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Bibliografías temáticas / GARCH analysis

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Literatura académica sobre el tema "GARCH analysis"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 20 de febrero de 2023

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Artículos de revistas sobre el tema "GARCH analysis"

1

Sucarrat, Genaro. "garchx: Flexible and Robust GARCH-X Modeling". R Journal 13, n.º 1 (2021): 276. http://dx.doi.org/10.32614/rj-2021-057.

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Teulon, Frederic, Khaled Guesmi y Saoussen Jebri. "Risk Analysis Of Hedge Funds: A Markov Switching Model Analysis". Journal of Applied Business Research (JABR) 30, n.º 1 (30 de diciembre de 2013): 243. http://dx.doi.org/10.19030/jabr.v30i1.8299.

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The paper applies Markov Regime Switching GARCH Model (SW-GARCH) to investigate the volatility behavior of strategies hedge fund monthly returns for the period 1997-2011. The results highlight two different regimes: The first regime is characterized by a high volatility for all strategies hedge fund monthly returns. The second is characterised by lower volatility and positive average returns (except Emerging Market strategy). Our results helped to capture even the short-lived crises along with the material crises of 2001 and 2008.

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3

WU, EDMOND H. C., PHILIP L. H. YU y W. K. LI. "VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS". International Journal of Neural Systems 16, n.º 05 (octubre de 2006): 371–82. http://dx.doi.org/10.1142/s0129065706000779.

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We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

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4

Ma, Dan, Tianxing Yang, Liping Liu y Yi He. "Analysis of Factors Influencing Stock Market Volatility Based on GARCH-MIDAS Model". Complexity 2022 (17 de enero de 2022): 1–10. http://dx.doi.org/10.1155/2022/6176451.

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This paper further extends the existing GARCH-MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long-term volatility components of the GARCH-MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick-by-tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables (i.e., macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value-added (IVA), and M2), Chinese Economic Policy Uncertainty (CEPU) index and Infectious Disease Equity Market Volatility Tracker (EMV) are introduced in the long-run volatility component of the GARCH-MIDAS model. As indicated by the results of this paper, the rAVGRV-based GARCH-MIDAS is slightly better than the RV model-based GARCH-MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant.

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5

Liesl le Roux, Corlise. "Co-Movement and Volatility Analysis of Sugar: Spot and Future". International Journal of Business Administration and Management Research 4, n.º 2 (23 de junio de 2018): 1. http://dx.doi.org/10.24178/ijbamr.2018.4.2.01.

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Co-movement and volatility analysis between variables are an important considerations in investment related decisions. The relationships of spot and two future priced sugar contracts are examined against the currency and main index of Brazil, China, Colombia, India, Indonesia, Mexico, Pakistan, Philippines and Thailand. Sugar which is produced in many countries around the world is the world's largest crop by production in metric tons. Co-movement and volatility analysis includes correlation, vector autoregression, Johansen cointegration test, impulse responses, pairwise Granger causality test and three GARCH models. The three GARCH models are the GARCH, GJR-GARCH and EGARCH models. A long run relationships exists between the three sugar variables, the three sugar variables and the Shanghai SE A Share Index; as well as between the tree sugar variables and the Thai Bhat. Co-movement results indicate that unidirectional and bidirectional relationships exist among the variables. A bi-directional relationship exists between sugar spot and CSCE sugar 11 future as well as between sugar spot and Liffe sugar future. Sugar spot and sugar future have a uni-directional relationship with the indices of Bangkok, Indonesia, Philippines, China, and India. Sugar spot and sugar futures Granger causes the currencies of Brazil, Colombia, Indonesia, Philippines, Thailand and India. The volatility analysis done shows that the AIC and SIC results of the GARCH models which indicates that the original GARCH model fits the data the best for sugar spot and the CSCE sugar 11 future. The EGARCH model fits the data the best for Liffe sugar future.

