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Journal articles on the topic 'GARCH'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Parwati, Lusiana Sani, Endar Hasafah Nugrahani, and Retno Budiarti. "Forecasting Stock Price Using Armax-Garchx Model During The Covid-19 Pandemic." Mathline : Jurnal Matematika dan Pendidikan Matematika 8, no. 2 (2023): 489–502. http://dx.doi.org/10.31943/mathline.v8i2.413.

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The Covid-19 pandemic, which was proclaimed by the World Health Organization (WHO) on March 2020, has impacted stock risk on the capital market. Stock price forecasting can be used to provide future stock projection prices in order to reduce risk. The ARMA GARCH model and its development model can forecast stock prices by incorporating exogenous factors such as the ARMAX GARCH, ARMA GARCHX, and ARMAX GARCHX models. PT Mitra Keluarga Karyasehat Tbk's stock price is analyzed in this study, along with the exogenous factors of total daily positive cases and total daily fatalities cases of Covid-19

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3

Sucarrat, Genaro. "garchx: Flexible and Robust GARCH-X Modeling." R Journal 13, no. 1 (2021): 276. http://dx.doi.org/10.32614/rj-2021-057.

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4

KOUKI, Sonia. "Analysis of Risk Premium Behavior in the Tunisian Foreign Exchange Market During Crisis Period." Journal of Academic Finance 10, no. 2 (2019): 28–38. http://dx.doi.org/10.59051/joaf.v10i2.318.

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In this paper, we empirically examine time-varying risk premia in the Tunisian foreign exchange market by applying GARCH-M modeling to the TND/Euro and TND/USD parities for 1 to 12 months forecasting horizons. Our ultimate objective is to help better manage the parities and to help provide stability to a foreign exchange market where EMH is very weak. Our findings show 1) a heteroscedasticity of residuals for the TND/Euro (all forecasting horizons) and the TND/USD(for 1 month) indicating a lack of stability of their volatility; 2) a non-significant standard deviation of the risk premium agains

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Nugroho, D. B., D. Kurniawati, L. P. Panjaitan, Z. Kholil, B. Susanto, and L. R. Sasongko. "Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility." Journal of Physics: Conference Series 1307 (August 2019): 012003. http://dx.doi.org/10.1088/1742-6596/1307/1/012003.

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6

Hwang *, Soosung, and Steve E. Satchell. "GARCH model with cross-sectional volatility: GARCHX models." Applied Financial Economics 15, no. 3 (2005): 203–16. http://dx.doi.org/10.1080/0960310042000314214.

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Wu, Kejin, Sayar Karmakar, Rangan Gupta, and Christian Pierdzioch. "Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa." Climate 12, no. 5 (2024): 68. http://dx.doi.org/10.3390/cli12050068.

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Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called mod

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Necula, Ciprian. "A Copula-Garch Modelcopula-Garch Model." Economic Research-Ekonomska Istraživanja 23, no. 2 (2010): 1–10. http://dx.doi.org/10.1080/1331677x.2010.11517408.

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9

SO, LEH-CHYAN, and JUN-YANG YU. "IMPROVED DETECTION OF RARE-EVENT RISK OF A PORTFOLIO WITH U.S. REITs." Annals of Financial Economics 10, no. 02 (2015): 1550015. http://dx.doi.org/10.1142/s2010495215500153.

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In measuring the market risks of a portfolio, value-at-risk (VaR) is one of the most commonly used tools. In this paper, the copula-generalized autoregressive conditional heteroskedasticity (GARCH) method is used to determine whether it is a better alternative for estimating the VaR of portfolios containing U.S. real estate investment trusts (REITs). The FTSE NAREIT US Real Estate Index, all REITs and the S&P 500 index are used to construct a portfolio. In total, 2800 daily data covering the period of the subprime mortgage crisis of 2007–2009 are used in this paper. We used six constant an

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

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

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11

Engle, Robert F., and Joshua V. Rosenberg. "GARCH Gamma." Journal of Derivatives 2, no. 4 (1995): 47–59. http://dx.doi.org/10.3905/jod.1995.407924.

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12

Brownlees, Christian T. "Hierarchical GARCH." Journal of Empirical Finance 51 (March 2019): 17–27. http://dx.doi.org/10.1016/j.jempfin.2019.01.009.

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13

Klaassen, Franc. "Improving GARCH volatility forecasts with regime-switching GARCH." Empirical Economics 27, no. 2 (2002): 363–94. http://dx.doi.org/10.1007/s001810100100.

