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Relevant bibliographies by topics / Multivariate conditional autoregressive model

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Academic literature on the topic 'Multivariate conditional autoregressive model'

Author: Grafiati

Published: 3 June 2025

Last updated: 13 July 2025

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Journal articles on the topic "Multivariate conditional autoregressive model"

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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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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 (2002, Journal of Business & Economic Statistics 20, 351–362) varying conditional correlation (VCC) models and does not impose unduly restrictive conditions on the parameters of the DCC model. The structural properties of the GARCC model, specifically, the analytical forms of the regularity conditions, are derived, and the asymptotic theory is established. The Baba, Engle, Kraft, and Kroner (BEKK) model of Engle and Kroner (1995, Econometric Theory 11, 122–150) is demonstrated to be a special case of a multivariate RCA process. A likelihood ratio test is proposed for several special cases of GARCC. The empirical usefulness of GARCC and the practicality of the likelihood ratio test are demonstrated for the daily returns of the Standard and Poor's 500, Nikkei, and Hang Seng indexes.

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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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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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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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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 to predict the log-return price of cryptocurrencies by implementing various types of time-series model. Based on the selected major features, the log-return price of cryptocurrency was predicted through the autoregressive integrated moving average (ARIMA) time-series prediction model and the artificial neural network-based time-series prediction model. As a result of log-return price prediction, the neural-network-based time-series prediction models showed superior predictive power compared to the traditional time-series prediction model.

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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 the predictive distributions are analysed in this work. An application to Hydrology is presented.         

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Dissertations / Theses on the topic "Multivariate conditional autoregressive model"

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Silvennoinen, Annastiina. "Essays on autoregressive conditional heteroskedasticity." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (EFI), 2006. http://www2.hhs.se/EFI/summary/711.htm.

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RAPOSO, GUSTAVO SANTOS. "HIGH FREQUENCY DATA AND PRICE-MAKING PROCESS ANALYSIS: THE EXPONENTIAL MULTIVARIATE AUTOREGRESSIVE CONDITIONAL MODEL - EMACM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8620@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A modelagem de dados que qualificam as transações de ativos financeiros, tais como, preço, spread de compra e venda, volume e duração, vem despertando o interesse de pesquisadores na área de finanças, levando a um aumento crescente do número de publicações referentes ao tema. As primeiras propostas se limitaram aos modelos de duração. Mais tarde, o impacto da duração sobre a volatilidade instantânea foi analisado. Recentemente, Manganelli (2002) incluiu dados referentes aos volumes transacionados dentro de um modelo vetorial. Neste estudo, nós estendemos o trabalho de Manganelli através da inclusão do spread de compra e venda num modelo vetorial autoregressivo, onde as médias condicionais do spread, volume, duração e volatilidade instantânea são descritas a partir de uma formulação exponencial chamada Exponential Multivariate Autoregressive Conditional Model (EMACM). Nesta nova proposta, não se fazem necessárias a adoção de quaisquer restrições nos parâmetros do modelo, o que facilita o procedimento de estimação por máxima verossimilhança e permite a utilização de testes de Razão de Verossimilhança na especificação da forma funcional do modelo (estrutura de interdependência). Em paralelo, a questão de antecipar movimentos nos preços de ativos financeiros é analisada mediante a utilização de um procedimento integrado, no qual, além da modelagem de dados financeiros de alta freqüência, faz-se uso de um modelo probit ordenado contemporâneo. O EMACM é empregado com o objetivo de capturar a dinâmica associada às variáveis e sua função de previsão é utilizada como proxy para a informação contemporânea necessária ao modelo de previsão de preços proposto.<br>The availability of high frequency financial transaction data - price, spread, volume and duration -has contributed to the growing number of scientific articles on this topic. The first proposals were limited to pure duration models. Later, the impact of duration over instantaneous volatility was analyzed. More recently, Manganelli (2002) included volume into a vector model. In this document, we extended his work by including the bid-ask spread into the analysis through a vector autoregressive model. The conditional means of spread, volume and duration along with the volatility of returns evolve through transaction events based on an exponential formulation we called Exponential Multivariate Autoregressive Conditional Model (EMACM). In our proposal, there are no constraints on the parameters of the VAR model. This facilitates the maximum likelihood estimation of the model and allows the use of simple likelihood ratio hypothesis tests to specify the model and obtain some clues about the interdependency structure of the variables. In parallel, the problem of stock price forecasting is faced through an integrated approach in which, besides the modeling of high frequency financial data, a contemporary ordered probit model is used. Here, EMACM captures the dynamic that high frequency variables present, and its forecasting function is taken as a proxy to the contemporaneous information necessary to the pricing model.