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6

Li, Yuanbo, Chi Tim Ng y Chun Yip Yau. "GARCH-type factor model". Journal of Multivariate Analysis 190 (julio de 2022): 105001. http://dx.doi.org/10.1016/j.jmva.2022.105001.

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7

Yu, Zhi Tao. "Gold Investment Risk Analysis Model Based on Time Series". Advanced Materials Research 926-930 (mayo de 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 better way to estimate the gold market risk. In addition, VAR-GARCH conditional variance model is also analyzed, and high-risk the real market is the corresponding.

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8

Fu, Sihan, Kexin He, Jialin Li y Zheng Tao. "Exploring Apple’s Stock Price Volatility Using Five GARCH Models". Proceedings of Business and Economic Studies 5, n.º 5 (21 de octubre de 2022): 137–45. http://dx.doi.org/10.26689/pbes.v5i5.4322.

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The financial market is the core of national economic development, and stocks play an important role in the financial market. Analyzing stock prices has become the focus of investors, analysts, and people in related fields. This paper evaluates the volatility of Apple Inc. (AAPL) returns using five generalized autoregressive conditional heteroskedasticity (GARCH) models: sGARCH with constant mean, GARCH with sstd, GJR-GARCH, AR(1) GJR-GARCH, and GJR-GARCH in mean. The distribution of AAPL’s closing price and earnings data was analyzed, and skewed student t-distribution (sstd) and normal distribution (norm) were used to further compare the data distribution of the five models and capture the shape, skewness, and loglikelihood in Model 4 – AR(1) GJR-GARCH. Through further analysis, the results showed that Model 4, AR(1) GJR-GARCH, is the optimal model to describe the volatility of the return series of AAPL. The analysis of the research process is both, a process of exploration and reflection. By analyzing the stock price of AAPL, we reflect on the shortcomings of previous analysis methods, clarify the purpose of the experiment, and identify the optimal analysis model.

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9

Mansur, Iqbal, Steven J. Cochran y David Shaffer. "Foreign Exchange Volatility Shifts and Futures Hedging: An ICSS-GARCH Approach". Review of Pacific Basin Financial Markets and Policies 10, n.º 03 (septiembre de 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 volatility models, the standard GARCH model yields the lowest performance, whereas the ICSS-GARCH model performs at least as well as these models. In out-of-sample analysis, the GARCH model provides substantial variance reductions relative to the constant volatility models. Moreover, the ICSS-GARCH model yields positive variance reductions relative to all competing models, including the standard GARCH model. The results suggest that in cases where dynamic hedging is important, sudden shifts in volatility should not be ignored.

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10

Choi, S. M., S. Y. Hong, M. S. Choi, J. A. Park, J. S. Baek y S. Y. Hwang. "Analysis of Multivariate-GARCH via DCC Modelling". Korean Journal of Applied Statistics 22, n.º 5 (31 de octubre de 2009): 995–1005. http://dx.doi.org/10.5351/kjas.2009.22.5.995.

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Tesis sobre el tema "GARCH analysis"

1

許偉才 y Wai-choi Hui. "Optimal asset allocation under GARCH model". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222717.

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Hui, Wai-choi. "Optimal asset allocation under GARCH model /". Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2160616X.

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Khalilzadeh, Amir Hossein. "Variance Dependent Pricing Kernels in GARCH Models". Thesis, Uppsala universitet, Analys och tillämpad matematik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-180373.

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4

Sze, Mei Ki. "Mixed portmanteau test for ARMA-GARCH models /". View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?MATH%202009%20SZE.

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5

ARAUJO, GUSTAVO SILVA. "ANALYSIS OF THE GARCH OPTION PRICING MODEL USING TELEBRAS CALLS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3343@1.

