Littérature scientifique sur le sujet « Subject GARCH model »
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Articles de revues sur le sujet "Subject GARCH model"
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Valencia-Herrera, Humberto, et Francisco López-Herrera. « Markov Switching International Capital Asset Pricing Model, an Emerging Market Case : Mexico ». Journal of Emerging Market Finance 17, no 1 (26 février 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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Burda, Martin, et Louis Bélisle. « Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo ». Dependence Modeling 7, no 1 (3 juin 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 parameters of interest is highly irregular, characterized by skewness, asymmetry, and truncation, hindering the applicability and accuracy of asymptotic inference. In this paper, we propose Bayesian analysis of the CMGARCH model based on Constrained Hamiltonian Monte Carlo (CHMC), which has been shown in other contexts to yield efficient inference on complicated constrained dependence structures. In the CMGARCH context, we contrast CHMC with traditional random-walk sampling used in the previous literature and highlight the benefits of CHMC for applied researchers. We estimate the posterior mean, median and Bayesian confidence intervals for the coefficients of tail dependence. The analysis is performed in an application to a recent portfolio of S&P500 financial asset returns.
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Lung Kuo, Shu, et 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 (7 septembre 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 series model VARMA to explore changes in the time series of the three air pollutants and to discuss their predictability.The results showed that, although the coefficient of the GARCH model was negative when estimating the variance equation, the conditional variance would always be positive after taking the logarithm. The results also suggested that the GARCH model was quite capable of capturing the asymmetric volatility. In other words, if the condition that pollution factors might be subject to seasonal changes or outliers generated by the human contamination is not considered, the GARCH model had very good ability to verify the results and make predictions, regardless of whether it adopted any of the three risk concepts: normal distribution, t-distribution, and generalized error distribution. For example, under the trend of time series temporal and spatial distribution in various pollution concentrations of photochemical factors, the optimal model VARMA(2,0,0)-GARCH(1,1) selected in this study was used to conduct time series predictability after the verification procedure. After capturing the last 50 entries of data on O3 concentrations in the sequence, the results showed that the predictability correlation (r) was 0.812, the predictability of NO2 was 0.783 and the predictability of PM10 was 0.759. It can be learned from the results that under the sequence of the GARCH model with strong asymmetric volatility, the residual values of these three sequences as white noise were quite evident, and there was also a high degree of correlation in predictability.
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García-Medina, Andrés, Norberto A. Hernández-Leandro, Graciela González Farías et Nelson Muriel. « Multistage allocation problem for Mexican pension funds ». PLOS ONE 16, no 4 (13 avril 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-GARCH(1,1) are also considered as inputs of the objective function. Forecasts are evaluated through their asymmetric dependencies as quantified by the transfer entropy measure. In general, we find very similar glide-paths so that the overall structure of the investment is maintained and does not rely on the particular forecasting model. However, the GARCH(1,1) under a fixed historical covariance matrix exhibits the highest Sharpe ratio and in this sense represents the best trade-off between wealth and risk. As expected, the initial stages of the obtained glide-paths are initially dominated by risky assets and gradually transition into bonds towards the end oof the trajectory. Overall, the methodology proposed here is computationally efficient and displays the desired properties of a TDF strategy in realistic settings.
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Bangar Raju, Totakura, Ayush Bavise, Pradeep Chauhan et Bhavana Venkata Ramalingeswar Rao. « Analysing volatility spillovers between grain and freight markets ». Pomorstvo 34, no 2 (21 décembre 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 Generalized Autoregressive Conditional Heteroskedasticity model (DCC GARCH) is used to find the spillovers between the two markets and thereby explore the effect of GOFI on GOI markets from the year 2013. The research article consists of four sections after introducing the subject namely a literature review, research methodology and models, analysis and conclusions of the study.