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Oztek, Mehmet Fatih. "Modeling Co-movements Among Financial Markets: Applications Of Multivariate Autoregressive Conditional Heteroscedasticity With Smooth Transitions In Conditional Correlations." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615713/index.pdf.

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The main purpose of this thesis is to assess the potential of emerging stock markets and commodity markets in attracting the attention of international investors who utilize various portfolio diversification strategies to reduce the cumulative risk of their portfolio. A successful portfolio diversification strategy requires low correlation among financial markets. However, it is now well documented that the correlations among financial markets in developed countries are very high and hence the benefits of international portfolio diversification among these markets have been very limited. This fact suggests that investors should look for alternative markets whose correlations with developed markets are low (or even negative if possible) and which have high growth potentials. In this thesis, two emerging countries&#039<br>stock markets and two commodity markets are considered as alternative markets. Among emerging countries, Turkey and China are chosen due to their promising growth performance since the mid-2000s. As commodity markets, agricultural commodity and precious metal markets are selected because of the outstanding performance of the former and the &quot<br>safe harbor&quot<br>property of the latter. The structures and properties of dependence between these markets and stock markets in developed countries are examined by modeling the conditional correlation in the dynamic conditional correlation framework. The results reveal that upward trend hypothesis is valid for almost all correlations among market pairs and market volatility plays significant role in time varying structures of correlations.

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Liu, Yingying. "Bayesian hierarchical normal intrinsic conditional autoregressive model for stream networks." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6606.

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Water quality and river/stream ecosystems are important for all living creatures. To protect human health, aquatic life and the surrounding ecosystem, a considerable amount of time and money has been spent on sampling and monitoring streams and rivers. Water quality monitoring and analysis can help researchers predict and learn from natural processes in the environment and determine human impacts on an ecosystem. Measurements such as temperature, pH, nitrogen concentration, algae and fish count collected along the network are all important factors in water quality analysis. The main purposes of the statistical analysis in this thesis are (1) to assess the relationship between the variable measured in the water (response variable) and other variables that describe either the locations on/along the stream network or certain characteristics at each location (explanatory variable), and (2) to assess the degree of similarity between the response variable values measured at different locations of the stream, i.e. spatial dependence structure. It is commonly accepted that measurements taken at two locations close to each other should have more similarity than locations far away. However, this is not always true for observations from stream networks. Observations from two sites that do not share water flow could be independent of each other even if they are very close in terms of stream distance, especially those observations taken on objects that move passively with the water flow. To model stream network data correctly, it is important to quantify the strength of association between observations from sites that do not share water.

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Somal, Harsimran S. "Heterogeneous computing for the Bayesian hierarchical normal intrinsic conditional autoregressive model with incomplete data." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2145.

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A popular model for spatial association is the conditional autoregressive (CAR) model, and generalizations exist in the literature that utilize intrinsic CAR (ICAR) models within spatial hierarchical models. One generalization is the class of Bayesian hierarchical normal ICAR models, abbreviated HNICAR. The Bayesian HNICAR model can be used to smooth areal or lattice data, estimate the directional strength of spatio-temporal associations, and make posterior predictions at each point in space or time. Furthermore, the Bayesian HNICAR model allows for sample-based posterior inference about model parameters and predictions. R package CARrampsOcl enables fast, independent sampling-based inference for a Bayesian HNICAR model when data are complete and the spatial precision matrix is expressible as a Kronecker sum of lower order matrices. This thesis presents an independent sampling algorithm to accommodate incomplete data and arbitrary precision structures, a parallelized implementation of the algorithm that can be executed on a wide range of hardware, including NVIDIA and AMD graphical processing units (GPUs) and multicore Intel CPUs, analysis of the effects of missingness on the posterior distribution of model parameters and predictive densities, and a survey of model comparison methods for CAR models. The merits of the model and algorithm are demonstrated through both simulation and analysis of an environmental data set.

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Cho, Jang Hyung. "An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/60.

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We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

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Lu, Cheng. "A new approach to calculate and forecast dynamic conditional correlation : the use of a multivariate heteroskedastic mixture model." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/172585/.