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Este trabalho procura confirmar a hipótese de o modelo de apreçamento de opções GARCH reduzir alguns dos já amplamente estudados vieses do modelo de Black & Scholes, utilizando opções de compra da Telebras no período julho de 1995 a junho de 2000. Para isso, comparam-se os preços encontrados por intermédio do modelo GARCH com os do modelo de Black & Scholes, cotejando-os com os preços de mercado. Os resultados indicaram que o modelo GARCH foi capaz de diminuir alguns dos vieses, principalmente para opções fora- do-dinheiro com curto tempo para o vencimento. Desta forma, o modelo GARCH se mostrou uma alternativa eficaz ao modelo de Black e Scholes, sobretudo para opções com pouca liquidez, nas quais não é possível a utilização da volatilidade implícita da equação de Black e Scholes.
This study attempts to confirm the hypothesis that the GARCH option pricing model reduces some of the well- documented biases associated with the Black & Scholes model, using Telebras calls in the period of July 1995 to June 2000. For this purpose, the prices obtained by the GARCH model are compared with the ones obtained by the Black and Scholes model, and both of them are checked with the market prices. The results of this research indicate that the GARCH model is able to lessen some biases, specially for out-of-the-money options with short maturity. Thus, the GARCH model is an efficient alternative to the Black and Scholes model, mainly for options with low liquidity, in which it is not possible to use the implicit volatility of the Black and Scholes equation.

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6

De, Wet Walter Albert. "A structural GARCH model an application to portfolio risk management /". Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-04132005-143137.

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7

Yuan, Huimin. "Analysis of Fractionally Differenced Processes with Heteroscedastic Errors". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18585.

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The prime goal of this research is to model the long-range dependency and volatility factors fitting in fractionally differenced ARMA (ARFIMA) and Gegenbauer ARMA processes (GARMA) in financial time series. This extends the efficiency in computing the exact maximum likelihood established by Sowell through conditional quasi maximum likelihood (QMLE) for ARFIMA and GARMA with conditional heteroscedastic errors. In particular, an extended algorithm together with corresponding asymptotic results of QMLE estimators are presented. The Monte Carlo simulation methods are used to study asymptotic properties and report the convergence rate for parameter estimates. Portmanteau test statistics are employed to check the model adequacy. As an application of this theory in the financial industry, a GARMA-GARCH model is fitted to daily returns of China Shanghai Composite stock index.

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8

Fučík, Vojtěch. "Principal component analysis in Finance". Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.

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The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by the industry sector to which the individual stocks belong. Joining together PCA with classical linear regression creates principal components regression which is further in the thesis applied to the German DAX 30 index logarithmic returns forecasting using various macroeconomic and financial predictors. The correlation between two energy stocks returns - Chevron and ExxonMobil is forecasted using orthogonal (or PCA) GARCH. The constructed forecast is then compared with the predictions constructed by the conventional multivariate volatility models - EWMA and DCC GARCH.

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9

Liu, Qingfeng. "Econometric methods for market risk analysis : GARCH-type models and diffusion models". Kyoto University, 2007. http://hdl.handle.net/2433/136053.

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10

Ozkan, Pelin. "Analysis Of Stochastic And Non-stochastic Volatility Models". Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605421/index.pdf.

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Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order to compare these models, the LR test statistic calculated for non-nested hypotheses is obtained.

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Libros sobre el tema "GARCH analysis"

1

Zaffaroni, Paolo. Contemporaneous aggregation of GARCH processes. Roma: Banca d'Italia, 2002.

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2

Ahlstedt, Monica. Analysis of financial risks in a GARCH framework. Helsinki: Bank of Finland, 1998.

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3

Fornari, Fabio. Recovering the probability density function of asset prices using GARCH as diffusion approximations. [Roma]: Banca d'Italia, 2001.

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4

Ghatak, Subrata. Risk and supply response in Turkish agriculture: An ARCH and GARCH analysis (1950-1990). Leicester: University of Leicester, Department of Economics, 1994.