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Singh, Amit Kumar, Rajat Agarwal et 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 (septembre 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 considering the existence of some linkages between them. The study is based on the daily stock indices data ranging from 16 August 2012 to 31 March 2021. Furthermore, the study reveals statistically significant internal volatility spillovers in the SME stock market and the cross-volatility transmission between the two indices. It affirms statistically significant volatility and return spillover between the main market index, SENSEX and SME index, BSE SME. The findings of this research have important implications for the diversification process. It provides crucial signals to investors, portfolio managers and policymakers, especially when there has been much impetus and promotion from the Indian government and emerging foreign investments in India’s SMEs in recent years.
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Singh, Vipul Kumar, et Pushkar Pachori. « A Kaleidoscopic Study of Pricing Performance of Stochastic Volatility Option Pricing Models : Evidence from Recent Indian Economic Turbulence ». Vikalpa : The Journal for Decision Makers 38, no 2 (avril 2013) : 61–80. http://dx.doi.org/10.1177/0256090920130204.
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A whole host of researchers have modeled volatility as a non-constant stochastic process, based on the principle that volatility follows a stochastic process whose parameters are not directly observable in the market. The objective of this research paper is to empirically investigate the forecasting performance of three most dominant models of this species namely, Hull-White (1988), Heston�s (1993), and Heston-Nandi GARCH (2000) option pricing model. These three models have been collaterally compared and contrasted against Black-Scholes and market for pricing S&P CNX Nifty 50 index option of India. The Hull-White model not only warrants a range of stochastic volatility specifications but also incorporates correlation of volatility of asset return and its price changes. The closed form Heston�s (1993) model explicitly and elaborately communicates non-lognormal distribution of the assets return, leverage effect, and mean-reverting property of volatility. The model of Heston-Nandi, also in closed form, successfully incorporates variance of asset returns as a range of GARCH process. It strongly permits correlation between returns of the spot asset and variance and also technically accepts multiple lags in the dynamics of the GARCH process. To decide, determine, and delineate the effectiveness of stochastic models against the Black-Scholes and market, the current paper adopts a structured approach of relative error price, viz., percentage mean error (PME) and mean absolute percentage error (MAPE). The most turbulent period of the Indian economy � January 1, 2008 to December 31, 2008 — was considered appropriate for testiing the suggested model. It was a testing time for the Indian economy as well as a critical period questioning the sustainability of all financial products/models and challenging their fundamental platform depicted as equity market. How to safeguard investors� faith and at least protect their investments if not multiply returns in the face of such financial hardships remained a burning question for all thinkers and experts on the subject. Data pertaining to the specific period of such drastic disturbance was analysed with the help of the proposed models. After rigorous churning of specific data taken across various models, the Heston model was found to outperform and surpass other models.
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Yeshiwas, Dawit, et Yebelay Berelie. « Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices : A Multivariate GARCH Approach with High-Frequency Data ». Journal of Probability and Statistics 2020 (4 avril 2020) : 1–10. http://dx.doi.org/10.1155/2020/1424020.
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Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.
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Mohammed, Tanimu, Yahaya Haruna Umar et Samuel Olorunfemi Adams. « MODELING THE VOLATILITY FOR SOME SELECTED BEVERAGES STOCK RETURNS IN NIGERIA (2012-2021) : A GARCH MODEL APPROACH ». Matrix Science Mathematic 6, no 2 (2022) : 41–51. http://dx.doi.org/10.26480/msmk.02.2022.41.51.
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The volatility of equity returns for two beverages traded on the Nigerian stock exchange is the subject of this study. The ARCH effect test demonstrated that the two beverages disprove the claim that there is no ARCH effect. According to the preliminary analysis, both beverages were volatile. CGARCH and EGARCH were chosen as the best volatility models for Guinness Nigeria Plc returns and Nigeria Breweries returns, respectively, based on model selection criteria. The EGARCH model, on the other hand, rejected the idea that Guinness Nigeria Plc’s equity returns respond equally to negative and positive shocks of similar magnitude. This study’s findings suggest that the government should be cautious about how it manages inflation and foreign direct investment because they affect the rising stock price. Financial stability will likely be a more direct and explicit part of the macroeconomic responsibilities of central banks in the coming years.