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Much research in finance has been directed towards forecasting time varying volatility of unidimensional macroeconomic variables such as stock index, exchange rate and interest rate. However, comparatively little is devoted to modelling time varying correlation. In this research, we extend the current literature on correlation modelling by reviewing existing time-series tools, performing empirical analysis and developing two new conditional heteroscedastic models based on mixture techniques. Specifically, Engle’s standard DCC is augmented with an asymmetric factor and then modified so that disturbances (conditional returns) can be modelled using multivariate Gaussian mixture distribution and multivariate T mixture distribution. A key motivation of proposing mixture models is to account for the bi-modality observed in unconditional distribution of realized correlation. Besides, the ultimate purpose of incorporating this assumption to a multivariate GARCH is to account for a variety of stylized features frequently presented in financial returns such as volatility clustering, correlation clustering, leverage effect, fat tails, skewness and leptokurtosis. Since the model flexibility given this assumption can be greatly enhanced, after a thorough comparison we find significant evidence of outperformance of our models over other alternative models from a range of perspectives. Besides, in this research we also study a new type of correlation model using multivariate skew-t as basis for quantifying the density values of conditional returns. Note that, the ADCC skew-t and AGDCC skew-t model analyzed in this research are both new to the financial literature

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Nakatani, Tomoaki. "Four Essays on Building Conditional Correlation GARCH Models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-952.

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This thesis consists of four research papers. The main focus is on building the multivariate Conditional Correlation (CC-) GARCH models. In particular, emphasis lies on considering an extension of CC-GARCH models that allow for interactions or causality in conditional variances. In the first three chapters, misspecification testing and parameter restrictions in these models are discussed. In the final chapter, a computer package for building major variants of the CC-GARCH models is presented. The first chapter contains a brief introduction to the CC-GARCH models as well as a summary of each research paper. The second chapter proposes a misspecification test for modelling of the conditional variance part of the Extended Constant CC-GARCH model. The test is designed for testing the hypothesis of no interactions in the conditional variances. If the null hypothesis is true, then the conditional variances may be described by the standard CCC-GARCH model. The test is constructed on the Lagrange Multiplier (LM) principle that only requires the estimation of the null model. Although the test is derived under the assumption of the constant conditional correlation, the simulation experiments suggest that the test is also applicable to building CC-GARCH models with changing conditional correlations. There is no asymptotic theory available for these models, which is why simulation of the test statistic in this situation has been necessary. The third chapter provides yet another misspecification test for modelling of the conditional variance component of the CC-GARCH models, whose parameters are often estimated in two steps. The estimator obtained through these two steps is a two-stage quasi-maximum likelihood estimator (2SQMLE). Taking advantage of the asymptotic results for 2SQMLE, the test considered in this chapter is formulated using the LM principle, which requires only the estimation of univariate GARCH models. It is also shown that the test statistic may be computed by using an auxiliary regression. A robust version of the new test is available through another auxiliary regression. All of this amounts to a substantial simplification in computations compared with the test proposed in the second chapter. The simulation experiments show that, under both under both Gaussian and leptokurtic innovations, as well as under changing conditional correlations, the new test has reasonable size and power properties. When modelling the conditional variance, it is necessary to keep the sequence of conditional covariance matrices positive definite almost surely for any time horizon. In the fourth chapter it is demonstrated that under certain conditions some of the parameters of the model can take negative values while the conditional covariance matrix remains positive definite almost surely. It is also shown that even in the simplest first-order vector GARCH representation, the relevant parameter space can contain negative values for some parameters, which is not possible in the univariate model. This finding makes it possible to incorporate negative volatility spillovers into the CC-GARCH framework. Many new GARCH models and misspecification testing procedures have been recently proposed in the literature. When it comes to applying these models or tests, however, there do not seem to exist many options for the users to choose from other than creating their own computer programmes. This is especially the case when one wants to apply a multivariate GARCH model. The last chapter of the thesis offers a remedy to this situation by providing a workable environment for building CC-GARCH models. The package is open source, freely available on the Internet, and designed for use in the open source statistical environment R. With this package can estimate major variants of CC-GARCH models as well as simulate data from the CC-GARCH data generating processes with multivariate normal or Student's t innovations. In addition, the package is equipped with the necessary functions for conducting diagnostic tests such as those discussed in the third chapter of this thesis.<br><p>Diss. Stockholm : Handelshögskolan, 2010. Sammanfattning jämte 4 uppsatser.</p>

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Tong, Zhigang. "Statistical Inference for Heavy Tailed Time Series and Vectors." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35649.