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5

Jens-Peter, Kreiß, Davis Richard A, Andersen Torben Gustav y SpringerLink (Online service), eds. Handbook of Financial Time Series. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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6

POSTECH-BSRI SNU-GARC International Conference on Several Complex Variables (1997 Pʻohang-si, Korea). Complex geometric analysis in Pohang: POSTECH-BSRI SNU-GARC International Conference on Several Complex Variables, June 23-27, 1997 at POSTECH. Editado por Kim Kang-Tae 1957- y Krantz Steven G. 1951-. Providence, R.I: American Mathematical Society, 1999.

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7

Paolella, Marc S. Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. Wiley & Sons, Incorporated, John, 2018.

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8

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. Wiley & Sons, Limited, John, 2018.

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9

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. Wiley & Sons, Incorporated, John, 2018.

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10

Quintana, José Mario, Carlos Carvalho, James Scott y Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.

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Capítulos de libros sobre el tema "GARCH analysis"

1

Ruppert, David. "GARCH Models". En Statistics and Data Analysis for Financial Engineering, 477–504. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7787-8_18.

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2

Ruppert, David y David S. Matteson. "GARCH Models". En Statistics and Data Analysis for Financial Engineering, 405–52. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_14.

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3

Board, John, Alfonso Dufour, Yusuf Hartavi, Charles Sutcliffe y Stephen Wells. "GARCH Analysis of Switchers". En Risk and Trading on London’s Alternative Investment Market, 83–86. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137361301_12.

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4

Lütkepohl, Helmut. "Multivariate ARCH and GARCH Models". En New Introduction to Multiple Time Series Analysis, 557–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-27752-1_16.

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5

Polasek, Wolfgang y Ren Lei. "Generalized Impulse Response Functions for VAR-GARCH-M Models". En Data Analysis, 299–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_24.

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Watanabe, Norio y Fumiaki Okihara. "On GARCH Models with Temporary Structural Changes". En Data Analysis and Applications 1, 91–103. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119597568.ch6.

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7

Almeida, Rui Jorge, Nalan Baştürk, Uzay Kaymak y João Miguel da Costa Sousa. "Conditional Density Estimation Using Fuzzy GARCH Models". En Synergies of Soft Computing and Statistics for Intelligent Data Analysis, 173–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33042-1_19.

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8

Chen, Cathy W. S., Edward M. H. Lin y Yi-Ru Lin. "A Bayesian Perspective on Mixed GARCH Models with Jumps". En Uncertainty Analysis in Econometrics with Applications, 141–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35443-4_10.

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Kridsadarat, Muttalath. "Estimating Time-Varying Systematic Risk by Using Multivariate GARCH". En Uncertainty Analysis in Econometrics with Applications, 227–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35443-4_16.

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Gao, Yan, Chengjun Zhang y Liyan Zhang. "Comparative Analysis of Three GARCH Models Based on MCMC". En Communications in Computer and Information Science, 494–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27452-7_67.

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Actas de conferencias sobre el tema "GARCH analysis"

1

Guo, Weiwei. "The GARCH Analysis of YU'EBAO Annual Yields". En 2016 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA-16). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/wartia-16.2016.36.

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Gonzaga, Alex C. "Estimation of periodic long-memory GARCH-in-mean model". En INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2020. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0081335.

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Wang, Weiqiang, Ying Guo, Zhendong Niu y Yujuan Cao. "Stock indices analysis based on ARMA-GARCH model". En 2009 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2009. http://dx.doi.org/10.1109/ieem.2009.5373131.

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Min, Zhu, Lei Min y Zhu Yongxiang. "Stationarity Analysis of a Model for Asymmtric GARCH". En 2013 Third International Conference on Intelligent System Design and Engineering Applications (ISDEA 2013). IEEE, 2013. http://dx.doi.org/10.1109/isdea.2012.303.

