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Journal articles on the topic 'Multivariate conditional autoregressive model'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Fernandes, Marcelo, Bernardo de Sá Mota, and Guilherme Rocha. "A multivariate conditional autoregressive range model." Economics Letters 86, no. 3 (2005): 435–40. http://dx.doi.org/10.1016/j.econlet.2004.09.005.

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2

McAleer, Michael, Felix Chan, Suhejla Hoti, and Offer Lieberman. "GENERALIZED AUTOREGRESSIVE CONDITIONAL CORRELATION." Econometric Theory 24, no. 6 (2008): 1554–83. http://dx.doi.org/10.1017/s0266466608080614.

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This paper develops a generalized autoregressive conditional correlation (GARCC) model when the standardized residuals follow a random coefficient vector autoregressive process. As a multivariate generalization of the Tsay (1987, Journal of the American Statistical Association 82, 590–604) random coefficient autoregressive (RCA) model, the GARCC model provides a motivation for the conditional correlations to be time varying. GARCC is also more general than the Engle (2002, Journal of Business & Economic Statistics 20, 339–350) dynamic conditional correlation (DCC) and the Tse and Tsui (200

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3

Liang, Ye. "Graph-based multivariate conditional autoregressive models." Statistical Theory and Related Fields 3, no. 2 (2019): 158–69. http://dx.doi.org/10.1080/24754269.2019.1666242.

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Yu, Philip L. H., W. K. Li, and F. C. Ng. "The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility." Journal of Business & Economic Statistics 35, no. 4 (2017): 513–27. http://dx.doi.org/10.1080/07350015.2015.1096788.

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Yip, Iris W. H., and Mike K. P. So. "Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model." Mathematics and Computers in Simulation 80, no. 2 (2009): 327–40. http://dx.doi.org/10.1016/j.matcom.2009.07.001.

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6

Golosnoy, Vasyl, Bastian Gribisch, and Roman Liesenfeld. "The conditional autoregressive Wishart model for multivariate stock market volatility." Journal of Econometrics 167, no. 1 (2012): 211–23. http://dx.doi.org/10.1016/j.jeconom.2011.11.004.

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7

Silvennoinen, A., and T. Terasvirta. "Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model." Journal of Financial Econometrics 7, no. 4 (2009): 373–411. http://dx.doi.org/10.1093/jjfinec/nbp013.

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8

Sung, Sang-Ha, Jong-Min Kim, Byung-Kwon Park, and Sangjin Kim. "A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model." Axioms 11, no. 9 (2022): 448. http://dx.doi.org/10.3390/axioms11090448.

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Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the selection of the most influential major features for each cryptocurrency using the volatility features of cryptocurrency, derived from the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models, along with the closing price of the cryptocurrency. In addition, we sought

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Tse, Y. K., and Albert K. C. Tsui. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model With Time-Varying Correlations." Journal of Business & Economic Statistics 20, no. 3 (2002): 351–62. http://dx.doi.org/10.1198/073500102288618496.

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Calderon, Sergio, and Fabio H. Nieto. "Forecasting with Multivariate Threshold Autoregressive Models." Revista Colombiana de Estadística 44, no. 2 (2021): 369–83. http://dx.doi.org/10.15446/rce.v44n2.91356.

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   An important stage in the analysis of time series is the forecasting. How- ever, the forecasting in non-linear time series models is not straightforward as in linear time series models because an exact analytical of the conditional expectation is not easy to obtain. Therefore, a strategy of forecasting with multivariate threshold autoregressive(MTAR) models is proposed via predictive distributions through Bayesian approach. This strategy gives us the forecast for the response and exogenous vectors. The coverage percentages of the forecast intervals and the variability of t

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11

Jiang, Cui Xia, Qi Fa Xu, and Shi Ying Zhang. "Multivariate Conditional Copula-GARCD-JSU Model and its Application." Advanced Materials Research 452-453 (January 2012): 997–1001. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.997.

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Generalized autoregressive conditional density model provides a useful tool for simulating the probability density function of financial asset return. It is essential for describing the dynamic character of financial asset return comprehensively. Copula function can be used to combine some marginal distributions together. Based on Copula function, multivariate conditional Copula-GARCD-JSU model, a new model, is proposed in the paper. Fortunately, the famous “dimension disaster” in multivariate GARCD model can be overcome by the new model. Furthermore, the estimation and test method for the mod

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12

Gelfand, A. E. "Proper multivariate conditional autoregressive models for spatial data analysis." Biostatistics 4, no. 1 (2003): 11–15. http://dx.doi.org/10.1093/biostatistics/4.1.11.