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14

Drost, Feike C., and Bas J. M. Werker. "Closing the GARCH gap: Continuous time GARCH modeling." Journal of Econometrics 74, no. 1 (1996): 31–57. http://dx.doi.org/10.1016/0304-4076(95)01750-x.

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15

van der Weide, Roy. "GO-GARCH: a multivariate generalized orthogonal GARCH model." Journal of Applied Econometrics 17, no. 5 (2002): 549–64. http://dx.doi.org/10.1002/jae.688.

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16

Mansur, Iqbal, Steven J. Cochran, and David Shaffer. "Foreign Exchange Volatility Shifts and Futures Hedging: An ICSS-GARCH Approach." Review of Pacific Basin Financial Markets and Policies 10, no. 03 (2007): 349–88. http://dx.doi.org/10.1142/s0219091507001112.

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In this study, the impact of volatility regime shifts on volatility persistence and hedge ratio estimation is determined for four major currencies using an iterated cumulative sums of squares (ICSS)-GARCH model. Employing a standard GARCH (1,1) model as the benchmark, within-sample results demonstrate that the inclusion of volatility shifts substantially reduces volatility persistence and the significance of the ARCH and GARCH coefficients. In terms of hedging effectiveness, the ICSS-GARCH model outperforms the standard GARCH model for all four currencies. In comparison to two constant volatil

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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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Paul, Samit, and Prateek Sharma. "Quantile forecasts using the Realized GARCH-EVT approach." Studies in Economics and Finance 35, no. 4 (2018): 481–504. http://dx.doi.org/10.1108/sef-09-2016-0236.

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PurposeThis study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.Design/methodology/approachOne-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stag

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19

Akpan, Emmanuel Alphonsus, and Imoh Udo Moffat. "Detection and Modeling of Asymmetric GARCH Effects in a Discrete-Time Series." International Journal of Statistics and Probability 6, no. 6 (2017): 111. http://dx.doi.org/10.5539/ijsp.v6n6p111.

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This study traced the patterns of discrete time series over time with respect to GARCH effect and asymmetric GARCH effect. Particularly, we paid attention to the weakness of the GARCH model in modeling the asymmetry of GARCH effect. In order to handle this weakness, we applied the sign and size bias test which comprises sign bias test, negative size bias test, positive size bias test, and Lagrange Multiplier test in order to identify the asymmetric effect in the residual series of the GARCH model. Where the asymmetric effect is present and significant, we fit the asymmetric GARCH models. Explo

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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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21

Kanal, Febrifke Adria, Tohap Manurung, and Jantje D. Prang. "PENERAPAN MODEL GARCH (GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY) DALAM MENGHITUNG NILAI BETA SAHAM INDEKS PEFINDO25." JURNAL ILMIAH SAINS 18, no. 2 (2018): 67. http://dx.doi.org/10.35799/jis.18.2.2018.19732.

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PENERAPAN MODEL GARCH (GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY) DALAM MENGHITUNG NILAI BETA SAHAM INDEKS PEFINDO25ABSTRAKSaat ini banyak orang yang berfikir untuk berinvestasi. Investasi adalah kegiatan yang mempunyai dua hasil, yaitu positif (return) dan negatif (risiko). Risiko saham khususnya risiko sistematik merupakan salah satu informasi penting yang dibutuhkan investor dalam melakukan investasi. Risiko sistematik dapat diukur dengan beta. Penelitian ini bertujuan untuk mengestimasi nilai beta saham dengan menggunakan model GARCH. Penelitian dilakukan di Universitas Sam

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22

Ou, Phich Hang, and Heng Shan Wang. "Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China." Advanced Materials Research 230-232 (May 2011): 953–57. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.953.

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Previous researches on oil price volatility have been done with parametric models of GARCH types. In this work, we model volatility of crude oil price based on GARCH(p,q) by using Neural Network which is one of powerful classes of nonparametric models. The empirical analysis based on crude oil prices in US and China show that the proposed models significantly generate improved forecasting accuracy than the parametric model of normal GARCH(p,q). Among nine different combinations of hybrid models (for p = 1,2,3 and q = 1,2,3), it is found that NN-GARCH(1,1) and NN-GARCH(2,2) perform better than

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23

A.K., Ockiya, Orumie U.C., and Emmanuel O. "Garch Models Comparison with Symmetric and Asymmetric Process for Univariate Econometric Series." African Journal of Mathematics and Statistics Studies 6, no. 2 (2023): 1–23. http://dx.doi.org/10.52589/ajmss-jdz6zoxg.