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Ramli, Suraya Fadilah, Zahrul Azmir A. B. S. L. Kamarul Adzhar, Syed Anand Najmi Sayed Abu Bashar y Muhammad Fikri Abdullah. "Analysis of Ethereum versus Bitcoin: The GARCH approach". En The 5TH ISM INTERNATIONAL STATISTICAL CONFERENCE 2021 (ISM-V): Statistics in the Spotlight: Navigating the New Norm. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0112690.

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Yu, Xuehang y Ying Zhan. "Stability Analysis of Chinese Stock Market Based on GARCH Model". En EBIMCS '19: 2019 2nd International Conference on E-Business, Information Management and Computer Science. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377817.3377833.

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Kuzu, Serdar y H. Muhammet Kekeç. "Analysis of the Effect of Weighted Average Cost of the CBRT Funding on BIST100 Index, BISTXBANK Index and Exchange Rate". En International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01884.

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This study is found to find out how Weighted Average Funding Cost, which is new policy tool implemented by The Central Bank of Turkey (CBRT) in 2011, weighted average funding cost -aiming at removing the ambiguities seen in the financial variables and minimizing the effect of capital movements on these variables is reviewed. In this study, the effects of the interest rate policy of the Central Bank of the Republic of Turkey (CBRT) on BIST100 index, BISTXBANK index and exchange rate are tested by Augmented Dickey Fuller Test (ADF), ML-GARCH and DCC GARCH models based on ENGLE, R.F. and SHEPPARD, K. (2001). According to the findings obtained, it is concluded that the decisions of the Weighted Average Funding Cost related to Central Bank of the Republic of Turkey (CBTR) lending and borrowing interest rates are direct effective on BIST100 index, BISTXBANK index but indirect with Exchange rate.

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Caiado, Jorge y Nuno Crato. "A GARCH-based method for clustering of financial time series: International stock markets evidence". En Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0064.

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Li, Li y Lei Xiao. "Value at Risk for Gold Spot Based on Quantile-GARCH Model". En 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/rac-16.2016.155.

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Zhao, Yuan-Qing y Rui-Qing Wang. "Analysis of Electricity Prices Volatility Based on Multicycle GARCH-M Model". En 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). IEEE, 2012. http://dx.doi.org/10.1109/icicee.2012.166.

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Informes sobre el tema "GARCH analysis"

1

Gamboa-Estrada, Fredy y Jose Vicente Romero. Modelling CDS Volatility at Different Tenures: An Application for Latin-American Countries. Banco de la República de Colombia, mayo de 2022. http://dx.doi.org/10.32468/be.1199.

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Assessing the dynamics of risk premium measures and its relationship with macroeconomic fundamentals is important for both macroeconomic policymakers and market practitioners. This paper analyzes the main determinants of CDS in Latin-America at different tenures, focusing on their volatility. Using a component GARCH model, we decompose volatility between permanent and transitory components. We find that the permanent component of CDS volatility in all tenors was higher and more persistent in the global financial crisis than during the recent COVID-19 shock. JEL Classification: C22, C58, G01, G15. Keywords: Credit default swaps (CDS), CDS in Latin-American countries, sovereign risk, volatility, crisis, component GARCH models

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Frydman, Roman y Nicholas Mangee. Expectations Concordance and Stock Market Volatility: Knightian Uncertainty in the Year of the Pandemic. Institute for New Economic Thinking Working Paper Series, septiembre de 2021. http://dx.doi.org/10.36687/inetwp164.

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This study introduces a novel index based on expectations concordance for explaining stock-price volatility when historically unique events cause unforeseeable change and Knightian uncertainty in the process driving outcomes. Expectations concordance measures the degree to which nonrepetitive events are associated with directionally similar expectations of future returns. Narrative analytics of daily news reports allow for assessment of bullish versus bearish views in the stock market. Increases in expectations concordance across all KU events leads to reinforcing effects and an increase in stock market volatility. Lower expectations concordance produces a stabilizing effect wherein the offsetting views reduce market volatility. The empirical findings hold for ex post and ex ante measures of volatility and for OLS and GARCH estimates.

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