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13

Asai, Manabu, and Michael McAleer. "Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes." International Journal of Statistics and Probability 6, no. 6 (2017): 13. http://dx.doi.org/10.5539/ijsp.v6n6p13.

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The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop

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Shapovalova, Yuliya, Nalan Baştürk, and Michael Eichler. "Multivariate Count Data Models for Time Series Forecasting." Entropy 23, no. 6 (2021): 718. http://dx.doi.org/10.3390/e23060718.

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Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from these classes: the log-linear multivariate conditional intensity model (also referred to as an integer-valued generalized autoregressive conditional heteroskedastic model) and the non-linear state-space model for count data. We compare these models in terms of forecasting performance on simulated data and two real datasets. In simulations, we

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Sofro, A. "Analysis Dengue Fever and Malaria Cases using Generalized Multivariate Conditional Autoregressive Model." Journal of Physics: Conference Series 1108 (November 2018): 012065. http://dx.doi.org/10.1088/1742-6596/1108/1/012065.

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16

Li, W. K., S. Ling, and H. Wong. "Estimation for partially nonstationary multivariate autoregressive models with conditional heteroscedasticity." Biometrika 88, no. 4 (2001): 1135–52. http://dx.doi.org/10.1093/biomet/88.4.1135.

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17

Singh, Amanjot, and Manjit Singh. "Conditional co-movement and dynamic interactions: US and BRIC equity markets." Ekonomski anali 62, no. 212 (2017): 85–111. http://dx.doi.org/10.2298/eka1712085s.

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The present study attempts to capture conditional or time-varying co-movement and dynamic interactions between the US and BRIC (Brazil, Russia, India, and China) equity markets across the sample period 2004 to 2014 by employing diverse econometric models. The sample period is further divided into three different sub-periods concerning the US financial crisis period, viz. pre-crisis, crisis, and post-crisis periods. The vector autoregression- dynamic conditional correlation-multivariate asymmetric generalized autoregressive conditional heteroskedastic [VAR-DCC-MVAGARCH (1.1)] model and Toda-Yam

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18

MacNab, Ying C. "On Gaussian Markov random fields and Bayesian disease mapping." Statistical Methods in Medical Research 20, no. 1 (2010): 49–68. http://dx.doi.org/10.1177/0962280210371561.

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We discuss the nature of Gaussian Markov random fields (GMRFs) as they are typically formulated via full conditionals, also named conditional autoregressive or CAR formulations, to represent small area relative risks ensemble priors within a Bayesian hierarchical model framework for statistical inference in disease mapping and spatial regression. We present a partial review on GMRF/CAR and multivariate GMRF prior formulations in univariate and multivariate disease mapping models and communicate insights into various prior characteristics for representing disease risks variability and ‘spatial

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19

L., Wiri, and Archibong M.E. "Symmetry and Asymmetry Multivariate Garch Modeling of Consumer Prices Index, Crude Oil Price, Inflation Rate and Exchange Rate." African Journal of Mathematics and Statistics Studies 6, no. 4 (2023): 68–76. http://dx.doi.org/10.52589/ajmss-6iylhm4z.

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The study looked at changes in Nigeria's exchange rate, inflation rate, consumer price index, and price of crude oil. Monthly data from January 2004 to December 2020 were utilized in this analysis, and they were taken from the statistical bulletin of the Central Bank of Nigeria (CBN). The data's time graphic showed the trend series' present state. For the analysis, E-view 12 statistical software was employed. Modeling employed both symmetric and asymmetric processes. Using both symmetric and asymmetric modeling techniques, the Multivariate Generalized Autoregressive Conditional Heteroscedastic

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20

Venkateswara Rao, K., D. Srilatha, D. Jagan Mohan Reddy, Venkata Subbaiah Desanamukula, and Mandefro Legesse Kejela. "Regression Based Price Prediction of Staple Food Materials Using Multivariate Models." Scientific Programming 2022 (June 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/4572064.

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Profit margins for essential foodstuffs could be a demand rising problem. There are several variables influencing currency fluctuations. For example, the various variables of commodity food prices are climate, crude prices, and so on. Forecasting the fluctuating prices of basic foodstuffs is also relevant even for the government, producers, and customers. The article will use ARCH (autoregressive conditional heteroskedasticity) to forecast the essential food market considering external conditions. The findings agree well enough with the assessment price in the industry by employing two main ap

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21

Mei, Bin, Michael Clutter, and Thomas Harris. "Modeling and forecasting pine sawtimber stumpage prices in the US South by various time series models." Canadian Journal of Forest Research 40, no. 8 (2010): 1506–16. http://dx.doi.org/10.1139/x10-087.

