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Littérature scientifique sur le sujet « Subject GARCH model »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Subject GARCH model"

Valencia-Herrera, Humberto, and Francisco López-Herrera. "Markov Switching International Capital Asset Pricing Model, an Emerging Market Case: Mexico." Journal of Emerging Market Finance 17, no. 1 (2018): 96–129. http://dx.doi.org/10.1177/0972652717748089.

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The article shows how the international capital asset pricing model (ICAPM) with Markov regime switching can model the asset returns in the emerging market of Mexico. For most assets, although significant, the international risk premium factor is not subject to regime switching, but the domestic factor is. The probabilities of regimes are correlated with the volatility of assets. A GARCH(1,1) Markov regime switching model offers better adjustment than a non-GARCH. JEL Classification: C58, F36, F65, G12, G15

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Hutajulu, Ronald, and Neli Agustina. "A Comparative Analysis of ARCH/GARCH and Decomposition-ARIMA Models for Gold Price Forecasting in Indonesia." InPrime: Indonesian Journal of Pure and Applied Mathematics 6, no. 2 (2024): 158–71. https://doi.org/10.15408/inprime.v6i2.40249.

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Gold is considered a low-risk investment, serving as a hedge asset and haven against inflation and economic shocks. While gold prices exhibit an increasing trend in the long term, they are subject to short-term fluctuations. Accurate gold price prediction is crucial for investors to maximize returns. This research aims to identify the most suitable method for forecasting gold prices in Indonesia, comparing the decomposition-ARIMA and ARCH-GARCH models. The findings reveal that the decomposition-ARIMA(2,1,2) method surpasses the GARCH(1,0) model in accuracy. The forecasting results indicate an

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Lung Kuo, Shu, and Ching Lin Ho. "The Assessment of Time Series for an Entire Air Quality Control District in Southern Taiwan Using GARCH Model." International Journal of Engineering & Technology 7, no. 3.19 (2018): 119. http://dx.doi.org/10.14419/ijet.v7i3.19.16999.

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The General Autoregressive Conditional Heteroskedastic (GARCH) model and 10 ordinary air quality monitoring stations in the entire air quality control district in Kaohsiung-Pingtung were used in this study. First, the factor analysis results within multivariate statistics were employed to select the main factor that affects air pollution, namely, the photochemical pollution factor. The characteristics of the GARCH model were discussed in terms of asymmetric volatility among the three air pollutants (PM10, NO2, and O3) within the factor. In addition, this study also combined the multiple time s

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Abu Hammad, Ma’mon, Rami Alkhateeb, Nabil Laiche, Adel Ouannas, and Shameseddin Alshorm. "Comparative Analysis of Bilinear Time Series Models with Time-Varying and Symmetric GARCH Coefficients: Estimation and Simulation." Symmetry 16, no. 5 (2024): 581. http://dx.doi.org/10.3390/sym16050581.

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This paper makes a significant contribution by focusing on estimating the coefficients of a sample of non-linear time series, a subject well-established in the statistical literature, using bilinear time series. Specifically, this study delves into a subset of bilinear models where Generalized Autoregressive Conditional Heteroscedastic (GARCH) models serve as the white noise component. The methodology involves applying the Klimko–Nilsen theorem, which plays a crucial role in extracting the asymptotic behavior of the estimators. In this context, the Generalized Autoregressive Conditional Hetero

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Burda, Martin, and Louis Bélisle. "Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo." Dependence Modeling 7, no. 1 (2019): 133–49. http://dx.doi.org/10.1515/demo-2019-0006.

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AbstractThe Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series. The model allows for capturing more flexible dependence patterns than a multivariate GARCH model and also generalizes static copula dependence models. Nonetheless, the model is subject to a number of parameter constraints that ensure positivity of variances and covariance stationarity of the modeled stochastic processes. As such, the resulting distribution of

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García-Medina, Andrés, Norberto A. Hernández-Leandro, Graciela González Farías, and Nelson Muriel. "Multistage allocation problem for Mexican pension funds." PLOS ONE 16, no. 4 (2021): e0249857. http://dx.doi.org/10.1371/journal.pone.0249857.

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The problem of multistage allocation is solved using the Target Date Fund (TDF) strategy subject to a set of restrictions which model the latest regulatory framework of the Mexican pension system. The investment trajectory or glide-path for a representative set of 14 assets of heterogeneous characteristics is studied during a 161 quarters long horizon. The expected returns are estimated by the GARCH(1,1), EGARCH(1,1), GJR-GARCH(1,1) models, and a stationary block bootstrap model is used as a benchmark for comparison. A fixed historical covariance matrix and a multi-period estimation of DCC-GAR

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Maihulla, Sani M., Afolabi W. Babayemi, Gerald I. Onwuka, and Anas S. Maihulla. "Investigating exchange rate volatility in some West African Countries using traditional time series and machine learning models." Caliphate Journal of Science and Technology 7, no. 1 (2025): 170–88. https://doi.org/10.4314/cajost.v7i1.17.

