High-Order Multivariate Markov Chain Applied in Dow Jones and Ibovespa Indexes

Cechin, Rafaela Boeira; Corso, Leandro Luís

doi:10.1590/0101-7438.2019.039.01.0205

Cited by 6 publications

(3 citation statements)

References 18 publications

(1 reference statement)

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“…For HILLIER & LIEBERMAN (2013) and CECHIN & CORSO (2019), these transition probabilities refer to the transition from a row state to a column state. These transition probabilities provide the state's probability of what the weather will be like tomorrow, given the state of the weather today.…”

Section: Markov Chainsmentioning

confidence: 99%

Markov chains to determine the probability of climate change for planting selection in the city of Caxias do Sul

2022

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The Markov stochastic chain model and the analytical hierarchy process (AHP) were used as tools to support decision-making for the best crop-planting choice in the city of Caxias do Sul, Brazil. Temperature and precipitation information were collected from the Meteorological Database for Teaching and Research of the National Institute of Meteorology of Brazil for the period 1997-2017. The stochastic model was applied to obtain the probability of transition between a range of variations for temperature and precipitation. In the second phase of the study, an algebraic model was developed, making it possible to link the probability of the Markov chain transition matrix to the AHP judgment matrix. In the third phase, the AHP was applied as a tool to determine the most beneficial crop that could be planted for the studied city, considering the evaluated criteria: temperature, precipitation, and soil pH. The alternatives for crop planting were carrots, tomatoes, apples, and grapes. These were chosen because they are the most-planted crops in the city of Caxias do Sul. The ranking of the benefit-force results of applying the model for spring was carrots (0.297), apples (0.259), grapes (0.228), and tomatoes (0.215); for summer: grapes (0.261), tomatoes (0.261), apples (0.238), and carrots (0.230); for autumn: carrots (0.316), grapes (0.243), tomatoes (0.228), and apples (0.213); and for winter: carrots (0.327), tomatoes (0.235), apples (0.222), and grapes (0.216). Thus, it was concluded that farmers would have a better chance of success if they planted carrots during the spring, autumn, and winter, and grapes during the summer.

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Section: Markov Chainsmentioning

confidence: 99%

Markov chains to determine the probability of climate change for planting selection in the city of Caxias do Sul

2022

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“…Also, its applications have been paid attention to from some recent publications (cf. Cechin & Corso, 2019 ; Oyieke & Inambao, 2019 ). Although the mentioned HOM-MC model has captured a considerably lower number of parameters, , than those of the full parameterized model, it has focused only on the positive correlations among the chains (so here denoted as ).…”

Section: Introductionmentioning

confidence: 99%

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak

Elshehawey

Qian

2023

J. Korean Stat. Soc.

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We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order for chains consisting of possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, , remarkably lower than required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial–temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.

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“…Um dos métodos probabilísticos que vem sendo largamente usado para prever o movimento do preço das ações é o das cadeias de Markov, conforme podemos ver em [2,3,5,8,11,14].…”

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Determinando a ordem das cadeias de Markov usadas na modelagem do mercado de ações

Pereira¹,

Smith²,

Lopes³

et al. 2021

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. Em muitos trabalhos sobre aplicação de cadeias de Markov no mercado de ações, a modelagem é feita via cadeias de primeira ordem. Muitos critérios de Informação (AIC, BIC, EDC, etc) foram desenvolvidos e dentre suas diversas aplicações uma delas é: Dado um conjunto de dados provenientes de uma cadeia de Markov, qual a ordem da cadeia de Markov que se ajusta melhor a esses dados? Os trabalhos analisados não se utilizaram de nenhum critério de seleção na determinação da ordem das cadeias utilizadas, e esse trabalho visa completar essa lacuna no estudo da aplicação das cadeias de Markov no mercado de ações.Palavras-chave. Processos Estocásticos, Cadeias de Markov, Critérios de Informação. IntroduçãoEm [2] é dito que as bolsas de valores são as instituições onde compra-se e/ou vende-se ações de forma sistematizada e que o único objetivo dos compradores de ações é obter lucro pelo aumento do valor das ações, entretanto existem vários diferentes motivos que podem afetar o desempenho das ações como: A tendência dos negócios à nível mundial, calamidades naturais, situação políticaeconômica, governança corporativa fraca, mudança nas políticas do governo, etc. [3] complementa que tais fatores induzem volatilidade aos preços. Em [4] acrescenta-se que a volatilidade de longo prazo está relacionada à fundamentos macroeconômicos associados com o fluxo de moeda e dos juros, enquanto a volatilidade de curto prazo é relativa a determinantes transitórios, tais como o sentimento do investidor.Existem, então, vários fatores, internos/externos, público/governamental, de curto e longo prazo que podem causar mudança nos preços das ações de maneiras distintas, mesmo quando condições semelhantes estavam presentes antes das oscilações. Isso nos revela que o comportamento do preço das ações não é determinado de forma determinística, portanto a teoria de probabilidade tem que ser utilizada para o estudo deste fenômeno. Existem vários métodos estatísticos/probabilísticos que podem ser utilizados para estudar esse fenômeno de prever o preço

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High-Order Multivariate Markov Chain Applied in Dow Jones and Ibovespa Indexes

Cited by 6 publications

References 18 publications

Markov chains to determine the probability of climate change for planting selection in the city of Caxias do Sul

Markov chains to determine the probability of climate change for planting selection in the city of Caxias do Sul

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak

Determinando a ordem das cadeias de Markov usadas na modelagem do mercado de ações

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