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Among the three timberland return drivers (biological growth, timber price, and land price), timber price remains the most unpredictable. It affects not only periodic dividends from timber sales but also timber production strategies embedded in timberland management. Using various time series techniques, this study aimed to model and forecast real pine sawtimber stumpage prices in 12 southern timber regions in the United States. Under the discrete-time framework, the univariate autoregressive integrated moving average model was established as a benchmark, whereas other multivariate time series

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22

Ben Alaya, M. A., F. Chebana, and T. B. M. J. Ouarda. "Probabilistic Multisite Statistical Downscaling for Daily Precipitation Using a Bernoulli–Generalized Pareto Multivariate Autoregressive Model." Journal of Climate 28, no. 6 (2015): 2349–64. http://dx.doi.org/10.1175/jcli-d-14-00237.1.

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Abstract A Bernoulli–generalized Pareto multivariate autoregressive (BMAR) model is proposed in this paper for multisite statistical downscaling of daily precipitation. The proposed model relies on a probabilistic framework to describe the conditional probability density function of precipitation at each station for a given day and handles multivariate dependence in both time and space using a multivariate autoregressive model. Within a probabilistic framework, BMAR employs a regression model whose outputs are parameters of the mixed Bernoulli–generalized Pareto distribution. As a stochastic c

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23

Tan, Shay Kee, Kok Haur Ng, and Jennifer So-Kuen Chan. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models." Mathematics 11, no. 1 (2022): 13. http://dx.doi.org/10.3390/math11010013.

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This paper extends the conditional autoregressive range (CARR) model to the multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain the fitted volatilities. Then covariances are calculated to construct the fitted variance–covariance matrix of returns which are imputed into the stage-two return model to capture the hete

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24

Francis Diaz, John, Peh Ying Qian, and Genevieve Liao Tan. "Variance Persistence in the Greater China Region: A Multivariate GARCH Approach." LAHORE JOURNAL OF ECONOMICS 23, no. 2 (2018): 49–68. http://dx.doi.org/10.35536/lje.2018.v23.i2.a3.

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This paper utilizes three Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) models to determine variance persistence in the Greater China region from 2009 to 2014. The first approach applies the Baba, Engle, Kraft and Kroner (BEKK) model and shows that the Shanghai Stock Exchange Composite Index (SSEI), Taiwan Capitalization Weighted Stock Index (TAEIX) and the Hang Seng Stock Index (HSEI) stock returns are all functions of their lagged covariances and lagged cross-product innovations. The second MGARCH approach applies two methodologies, namely, dynamic conditional c

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25

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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26

Diaz, John Francis T. "Volatility Dynamics in the ASEAN– China Free Trade Agreement." Journal of Emerging Market Finance 17, no. 3 (2018): 287–306. http://dx.doi.org/10.1177/0972652718797812.

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This study used three multivariate general autoregressive conditional heteroskedasticity models to analyze the volatility dynamics in the ASEAN–China Free Trade Agreement. Results indicated the presence of long-run persistence, wherein shocks in China’s stock market affect other ASEAN stock indices in the long term. Further tests revealed the presence of time-varying correlations, suggesting dynamic models, such as the dynamic conditional correlations model, are appropriate. The Baba, Engle, Kraft, and Kroner model determined that the conditional covariances of the Chinese and ASEAN indices ar

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27

Ngatchou-Wandji, Joseph, Marwa Ltaifa, Didier Alain Njamen Njomen, and Jia Shen. "Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models." Mathematics 10, no. 4 (2022): 624. http://dx.doi.org/10.3390/math10040624.

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This work is concerned with multivariate conditional heteroscedastic autoregressive nonlinear (CHARN) models with an unknown conditional mean function, conditional variance matrix function and density function of the distribution of noise. We study the kernel estimator of the latter function when the former are either parametric or nonparametric. The consistency, bias and asymptotic normality of the estimator are investigated. Confidence bound curves are given. A simulation experiment is performed to evaluate the performance of the results.

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28

Maanan, Saïd, Bogdan Dumitrescu, and Ciprian Doru Giurcăneanu. "Conditional independence graphs for multivariate autoregressive models by convex optimization: Efficient algorithms." Signal Processing 133 (April 2017): 122–34. http://dx.doi.org/10.1016/j.sigpro.2016.10.023.