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In this thesis we deal with statistical inference related to extreme value phenomena. Specifically, if X is a random vector with values in d-dimensional space, our goal is to estimate moments of ψ(X) for a suitably chosen function ψ when the magnitude of X is big. We employ the powerful tool of regular variation for random variables, random vectors and time series to formally define the limiting quantities of interests and construct the estimators. We focus on three statistical estimation problems: (i) multivariate tail estimation for regularly varying random vectors, (ii) extremogram estimation for regularly varying time series, (iii) estimation of the expected shortfall given an extreme component under a conditional extreme value model. We establish asymptotic normality of estimators for each of the estimation problems. The theoretical findings are supported by simulation studies and the estimation procedures are applied to some financial data.

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Grego, Simone. "Modelos para relacionar variáveis de solos e área basal de espécies florestais em uma área de vegetação natural." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-03122014-142123/.

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O padrão espacial de ocorrência de atributos de espécies florestais, tal como a área basal das árvores, pode fornecer informações para o entendimento da estrutura da comunidade vegetal. Uma vez que fatores ambientais podem influenciar tanto o padrão espacial de ocorrência quanto os atributos das espécies em florestas nativas. Desse modo, investigar a relação entre as características ambientais e o padrão espacial de espécies florestais pode ajudar a entender a dinâmica das florestas. Especificamente, neste trabalho, o objetivo é avaliar métodos estatísticos que permitam identificar quais atributos do solo são capazes de explicar a variação da área basal de cada espécie de árvore. A área basal foi considerada como variável resposta e como covariáveis, um grande número de atributos físicos e químicos do solo, medidos em uma malha de localizações cobrindo a área de estudo. Foram revisados e utilizados os métodos de regressão linear múltipla com método de seleção stepwise, modelos aditivos generalizados e árvores de regressão. Em uma segunda fase das análises, adicionou-se um efeito espacial aos modelos, com o intuito de verificar se havia ainda padrões na variabilidade, não capturados pelos modelos. Com isso, foram considerados os modelos autoregressivo simultâneo, condicional autoregressivo e geoestatístico. Dado o grande número de atributos do solo, as análises foram também conduzidas utilizando-se as covariáveis originais, fatores identificados em uma análise fatorial prévia dos atributos de solo. A seleção de modelos com melhor ajuste foi utilizada para identificar os atributos de solo relevantes, bem como a presença e melhor descrição de padrões espaciais. A área de estudo foi a Estação Ecológica de Assis, da Unidade de Conservação do Estado de São Paulo em parcelas permanentes, dentro do projeto \"Diversidade, Dinâmica e Conservação em Florestas do Estado de São Paulo: 40 ha de parcelas permanentes\", do programa Biota da FAPESP. As análises reportadas aqui se referem à área basal das espécies Copaifera langsdorffii, Vochysia tucanorum e Xylopia aromatica. Com os atributos de solo reduzidos e consistentemente associados à área basal, a declividade, altitude, saturação por alumínio e potássio mostraram-se relevantes para duas das espécies. Resultados obtidos mostraram a presença de um padrão na variabilidade, mesmo levando-se em consideração os efeitos das covariáveis, ou seja, os atributos do solo explicam parcialmente a variabilidade da área basal, mas existe um padrão que ocorre no espaço que não é capturado por essas covariáveis.<br>The spatial pattern of occurrenceis of forest species and their attributes, such as the basal area of trees, can provide information for understanding the structure of the vegetable community. Considering the environmental factors can influence the spatial pattern of occurrences of species in native forests and related attributes, describing relationship between environmental characteristics and spatial pattern of forest species can be associated with the dynamics of forests. The objective of the present study is to assess different statistical methods used to identify which soil attributes are associated with the basal area of each tree selected species. The basal area was considered as the response variable and the covariates are given by a large number of physical and chemical attributes of the soil, measured at a grid of locations covering the study area. The methods considered are the multiple linear regression with stepwise model selection, generalized additive models and regression trees. Spatial effects were added to the models, in order to ascertain whether there is residual spatial patterns not captured by the covariates. Thus, simultaneous autoregressive model, autoregressive conditional and geostatistical were considered. Considering the large number of soil attributes, analysis were were conducted both ways, using the original covariates, and using factors identified in a preliminar factor analysis of the soil attributes. Model selection was used to identify the relevant attributes of soil as well as the presence and better description of spatial patterns. The study area was the Ecological Station of Assis, the Conservation Unit of the State of São Paulo in permanent plots within the \"Diversity Dynamics and Conservation Forests in the State of São Paulo: 40 ha of permanent plots\" project, under the research project FAPESP biota. The analyzes reported here refer to the basal area of the species Copaifera langsdorffii, Vochysia tucanorum and Xylopia aromatica. Results differ among the considered methods reinforcing the reccomendation of considering differing modeling strategies. Covariates consistently associated with basal area are slope, altitude and aluminum saturation, potassium, relevant to at least two of the species. Results obtained showed the presence of patterns in residual variability, even taking into account the effects of covariates. The soil characteristics only partially explain the variability of the basal area and there are spatial patterns not captured by these covariates.