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The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was modeled using both symmetric and asymmetric processes. Secondary data from January 2005 to December 2021 on the Consumer Price Index, Exchange Rate, Crude Oil Price, and Inflation Rate were used for this study. The research was conducted using the statistical software packages Minitab and E-view. The aforementioned four macroeconomic variables show a tendency for volatility to cluster across time. In both symmetric and asymmetric processes, the volatility condition and leverage impact coefficients were present. By

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Francq, Christian, Olivier Wintenberger, and Jean-Michel Zakoïan. "GARCH models without positivity constraints: Exponential or log GARCH?" Journal of Econometrics 177, no. 1 (2013): 34–46. http://dx.doi.org/10.1016/j.jeconom.2013.05.004.

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Setiawan, E., N. Herawati, and K. Nisa. "Modeling Stock Return Data using Asymmetric Volatility Models : A Performance Comparison based on the Akaike Information Criterion and Schwarz Criterion." Journal of Engineering and Scientific Research 1, no. 1 (2019): 40. http://dx.doi.org/10.23960/jesr.v1i1.9.

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The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modelhas been widely used in time series forecasting especially with asymmetricvolatility data. As the generalization of autoregressive conditionalheteroscedasticity model, GARCH is known to be more flexible to lag structures.Some enhancements of GARCH models were introduced in literatures, among themare Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) andAsymmetric Power GARCH (APGARCH) models. This paper aims to compare theperformance of the three enhancements of the asymmetric volatility models bymeans of applying the

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Geng, Li Yan, and Yi Gang Liang. "Prediction on Fund Volatility Based on SVRGM-GARCH Model." Advanced Materials Research 403-408 (November 2011): 3763–68. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3763.

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GM-GARCH model is a new hybrid volatility model which integrates grey forecasting model (GM (1,1)) into GARCH model. As for the limitation of the parameters estimation algorithm of GM (1,1) model, a SVRGM-GARCH model is established to enhance volatility forecasting performance further. Firstly, support vector machines for regression (SVR) is utilized to estimate the parameters of GM (1,1) model (SVRGM). Then, the SVRGM model is used to modify the random error term sequence of GARCH model. An empirical research is performed on SSE Fund Index and SZSE Fund Index. The result shows that the SVRGM-

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Benaid, Brahim, Iman Al Hasani, and Mhamed Eddahbi. "Number of Volatility Regimes in the Muscat Securities Market Index in Oman Using Markov-Switching GARCH Models." Symmetry 16, no. 5 (2024): 569. http://dx.doi.org/10.3390/sym16050569.

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The predominant approach for studying volatility is through various GARCH specifications, which are widely utilized in model-based analyses. This study focuses on assessing the predictive performance of specific GARCH models, particularly the Markov-Switching GARCH (MS-GARCH). The primary objective is to determine the optimal number of regimes within the MS-GARCH framework that effectively captures the conditional variance of the Muscat Securities Market Index (MSMI). To achieve this, we employ the Akaike Information Criterion (AIC) to compare different MS-GARCH models, estimated via Maximum L

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Zhang, Yue-Jun, and Han Zhang. "Volatility Forecasting of Crude Oil Market: Which Structural Change Based GARCH Models have Better Performance?" Energy Journal 44, no. 1 (2023): 175–94. http://dx.doi.org/10.5547/ej44-1-zhang.

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GARCH-type models have been widely used for forecasting crude oil price volatility, but often ignore the structural changes of time series, which may lead to spurious volatility persistence. Therefore, this paper focuses on the smooth and sharp structural changes in crude oil price volatility, i.e., smooth shift and regime switching, respectively, and investigates which structural change based GARCH models have better performance for forecasting crude oil price volatility. The empirical results indicate that, first, the flexible Fourier form (FFF) GARCH-type models considering smooth shift can

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Queiroz, Rhenan G. S., and Sergio A. David. "Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility." Risks 11, no. 12 (2023): 211. http://dx.doi.org/10.3390/risks11120211.

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Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the

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Afnanda, Afridho, Maiyastri Maiyastri, and Dodi Devianto. "Model Volatilitas Return Index Saham Syariah Indonesia Melalui Pendekatan Bayesian Markov Switching GARCH." Lattice Journal : Journal of Mathematics Education and Applied 4, no. 1 (2024): 14–26. https://doi.org/10.30983/lattice.v4i1.8381.

