measures. However, users have not paid much attention to the estimation risk issues, especially in the case of heteroskedastic financial time series. The key challenge arises from the fact that the estimated generalized autoregressive conditional heteroskedasticity (GARCH) innovations are not the true independent innovations. The purpose of this work is to provide an analytical method to assess the precision of conditional VaR and ES in the GARCH model estimated by the filtered historical simulation (FHS) method base

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Trimono, Trimono, and Fira Agista. "MODEL ARMA-GARCH PREDIKSI VALUE-AT-RISK PADA SAHAM PT. ASTRA AGRO LESTARI.TBK." Prosiding Seminar Nasional Informatika Bela Negara 2 (November 25, 2021): 116–21. http://dx.doi.org/10.33005/santika.v2i0.127.

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PT. Astra Agro Lestari Tbk (AALI) merupakan salah satu perusahaan perkebunan dengan nilai kapitalisasi pasar terbesar di Indonesia. Pada Bursa Efek Indonesia, setiap tahun AALI.JK menjadi salah satu saham yang konsisten masuk dalam 5 besar saham Blue Chip. Beberapa karakteristik harga saham AALI.JK antara lain adalah memiliki nilai yang berfluktuasi dan volatilitas return saham yang tidak konstan (bersifat heteroskedastik). Sehingga meskipun memiliki nilai kapitalisasi pasar terbesar, berinvestasi pada saham AALI.JK tetap mengandung unsur risiko. Salah satu model kuantitatif yang dapat digunak

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Faruq, Umar Al, Dwi Fitrizal Salim, and Farida Titik Kristanti. "Risk measurement model on top 10 cryptocurrency market capitalization." Edelweiss Applied Science and Technology 9, no. 4 (2025): 2395–404. https://doi.org/10.55214/25768484.v9i4.6554.

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This study conducted a large-scale analysis to evaluate the performance of traditional and Markov-Switching GARCH (MS-GARCH) models to estimate the volatility of the top 10 cryptocurrencies by market capitalization. The study compared the performance of the models using goodness-of-fit measures, specifically the Deviance Information Criterion (DIC) and the Bayesian Predictive Information Criterion (BPC). Secondly, we assess the forecasting accuracy for one-day-ahead conditional volatility and Value-at-Risk (VaR). The results obtained show that, in a manner consistent with the findings for the

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Ndlovu, Thabani, and Delson Chikobvu. "A Wavelet-Decomposed WD-ARMA-GARCH-EVT Model Approach to Comparing the Riskiness of the BitCoin and South African Rand Exchange Rates." Data 8, no. 7 (2023): 122. http://dx.doi.org/10.3390/data8070122.

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In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR e

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Kruslat, Dariyem Naandi, Waheed B. Yahya, and Msugh Moses Kembe. "Comparative Analysis of Stochastics Approaches in Forecasting Nigeria’s Key Macroeconomic Indicators." Asian Journal of Probability and Statistics 26, no. 12 (2024): 38–50. https://doi.org/10.9734/ajpas/2024/v26i12682.

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The Nigerian economy faces significant volatility in key macroeconomic variables, posing challenges to economic stability and growth. This study compares the performance of ARIMA, GARCH, and VAR models in forecasting GDP, exchange rates, interest rates, inflation, and unemployment, using annual data from 1981-2024. Results show that while ARIMA and GARCH models capture certain dynamics, the VAR model consistently delivers the highest forecast accuracy across all variables. These findings offer valuable insights for policymakers seeking data-driven strategies to stabilize the economy and manage

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Tanasya, Lina, Di Asih I. Maruddani, and Tarno Tarno. "EXPECTED SHORTFALL DENGAN PENDEKATAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH DAN GENERALIZED PARETO DISTRIBUTION." Jurnal Gaussian 9, no. 4 (2020): 505–14. http://dx.doi.org/10.14710/j.gauss.v9i4.29447.

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Stock is a type of investment in financial assets that are many interested by investors. When investing, investors must calculate the expected return on stocks and notice risks that will occur. There are several methods can be used to measure the level of risk one of which is Value at Risk (VaR), but these method often doesn’t fulfill coherence as a risk measure because it doesn’t fulfill the nature of subadditivity. Therefore, the Expected Shortfall (ES) method is used to accommodate these weakness. Stock return data is time series data which has heteroscedasticity and heavy tailed, so time s

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De Moraes, Alex Sandro Monteiro, Antonio Carlos Figueiredo Pinto, and Marcelo Cabus Klotzle. "Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH." Brazilian Review of Finance 13, no. 3 (2015): 394. http://dx.doi.org/10.12660/rbfin.v13n3.2015.53080.

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This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock indices. We used daily data from 1999 to 2014 and an adaptation of the Monte Carlo simulation to estimate VaR and ES forecasts for multiple steps ahead (1, 10 and 20 days ), using FIGARCH and GARCH models for four errors distributions. The results suggest that, in general, the FIGARCH models improve the accuracy of forecasts for longer horizon

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Wu, Maoguo, and Zeyang Li. "Risk Analysis of Shanghai Inter-Bank Offered Rate - A GARCH-VaR Approach." European Scientific Journal, ESJ 13, no. 22 (2017): 252. http://dx.doi.org/10.19044/esj.2017.v13n22p252.

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The inter-bank offered rate widely used by Chinese commercial banks is Shanghai Inter-Bank Offered Rate (Shibor). Shibor has experienced significant development since it was created. It offers different products by duration. Despite its importance in China’s financial market, Shibor’s risk has largely remained unexplored. Making contribution to existing literature on risk management of Shibor, this paper investigates risk of Shanghai Inter- Bank Offered Rate (Shibor) utilizing GARCH-VaR method. The VaR of each product is calculated and compared while GARCH model is designed for a simpler calcu

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