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29

Berdinazarov, Zafar, Khasanjon Dodoev, Jamshid Mamasalaev, and Jakhongirmirzo Fakhodjonov. "Determinants of Exchange Rate Fluctuations of Uzbek Sum." Business and Management Studies 5, no. 1 (2019): 52. http://dx.doi.org/10.11114/bms.v5i1.4162.

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This paper examines the determinants of exchange rate fluctuations of Uzbek sum by using three econometric models OLS (Ordinary Least Squares), ARIMA (Autoregressive Integrated Moving Average) and ML ARCH (Multivariate Long memory Autoregressive Conditional Heteroskadasticity). Model results show that the effects of money supply and remittances to the nominal and real exchange rates (USD/UZS) are found statistically significant; the impacts of inflation and interest rate are not econometrically meaningful. Also, it should be noted that the level of net trade influences to the exchange rate is

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30

Cui, Haipeng, Kun Xie, Bin Hu, Hangfei Lin, and Rui Zhang. "Analysis of Bus Speed Using a Multivariate Conditional Autoregressive Model: Contributing Factors and Spatiotemporal Correlation." Journal of Transportation Engineering, Part A: Systems 145, no. 4 (2019): 04019009. http://dx.doi.org/10.1061/jtepbs.0000226.

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31

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

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

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32

Broda, Simon, and Marc S. Paolella. "ARCHModels.jl: Estimating ARCH Models in Julia." Journal of Statistical Software 107, no. 5 (2023): 1–25. https://doi.org/10.5281/zenodo.10682941.

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This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models. This model class is the workhorse tool for modeling the conditional volatility of financial assets. The distinguishing feature of these models is that they model the latent volatility as a (deterministic) function of past returns and volatilities. This recursive structure results in loop-heavy code which, due to its just-in-time compiler, Julia is well-equipped to handle. As such, the entire package is wr

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33

Bauer, Dietmar. "USING SUBSPACE METHODS FOR ESTIMATING ARMA MODELS FOR MULTIVARIATE TIME SERIES WITH CONDITIONALLY HETEROSKEDASTIC INNOVATIONS." Econometric Theory 24, no. 4 (2008): 1063–92. http://dx.doi.org/10.1017/s0266466608080419.

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This paper deals with the estimation of linear dynamic models of the autoregressive moving average type for the conditional mean for stationary time series with conditionally heteroskedastic innovation process. Estimation is performed using a particular class of subspace methods that are known to have computational advantages as compared to estimation based on criterion minimization. These advantages are especially strong for high-dimensional time series. Conditions to ensure consistency and asymptotic normality of the subspace estimators are derived in this paper. Moreover asymptotic equivale

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Feng, Shibo, Chunyan Miao, Zhong Zhang, and Peilin Zhao. "Latent Diffusion Transformer for Probabilistic Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 11979–87. http://dx.doi.org/10.1609/aaai.v38i11.29085.

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The probability prediction of multivariate time series is a notoriously challenging but practical task. This research proposes to condense high-dimensional multivariate time series forecasting into a problem of latent space time series generation, to improve the expressiveness of each timestamp and make forecasting more manageable. To solve the problem that the existing work is hard to extend to high-dimensional multivariate time series, we present a latent multivariate time series diffusion framework called Latent Diffusion Transformer (LDT), which consists of a symmetric statistics-aware aut

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35

Umar, Saminu, and Gafar M. Oyeyemi. "A Hybrid LSTM-DCC Model for Multivariate Cryptocurrency Volatility Prediction." Asian Journal of Probability and Statistics 27, no. 7 (2025): 179–91. https://doi.org/10.9734/ajpas/2025/v27i7784.

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Accurate volatility forecasting remains a central challenge in the analysis of cryptocurrency markets, where extreme price fluctuations, nonlinear dependencies and evolving cross-asset correlations complicate traditional modeling approaches. This study proposes a hybrid framework that integrates the Dynamic Conditional Correlation (DCC) Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) model with Long Short-Term Memory (LSTM) networks to enhance forecasting accuracy. The LSTM–DCC model improves the representation of volatility clustering, structural breaks and int

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36

Wang, Yiyi, and Kara M. Kockelman. "A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods." Accident Analysis & Prevention 60 (November 2013): 71–84. http://dx.doi.org/10.1016/j.aap.2013.07.030.