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Volatility is an important aspect of financial analysis that plays a crucial role in risk management and investment decision making. Modeling the volatility of financial asset prices is challenging due to its dynamic and complex nature. One approach used to address this problem is the GARCH model. In volatility problems, there is a tendency for structural changes in more complex data so that the GARCH model cannot be used, to overcome this, the Markov Switching GARCH (MS-GARCH) model is used to overcome the problem of changing the data structure. Furthermore, the Bayesian model is also used in

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Adubisi, O. D., and D. J. Adashu. "Inference and Asymmetric GARCH-Model with a New Distributed Innovation." Mikailalsys Journal of Mathematics and Statistics 2, no. 3 (2024): 156–79. http://dx.doi.org/10.58578/mjms.v2i3.3863.

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A novel generalized-odd-generalized exponentiated skew-t (GOGEST) innovation density for the generalized autoregressive conditional heteroskedasticity (GARCH) models is proposed. The features of the proposed distribution were derived. The parameter estimates of the proposed distribution through simulation were carried-out with maximum likelihood estimation technique. The performance of the asymmetric GARCH-GOGEST model relative to five other asymmetric GARCH-various existing innovation densities in volatility modeling was investigated using the Bitcoin log-returns. The empirical results showed

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Marisetty, Nagendra. "Evaluating the Efficacy of GARCH Models in Forecasting Volatility Dynamics Across Major Global Financial Indices: A Decade-long Analysis." Journal of Economics, Management and Trade 30, no. 9 (2024): 16–33. http://dx.doi.org/10.9734/jemt/2024/v30i91238.

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This study investigates the volatility dynamics of five major global financial indices—FTSE 100, Hang Seng, NIKKEI 225, NSE 50, and S&P 500—using a range of GARCH models over a ten-year period from January 1, 2014, to December 31, 2023. The analysis involves preprocessing the data to ensure stationarity, calculating log returns, and conducting stationarity and ARCH effect LM tests. Various GARCH models, including GARCH (0,1), GARCH (1,1), GARCH (1,2), and GARCH (2,2), are applied to capture and forecast volatility. The study aims to determine the most effective model for accurately reflect

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Fink, Holger, Andreas Fuest, and Henry Port. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates." Risks 6, no. 3 (2018): 84. http://dx.doi.org/10.3390/risks6030084.

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A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model implements the yield curve differentials between EUR and the US as exogenous factors. Functional principal component analysis allows us to use the information of basically the whole yield curve in a parsimonious way for exchange rate risk prediction. The data analyzed in our empirical study consist of the EURUSD exchange rate and the EUR- and US-yield curves from 15 August 2005–30 September 2016. As a benchmark, we take an ARMA-GARCH and an ARMAX-GARCHX with the 2y-yield differen

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MEHER, BHARAT KUMAR, SANTOSH KUMAR, ABHISHEK ANAND, et al. "Predictive power of newfangled GARCH models in assessing stock price volatility of Indian textile companies." Industria Textila 76, no. 02 (2025): 171–84. https://doi.org/10.35530/it.076.02.2023138.

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This research endeavours to contribute to the existing body of knowledge by assessing the predictive power of various GARCH models in the specific context of Indian textile companies listed on stock exchanges. The GARCH family encompasses several models, each designed to address specific aspects of volatility dynamics. By evaluating the performance of these models against historical stock price data, we aim to shed light on their efficacy in forecasting volatility patterns and enhancing risk management strategies for investors in the Indian textile sector by applying symmetric and asymmetric m

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Zhang, Huannan, and Qiujun Lan. "GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/386721.

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On the basis of GARCH-RV-type model, we decomposed the realized volatility into continuous sample path variation and discontinuous jump variation, then proposed a new volatility model which we call the GARCH-type model with continuous and jump variation (GARCH-CJ-type model). By using the 5-minute high frequency data of HUSHEN 300 index in China, we estimated parameters of the GARCH-type model, the GARCH-RV-type model, and the GARCH-CJ-type model and compared the three types of models’ predictive power to the future volatility. The results show that the realized volatility and the continuous s

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MESTIRI, Sami. "Modeling the volatility of Bitcoin returns using Nonparametric GARCH models." Journal of Academic Finance 13, no. 1 (2022): 2–16. http://dx.doi.org/10.59051/joaf.v13i1.489.