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Books on the topic "Multivariate conditional autoregressive model"

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Engle, R. F. Forecasting transaction rates: The autoregressive conditional duration model. National Bureau of Economic Research, 1994.

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Shi, Feng. Learn About the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in R With Data From the DJIA 30 Stock Time Series (2018). SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526487650.

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Krause, Timothy A. Pricing of Futures Contracts. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656010.003.0015.

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This chapter examines the relation between futures prices relative to the spot price of the underlying asset. Basic futures pricing is characterized by the convergence of futures and spot prices during the delivery period just before contract expiration. However, “no arbitrage” arguments that dictate the fair value of futures contracts largely determine pricing relations before expiration. Although the cost of carry model in its various forms largely determines futures prices before expiration, the chapter presents alternative explanations. Related commodity futures complexes exhibit mean-reverting behavior, as seen in commodity spread markets and other interrelated commodities. Energy commodity futures prices can be somewhat accurately modeled as a generalized autoregressive conditional heteroskedastic (GARCH) process, although whether these models provide economically significant excess returns is uncertain.

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Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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Book chapters on the topic "Multivariate conditional autoregressive model"

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Chen, Jenny K. "Generalized AutoRegressive Conditional Heteroskedasticity Model." In Financial Data Analytics with R. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003469704-8.

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Yadav, Romika, and Savita Kumari Sheoran. "Autoregressive Model for Multivariate Crime Prediction." In Asset Analytics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3643-4_23.

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Reinsel, Gregory C., and Raja P. Velu. "Reduced-Rank Regression Model With Autoregressive Errors." In Multivariate Reduced-Rank Regression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2853-8_4.

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Reinsel, Gregory C., Raja P. Velu, and Kun Chen. "Reduced-Rank Regression Model With Autoregressive Errors." In Multivariate Reduced-Rank Regression. Springer New York, 2022. http://dx.doi.org/10.1007/978-1-0716-2793-8_4.

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Prigent, Jean-Luc, Olivier Renault, and Olivier Scaillet. "An Autoregressive Conditional Binomial Option Pricing Model." In Springer Finance. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-12429-1_17.

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Ogunsakin, Ropo Ebenezer, and Ding-Geng Chen. "Bayesian Spatial Modeling of HIV Using Conditional Autoregressive Model." In Modern Biostatistical Methods for Evidence-Based Global Health Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11012-2_13.

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Ferreira, Guillermo, Jean Paul Navarrete, Luis M. Castro, and Mário de Castro. "Conditional Predictive Inference for Beta Regression Model with Autoregressive Errors." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12454-4_30.

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Yatigammana, Rasika P., S. T. Boris Choy, and Jennifer S. K. Chan. "Autoregressive Conditional Duration Model with an Extended Weibull Error Distribution." In Causal Inference in Econometrics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27284-9_5.

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Wada, Takao. "Feedback Analysis of a Living Body by a Multivariate Autoregressive Model." In The Practice of Time Series Analysis. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-2162-3_2.

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Gui, Lujun, Chuyang Ye, and Tianyi Yan. "CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72104-5_16.

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Conference papers on the topic "Multivariate conditional autoregressive model"

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Torabi, Ali, James Reilly, and Duncan MacCrimmon. "Diagnosis of schizophrenia using an extended multivariate autoregressive model for EEGs." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782941.

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Devianto, Dodi, Tiansi Ade Bora, Maiyastri, Yudiantri Asdi, Dony Permana, and Erna Tri Herdiani. "The Causality Model of Indonesia Rupiah Exchange Rates, Imports, and Exports Using Multivariate Time Series Model of Vector Autoregressive." In 2024 2nd International Symposium on Information Technology and Digital Innovation (ISITDI). IEEE, 2024. https://doi.org/10.1109/isitdi62380.2024.10796690.