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A.E., Usoro, and Ekong A. "Modeling Nigeria Crude Oil Production and Price Volatility Using Multivariate Generalized Autoregressive Conditional Heteroscedasticity Models." African Journal of Mathematics and Statistics Studies 5, no. 1 (2022): 33–54. http://dx.doi.org/10.52589/ajmss-l4fi9dw6.

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Modelling of Nigeria's Crude Oil Production and Price Volatilities was the major focus of this paper. The paper investigated the stationarity of the multivariate time series positive definiteness property, and the results revealed the stationarity of the multivariate time series. Special classes of MARCH and MGARCH models were fitted to the crude oil price and production volatilities, and MARCH [p (3,1)] outperformed other models with the aid of model selection criteria. The research has established interaction and interdependence between the two macroeconomic variables and has also revealed b

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A.O., Oyedepo, Adegbite I.O., Omisore A.O., and Babatola B.K. "Multivariate Volatility Modeling of Nigerian Bank Share Prices." African Journal of Mathematics and Statistics Studies 5, no. 2 (2022): 10–18. http://dx.doi.org/10.52589/ajmss-jxr5zpfr.

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This study aims at finding the optimal Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model among Diag-BEKK, Scalar-BEKK and CCC that captures the dynamics of returns in Nigerian bank share prices, using the data of daily share prices of two highly capitalized banks in Nigeria listed on the platform of Nigerian Stock Exchange (NSE) which span from 2nd January, 2009 to 28th December, 2019. Multivariate Normal and Multivariate student-t log-likelihood functions were simplified using the BHHH and Marquardt algorithm and the optimal solution was obtained using the

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Kumar, Surender, Moon MoonHaque, and Prashant Sharma. "Volatility Spillovers across Major Emerging Stock Markets." Asia-Pacific Journal of Management Research and Innovation 13, no. 1-2 (2017): 13–33. http://dx.doi.org/10.1177/2319510x17740043.

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Emerging stock markets of Asia have become a matter of interest for international financial researchers and policy-makers during the last couple of decades. Series of reforms, increasing financial transparency and decreasing restrictions on transactions have made these markets better diversification opportunities for international investors. This paper examines independently as well the linkages of stock markets across the selected Asian countries. The volatility spillover is modelled through an asymmetric multivariate generalized autoregressive conditional heteroscedastic model. In large numb

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40

Liang, Menglu, Zheng Li, Lijun Zhang, and Ming Wang. "A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data." Stats 7, no. 4 (2024): 1379–91. http://dx.doi.org/10.3390/stats7040080.

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Epidemiological approaches for examining human health responses to environmental exposures in observational studies frequently address confounding by employing advanced matching techniques and statistical methods grounded in conditional likelihood. This study incorporates a recently developed Bayesian hierarchical spatiotemporal model within a conditional logistic regression framework to capture the heterogeneous effects of environmental exposures in a case-crossover (CCO) design. Spatial and temporal dependencies are modeled through random effects incorporating multivariate conditional autore

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Le Fur, Eric, Hachmi Ben Ameur, and Benoit Faye. "Time-Varying Risk Premiums in the Framework of Wine Investment." Journal of Wine Economics 11, no. 3 (2016): 355–78. http://dx.doi.org/10.1017/jwe.2016.15.

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AbstractThis article examines the time-varying risk premium with reference to investments in fine wines. Unlike previous studies, our article focuses on this issue within the context of the financial crisis. To do this, we propose the use of a conditional capital asset pricing model and a multivariate generalized autoregressive conditional heteroskedasticity model on several appellation wines worldwide. We find that Bordeaux fine wines were more volatile during the financial crisis and are less volatile in non-crisis periods. In addition, while the volatility of Burgundy wines is second only t

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42

Rahmat Widianto and Setyo Riyanto. "Analisis Univariat Dan Multivariat Pada Perusahaan Pt Ace Hardware Indonesia Tbk Dan Pt Ekadharma International Tbk." Jurnal Syntax Transformation 1, no. 7 (2020): 359–65. http://dx.doi.org/10.46799/jst.v1i7.96.