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Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved

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Abdul Rahman, Nur Haizum, Goh Hui Jia, and Hani Syahida Zulkafli. "GARCH Models and Distributions Comparison for Nonlinear Time Series with Volatilities." Malaysian Journal of Fundamental and Applied Sciences 19, no. 6 (2023): 989–1001. http://dx.doi.org/10.11113/mjfas.v19n6.3101.

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The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is extensively used for handling volatilities. However, with numerous extensions to the standard GARCH model, selecting the most suitable model for forecasting price volatilities becomes challenging. This study aims to examine the performance of different GARCH models in forecasting crude oil price volatilities using West Texas Intermediate (WTI) data. The models considered are the standard GARCH, Integrated GARCH (IGARCH), Exponential GARCH (EGARCH), and Golsten, Jagannathan, and Runkle GARCH (GJR-GARCH), each with no

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Chen, Yufan. "Application of GARCH Model in the Field of Finance." Advances in Economics, Management and Political Sciences 124, no. 1 (2024): 38–43. http://dx.doi.org/10.54254/2754-1169/2024.17736.

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This paper reviews the fundamental principles, mathematical formulation, advantages, and disadvantages of the GARCH model and its extensive applications in finance. Initially, this paper introduces the mathematical structure and fundamental properties of the GARCH model and analyzes the advantages and disadvantages of the model in practical applications. Furthermore, this paper applies the GARCH model in financial market forecasting, risk management, and asset pricing, illustrating these through specific cases. In particular, the GARCH model is employed to forecast the volatility of the electr

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Aktan, Bora, Renata Korsakienė, and Rasa Smaliukienė. "TIME‐VARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS." Journal of Business Economics and Management 11, no. 3 (2010): 511–32. http://dx.doi.org/10.3846/jbem.2010.25.

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As time‐varying volatility has found applications in roughly all time series modelling in economics, it largely draws attention in the areas of financial markets. This study also examines the characteristics of conditional volatility in the Baltic Stock Markets (Estonia, Latvia and Lithuania) by using a broad range of GARCH volatility models. Correctly forecasting the volatility leads to better understanding and managing financial market risk. Daily returns from four Baltic stock indexes are used; Estonia (TALSE index), Latvia (RIGSE index), Lithuania (VILSE index) and synthetic BALTIC benchma

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Hedi, Hedi. "PREDIKSI RETURN KURS RUPIAH TERHADAP DOLAR AMERIKA DENGAN MENERAPKAN MODEL ARIMA DAN GARCH." Sigma-Mu 10, no. 2 (2018): 17–29. http://dx.doi.org/10.35313/sigmamu.v10i2.1226.

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Penelitian ini memprediksi return kurs rupiah terhadap dolar Amerika pada masa yang akan datang dengan menerapkan dua model yaitu ARIMA dan GARCH. Model ARIMA mengasumsikan mean bersyarat tidak nol, sedangkan GARCH mean bersyaratnya nol. Selanjutnya, varian bersyarat model ARIMA konstan, sedangkan GARCH tidak konstan. Dengan menerapkan kriteria AIC dan SC pada masing-masing model, diperoleh model ARIMA(0,1,1) dan GARCH (1,1). Berdasarkan perhitungan RMSE, MAE, dan MAPE, ARIMA(0,1,1) yang paling kecil. Dengan demikian, model ARIMA(0,1,1) yang paling baik untuk memprediksi return kurs rupiah ter

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Kargar, Noorya. "Generalized autoregressive conditional heteroscedasticity (GARCH) for predicting volatility in Stock Market." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 3 (2021): 73–75. http://dx.doi.org/10.54660/.ijmrge.2021.2.3.73-75.

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Volatility plays an important role in financial markets and has held the attention of academics and practitioners. Using Generalized autoregressive conditional heteroscedasticity (GARCH) is one of the effective perspective for forecasting volatility. This study focused on comparing GARCH (P, Q) model with GJR-GARCH (P, Q) model and EGARCH (P, Q) model to make prediction more reliable and accurate. The results suggested that both GARCH (P, Q) model and GJR-GARCH (P, Q) model are good choices for forecasting volatility in financial market, especially for describing heteroscedastic time series. G

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Pedersen, Rasmus Søndergaard, and Anders Rahbek. "TESTING GARCH-X TYPE MODELS." Econometric Theory 35, no. 05 (2018): 1012–47. http://dx.doi.org/10.1017/s026646661800035x.