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Yin, Zi Wen, and Chun Jiang. "Extreme Learning Machine-Autoregression Integrated Moving Average Composite Model." In 12th Annual International Conference on Material Science and Engineering. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-4rjyxe.

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Excessive carbon dioxide emissions are the primary factor causing global warming. Currently, models for controlling carbon dioxide emissions mainly focus on population, economy, and technology. A significant amount of research has been conducted on multivariate linear regression analysis encompassing factors such as population, GDP, and energy consumption. However, the studies examining the impact of green finance on emissions have been limited to qualitative and semi-quantitative levels, lacking in-depth and systematic research. This study establishes a composite model combining the Autoregressive Integrated Moving Average (ARIMA) model and Extreme Learning Machine (ELM) model. This composite model is employed to analyze the impact of Hubei provinces’ permanent resident population, GDP, comprehensive energy consumption, and green finance index on carbon dioxide emissions. In the ELM model, the impact of four variables-population, GDP, comprehensive energy consumption, and green finance index-on carbon dioxide emissions is investigated. Using data from 2002 to 2019, the ARIMA model is applied to predict these four variables after differencing. The ELM model's prediction of carbon dioxide emissions has a very small relative error compared to actual results. The composite mode- ELM-ARIMA model is used to analyze the province's carbon dioxide emissions from 2024 to 2030.

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Roth, John T., and Sudhakar M. Pandit. "Development of a Cutting Direction and Sensor Orientation Independent Monitoring Technique for End-Milling." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0720.

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Abstract In the authors’ previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to tri-axial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation.

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Jaisankar, R., and M. Ranjani. "A comparison of disease mapping of dengue incidences in Tamil Nadu using Bayesian conditional autoregressive model and intrinsic conditional autoregressive model." In PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS RESEARCH (ICAMR - 2019). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0017007.

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Chi Xie and Lin Yao. "Portfolio Value-at-Risk estimating on markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model." In 2012 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2012. http://dx.doi.org/10.1109/iciii.2012.6339654.

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Chen, Hao, Jie Wu, and Shan Gao. "A Study of Autoregressive Conditional Heteroscedasticity Model in Load Forecasting." In 2006 International Conference on Power System Technology. IEEE, 2006. http://dx.doi.org/10.1109/icpst.2006.321620.

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Zeng, Zehua, Chenyang Tu, Neng Gao, Cong Xue, Cunqing Ma, and Yiwei Shan. "CMVCG: Non-autoregressive Conditional Masked Live Video Comments Generation Model." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533460.

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Ommi, Yassaman, Matin Yousefabadi, Faezeh Faez, Amirmojtaba Sabour, Mahdieh Soleymani Baghshah, and Hamid R. Rabiee. "CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation." In WWW '22: The ACM Web Conference 2022. ACM, 2022. http://dx.doi.org/10.1145/3487553.3524721.

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Miao, Junhong, and Yanying Wang. "A Family of Autoregressive Conditional Duration Model under Random Environment." In 2011 4th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2011. http://dx.doi.org/10.1109/iscid.2011.129.

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Reports on the topic "Multivariate conditional autoregressive model"

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Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.

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Engle, Robert, and Jeffrey Russell. Forecasting Transaction Rates: The Autoregressive Conditional Duration Model. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/w4966.

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Martin, Julien, Kevin Moran, and Dalibor Stevanovic. Macroeconomic Impacts of a Canada-U.S. Tariff War. CIRANO, 2025. https://doi.org/10.54932/rfdn9707.

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We use a vector autoregressive (VAR) model to produce macroeconomic forecasts conditional on two trade war scenarios between the United States and Canada. The first scenario examines the impact of tariff imposition on Canadian exports to the United States, while the second scenario incorporates the effect of Canadian retaliatory tariffs on imports from the United States. Our results show that these trade tensions would have significant consequences on the Canadian economy, with notable declines in GDP and employment. The analysis highlights a more pronounced contraction when imports are also affected, emphasizing the amplifying effects of retaliatory measures. In the trade war scenario with retaliatory tariffs, the model predicts a GDP decline of 4.2%, corresponding to the loss of approximately 700,000 jobs in Canada. Furthermore, the model interprets these scenarios as negative demand shocks for the Canadian economy, leading to deflationary pressures and an adjustment of interest rates by the Bank of Canada. These results illustrate the relevance of risk scenarios in the analysis of economic shocks and their usefulness for economic policy design.

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