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Penelitian ini bertujuan untuk mengetahui perubahan harga saham jangka pendek pada PT. Ace Hardware Indonesia Tbk. Dan PT. Ekadharma International Tbk. khususnya perubahan harga saham harian, memerlukan metode, model, atau pendekatan yang harus diuji keakuratannya. Ada beberapa model dalam analisis peramalan, antara lain Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARARCH), Autoregressive Integrated Moving Average (ARIMA), Autoregressive Conditional Heteroscedasticity (ARCH) Generalized Autoregressive Conditional Heteroscedasticity (GARCH)). Berdasarkan hasil analis

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KUMAR, K. KIRAN, and SHREYA BOSE. "HEDGING EFFECTIVENESS OF CROSS-LISTED NIFTY INDEX FUTURES." Global Economy Journal 19, no. 02 (2019): 1950011. http://dx.doi.org/10.1142/s2194565919500118.

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This paper investigates the hedging effectiveness of cross-listed Nifty Index futures and compares the performance of constant and dynamic optimal hedging strategies. We use daily data of Nifty index traded on the National Stock Exchange (NSE), India and cross-listed Nifty futures traded on the Singapore Stock Exchange (SGX) for a period of six years from July 15, 2010 to July 15, 2016. Various competing forms of Multivariate Generalised Autoregressive Conditional Heteroscedasticity (MGARCH) models, such as Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation (DCC), have

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Monika, Putri, Budi Nurani Ruchjana, and Atje Setiawan Abdullah. "The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city." International Journal of Data and Network Science 6, no. 4 (2022): 1309–18. http://dx.doi.org/10.5267/j.ijdns.2022.6.004.

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A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated

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Jain, Sonali. "Betas in the time of corona: a conditional CAPM approach using multivariate GARCH model for India." Managerial Finance 48, no. 2 (2021): 243–57. http://dx.doi.org/10.1108/mf-05-2021-0226.

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PurposeThis paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.Design/methodology/approachA conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves

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Katusiime, Lorna. "Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda." Economies 7, no. 1 (2018): 1. http://dx.doi.org/10.3390/economies7010001.

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This study investigates the impact of commodity price volatility spillovers on financial sector stability. Specifically, the study investigates the spillover effects between oil and food price volatility and the volatility of a key macroeconomic indicator of importance to financial stability: the nominal Uganda shilling per United States dollar (UGX/USD) exchange rate. Volatility spillover is examined using the Generalized Vector Autoregressive (GVAR) approach and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) techniques, namely the dynamic conditional correlat

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WU, EDMOND H. C., PHILIP L. H. YU, and W. K. LI. "VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS." International Journal of Neural Systems 16, no. 05 (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 esti

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Ba, Xuezhen, Xizhao Wang, and Yu Zhong. "The Impact of Federal Reserve Monetary Policy on Commodity Prices: Evidence from the U.S. Dollar Index and International Grain Futures and Spot Markets." Agriculture 15, no. 9 (2025): 923. https://doi.org/10.3390/agriculture15090923.

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There is a strong connection between the Federal Reserve’s monetary policy and the trend of international food prices. Employing the average information share model, EGARCH(Exponential Generalized Autoregressive Conditional Heteroskedasticity), and DCC-MGARCH(Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models, this study investigates the relationship between the U.S. dollar index, international grain futures prices, and spot prices in the context of Federal Reserve monetary policy adjustments from 2000 to 2023. The findings reveal tha

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Singh, Amanjot, and Manjit Singh. "Cross country co-movement in equity markets after the US financial crisis." Journal of Indian Business Research 8, no. 2 (2016): 98–121. http://dx.doi.org/10.1108/jibr-08-2015-0089.

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Purpose This paper aims to attempt to capture the co-movement of the Indian equity market with some of the major economic giants such as the USA, Europe, Japan and China after the occurrence of global financial crisis in a multivariate framework. Apart from these cross-country co-movements, the study also captures an intertemporal risk-return relationship in the Indian equity market, considering the covariance of the Indian equity market with the other countries as well. Design/methodology/approach To account for dynamic correlation coefficients and risk-return dynamics, vector autoregressive

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Joyo, Ahmed Shafique, and Lin Lefen. "Stock Market Integration of Pakistan with Its Trading Partners: A Multivariate DCC-GARCH Model Approach." Sustainability 11, no. 2 (2019): 303. http://dx.doi.org/10.3390/su11020303.

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A decade after the global financial crisis, the developments in stock market integration have increased the stability and liquidity of markets, and decreased the diversification benefits for investors. International trade is an important determinant of stock market interdependence. The objective of this study is to analyze the co-movements and the portfolio diversification between the stock markets of Pakistan and its top trading partners, namely China, Indonesia, Malaysia, the United Kingdom, and the United States. We employed Dynamic Conditional Covariance (DCC)-Generalized Autoregressive Co

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