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We present novel theory for testing for reduction of GARCH-X type models with an exogenous (X) covariate to standard GARCH type models. To deal with the problems of potential nuisance parameters on the boundary of the parameter space as well as lack of identification under the null, we exploit a noticeable property of specific zero-entries in the inverse information of the GARCH-X type models. Specifically, we consider sequential testing based on two likelihood ratio tests and as demonstrated the structure of the inverse information implies that the proposed test neither depends on whether the

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Chlebus, Marcin. "Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?" Przegląd Statystyczny 63, no. 3 (2016): 329–50. http://dx.doi.org/10.5604/01.3001.0014.1212.

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In the study, two-step EWS-GARCH models to forecast Value-at-Risk are analysed. The following models were considered: the EWS-GARCH models with lognormal, Weibull or Gamma distributions as a distributions in a state of turbulence, and with GARCH(1,1) or GARCH(1,1) with the amendment to empirical distribution of random error models as models used in a state of tranquillity. The evaluation of the quality of the Value-at-Risk forecasts was based on the Value-at-Risk forecasts adequacy (the excess ratio, the Kupiec test, the Christoffersen test, the asymptotic test of unconditional coverage and th

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Agyarko, Kofi, Nana Kena Frempong, and Eric Neebo Wiah. "Hybrid Model for Stock Market Volatility." Journal of Probability and Statistics 2023 (April 25, 2023): 1–10. http://dx.doi.org/10.1155/2023/6124649.

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Empirical evidence suggests that the traditional GARCH-type models are unable to accurately estimate the volatility of financial markets. To improve on the accuracy of the traditional GARCH-type models, a hybrid model (BSGARCH (1, 1)) that combines the flexibility of B-splines with the GARCH (1, 1) model has been proposed in the study. The lagged residuals from the GARCH (1, 1) model are fitted with a B-spline estimator and added to the results produced from the GARCH (1, 1) model. The proposed BSGARCH (1, 1) model was applied to simulated data and two real financial time series data (NASDAQ 1

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Wanjuki, Teddy Mutugi, Victor Wandera Lumumba, Emmanuel Koech Kimtai, Morris Kateeti Mbaluka, and Elizabeth Wambui Njoroge. "Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya." European Journal of Mathematics and Statistics 5, no. 4 (2024): 1–18. http://dx.doi.org/10.24018/ejmath.2024.5.4.310.

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This paper conducted a comprehensive comparative analysis of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to forecast financial market volatility, with a specific focus on the Nairobi Stock Exchange Market. The examined models include symmetric and asymmetric GARCH types, such as sGARCH, GJR-GARCH, AR (1) GJG-GARCH, among others. The primary objective is to identify the most suitable model for capturing the complex dynamics of financial market volatility. The study employs rigorous evaluation criteria, including the Akaike Information Criterion (AIC), Bayesi

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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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AMELIAH, VIKY, KOMANG DHARMAWAN, and I. NYOMAN WIDANA. "MEMBANDINGKAN RISIKO SISTEMATIS MENGGUNAKAN CAPM-GARCH DAN CAPM-EGARCH." E-Jurnal Matematika 6, no. 4 (2017): 241. http://dx.doi.org/10.24843/mtk.2017.v06.i04.p172.

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In making stock investments, investors usually pay attention to the rate of return and risk of the stock investment. To calculate risk using capital asset pricing model (CAPM), GARCH, and EGARCH. The data used in this study is secondary data in the form of daily closing price (daily close price), JII price index and monthly SBI rate. All data were processed using matlab 13. The research sample consisted of 6 flagship shares for the period of 2013-2017 ie ADHI, SMGR, UNTR, BSDE, ICBP, KLBF. The conclusion of the research is the beta of each stock including aggressive beta because beta greater t

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Setiawan, Eri, Netti Herawati, and Khoirin Nisa. "Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion." INSIST 3, no. 2 (2018): 160. http://dx.doi.org/10.23960/ins.v3i2.160.

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The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of appl

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Caporin, M. "Dynamic Asymmetric GARCH." Journal of Financial Econometrics 4, no. 3 (2006): 385–412. http://dx.doi.org/10.1093/jjfinec/nbj011.

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Smetanina, Ekaterina. "Real-Time GARCH*." Journal of Financial Econometrics 15, no. 4 (2017): 561–601. http://dx.doi.org/10.1093/jjfinec/nbx008.

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