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Benada, Luděk. « Comparison of the Impact of Econometric Models on Hedging Performance by Crude Oil and Natural Gas ». Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no 2 (2018) : 423–29. http://dx.doi.org/10.11118/actaun201866020423.
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The paper examines the performance of hedging spot prices in crude oil and natural gas. The subject of the research are spot prices of West Texas Intermediate and Henry Hub. The risk protection is provided by the application of futures contracts of underlying assets. In our analysis three econometric models (OLS, Copula, GARCH) and a naive portfolio are applied to obtain the optimal hedge ratio. Afterwards, the calculated weights for futures are verified for the ability to reduce the spot price risk over twelve months. The success of each model in risk reduction is measured over the test period by a conventional tool and across the models by proper metric. The results of the analysis confirm high level of risk reduction by crude oil across models. On the contrary, the results of hedging in natural gas significantly lag in comparison to crude oil. In addition, the analysis confirms a strong variability over the tested period and models.
Thèses sur le sujet "Subject GARCH model"
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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 2012Examining 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.
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. Granger causality in conditional means and conditional variances of time series is investigated in the framework of several popular multivariate econometric models. Bayesian inference is proposed as a method of assessment of the hypotheses of Granger noncausality. First, the family of ECCC-GARCH models is used in order to perform inference about Granger-causal relations in second conditional moments. The restrictions for second-order Granger noncausality between two vectors of variables are derived. Further, in order to investigate Granger causality in conditional mean and conditional variances of time series VARMA-GARCH models are employed. Parametric restrictions for the hypothesis of noncausality in conditional variances between two groups of variables, when there are other variables in the system as well are derived. These novel conditions are convenient for the analysis of potentially large systems of economic variables. Bayesian testing procedures applied to these two problems, Bayes factors and a Lindley-type test, make the testing possible regardless of the form of the restrictions on the parameters of the model. This approach also enables the assumptions about the existence of higher-order moments of the processes required by classical tests to be relaxed. Finally, a method of testing restrictions for Granger noncausality in mean, variance and distribution in the framework of Markov-switching VAR models is proposed. Due to the nonlinearity of the restrictions derived by Warne (2000), classical tests have limited use. Bayesian inference consists of a novel Block Metropolis-Hastings sampling algorithm for the estimation of the restricted models, and of standard methods of computing posterior odds ratios. The analysis may be applied to financial and macroeconomic time series with changes of parameter values over time and heteroskedasticity.
Chapitres de livres sur le sujet "Subject GARCH model"
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Al Janabi, Mazin A. M. « Evaluation of Optimum and Coherent Economic-Capital Portfolios Under Complex Market Prospects ». Dans Handbook of Research on Big Data Clustering and Machine Learning, 214–30. 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 GARCH-M technique to forecast conditional volatility and expected return. Moreover, in this chapter, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm, which allocates both economic-capital and trading assets by minimizing L-VaR objective function. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on the L-VaR optimization procedure.
Actes de conférences sur le sujet "Subject GARCH model"
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Gerni, Cevat, Özge Buzdağlı, Dilek Özdemir et Ömer Selçuk Emsen. « Elections and The Real Exchange Rate Volatility In Turkey (1992-2014) ». Dans 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 rate volatility is examined using E-GARCH method. On the other hand, political variables such as independence of Central Bank, exchange rate regime, the number of representatives of the ruling party in the parliament and coalition are included to the model while the pre and after election period from the 1st to the 6th month as dummy variables. Based on the results of the analysis, it can be said that the elections and the political variables affect the real exchange rate and its volatility in Turkey. However, there is no significant evidence whether the politicians act opportunistic behavior to be reelected. Since the uncertainty during the election period cause outflow of the capital and deferral of the investment decisions of the investors until after the election, it may well be said that the politicians fail to influence the real exchange rate for their self-interests.