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This study used both conventional time series models and machine learning techniques to examine the volatility of exchange rates in Nigeria, Ghana, Niger, Gambia, and Sierra Leone. Due to their economic importance, regional representation, and variety of exchange rate regimes, the chosen countries were the subject of the 1999–2022 study. Data were sourced from the World Bank and the International Monetary Fund (IMF). The traditional time series models employed included ARIMAX and GARCH, while machine learning techniques included Long Short-Term Memory (LSTM) and hybrid models combining traditi

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Bangar Raju, Totakura, Ayush Bavise, Pradeep Chauhan, and Bhavana Venkata Ramalingeswar Rao. "Analysing volatility spillovers between grain and freight markets." Pomorstvo 34, no. 2 (2020): 428–37. http://dx.doi.org/10.31217/p.34.2.23.

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The International Grain Council (IGC) circulates two price indices which are the Grain and Oilseeds Index (GOI) and the Grain and Oilseeds Freight Market Index (GOFI). These two indices indicate the respective market prices. The GOI markets are affected by various factors like supply and demand, weather, freight markets, etc. This research article attempts to explore and analyse volatility in GOI and GOFI markets using various GARCH family models, that is Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) analysis. The multivariate Dynamic Conditional Correlation Ge

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Öner, Selma, and Hakan Öner. "Symmetric and asymmetric volatility: Forecasting the Borsa Istanbul 100 index return volatility." Financial Internet Quarterly 19, no. 1 (2023): 48–56. http://dx.doi.org/10.2478/fiqf-2023-0005.

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Abstract The development of technology and the globalization of financial markets have increased the volatility in financial markets and caused the emergence of risks and uncertainties that have not been previously encountered. Since traditional econometric models cannot fully explain this volatility, nonlinear conditional variance models such as ARCH, GARCH, EGARCH and TARCH are used today. From this point of view, this study aims to determine the most explanatory model that fund managers who are considering investing in the Borsa Istanbul 100 (BIST 100) Index, and academicians doing research

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Singh, Amit Kumar, Rajat Agarwal, and Rohit Kumar Shrivastav. "Returns and Volatility Spillover Between BSE SENSEX and BSE SME Stock Exchange of India." SEDME (Small Enterprises Development, Management & Extension Journal): A worldwide window on MSME Studies 48, no. 3 (2021): 257–71. http://dx.doi.org/10.1177/09708464211070054.

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Investigating the impact of volatility spillover among various markets has been the subject matter of numerous research. This study investigates the dynamic relationship between the Bombay Stock Exchange index (SENSEX) and the small and medium enterprises (SME) stock index (BSE SME) in India. The study uses univariate autoregressive conditional heteroskedasticity (ARCH)/generalised autoregressive conditional heteroskedasticity (GARCH) models to model the time-varying volatility of the BSE SME market and multivariate BEKK-GARCH analysis to model the volatility of the SENSEX and BSE SME Index co

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Thèses sur le sujet "Subject GARCH model"

WOŹNIAK, Tomasz. "Granger-Causal Analysis of Conditional Mean and Volatility Models." Doctoral thesis, 2012. http://hdl.handle.net/1814/25136.

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Defence date: 18 December 2012<br>Examining Board: Professor Helmut Lütkepohl, DIW Berlin and Freie Universität (External Supervisor); Professor Massimiliano Marcellino, European University Institute; Professor Jacek Osiewalski, Cracow University of Economics; Professor Giampiero Gallo, University of Florence.<br>Recent economic developments have shown the importance of spillover and contagion effects in financial markets as well as in macroeconomic reality. Such effects are not limited to relations between the levels of variables but also impact on the volatility and the distributions. Grange

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Chapitres de livres sur le sujet "Subject GARCH model"

Al Janabi, Mazin A. M. "Evaluation of Optimum and Coherent Economic-Capital Portfolios Under Complex Market Prospects." In Handbook of Research on Big Data Clustering and Machine Learning. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0106-1.ch011.

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This chapter examines the performance of liquidity-adjusted risk modeling in obtaining optimum and coherent economic-capital structures, subject to meaningful operational and financial constraints as specified by the portfolio manager. Specifically, the chapter proposes a robust approach to optimum economic-capital allocation in a liquidity-adjusted value at risk (L-VaR) framework. This chapter expands previous approaches by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple-assets' L-VaR matrix along with

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Actes de conférences sur le sujet "Subject GARCH model"

Gerni, Cevat, Özge Buzdağlı, Dilek Özdemir, and Ömer Selçuk Emsen. "Elections and The Real Exchange Rate Volatility In Turkey (1992-2014)." In International Conference on Eurasian Economies. Eurasian Economists Association, 2016. http://dx.doi.org/10.36880/c07.01553.

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Sudden fluctuations that occur as results of politicians’ manipulation on the macroeconomic variables during the election period are called as Political Business Cycle. In recent years, exchange rate also has become an important subject of many studies in this framework. Before the elections, to gain the public’s votes, politicians firstly put pressure on the exchange rates to prevent currency depreciation, and then this can lead to manipulative fluctuations. In this respect, during the 1992:01-2014:12 periods in Turkey, the impact of the entire local and general elections on the real exchange

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