scholarly journals Stock Daily Price Regime Model Detection using Markov Switching Model

MATEMATIKA ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 127-140
Author(s):  
Wiwik Prihartani ◽  
Dwilaksana Abdullah Rasyid ◽  
Nur Iriawan
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Transition Probability ◽  
Structural Break ◽  
Markov Switching ◽  
Information Criterion ◽  
Model Parameters ◽  
Markov Switching Model ◽  
Switching Model ◽  
Markov Transition

Changes in stock prices randomly occur due to market forces with reoccurrencepossibilities. This process, also known as the structural break model, is captured throughchanges in the linear model parameters among periods with the Markov Switching Model(MSwM) used for detection. Furthermore, using the smallest Akaike Information Criterion(AIC) value on all feasible MSwM alternatives formed for a daily stock price, the completeMSwM model with its Markov transition is determined. This method has been tested andapplied to daily stock price data in several sectors. The result showed that the number ofregime models coupled with its transition probability helped investors make investmentdecisions.

scholarly journals Modeling South African Banks closing stock prices: a Markov-Switching Approach

2016 ◽  
Vol 8 (1(J)) ◽  
pp. 36-40
Author(s):  
Diteboho Xaba ◽  
Ntebogang Dinah Moroke ◽  
Johnson Arkaah ◽  
Charlemagne Pooe
Keyword(s):  
Stock Prices ◽  
Autoregressive Model ◽  
Stock Price ◽  
Stock Exchange ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Johannesburg Stock Exchange ◽  
Augmented Dickey Fuller ◽  
National Bank

In this paper, we provide evidence that the five variables used in the study were nonlinear in nature, while finding a better Markov-switching model. The study used dailydata obtained from the Johannesburg Stock Exchange over the period from January 2010 to December 2012. An extension of Markov Switching with autoregressive model was used for empirical analysis. Prior to using this model, the series were tested for nonlinear unit root with modified Kapetanois-Shin-Snell nonlinear Augmented Dickey-Fuller (KSS-NADF) test which successfully provided positive results.Other preliminary tests selected the first lag as optimal and confirmed that stock prices may switch between two regimes. Further empirical findings proved that stock prices can be successfully modelled with Markov Switching Autoregressive model of order one. First National bank was found to have 99.64% longer stock price stability if adjustments regards tofinancialpolicies are made. Capitec Bank was the least favoured among the banks.

scholarly journals Estimasi Bullish dan Bearish dengan Model Perpindahan Markov dan Risiko Sistematis (beta) dengan Model Penilaian Modal Sharpe dalam Investasi Saham di Bursa Efek Indonesia, Tahun 2011 - 2016

2018 ◽  
Vol 8 (3) ◽  
pp. 221
Author(s):  
Prima Respati ◽  
Budi Purwanto ◽  
Abdul Kohar Irwanto
Keyword(s):  
Interest Rates ◽  
Capital Market ◽  
Stock Price ◽  
Markov Switching ◽  
Systematic Risk ◽  
Risk Identification ◽  
Pricing Model ◽  
Markov Switching Model ◽  
Switching Model ◽  
Capital Assets

<p><em>ABSTRACT</em></p><p><em>Various research including Panggabean (2010) and Usman (2016) show that the long-term trend of Indonesia's capital market is on an uptrend, marked by more bullish periods and longer duration than bearish; and the development determined by rising rates of return rather than interest rates and exchange rates (Defrizal et al, 2015). However, the research has not determined yet whether there are any difference risks in bullish and bearish conditions, especially for systematic or market risk. This study aims to 1) identify the bullish and bearish segmentation period using the Markov Switching Model, and 2) measure systematic risk using the capital assets pricing model (CAPM) with the Sharpe beta indicator. Using the composite stock price index (JCI) and trading data from TICMI (The Indonesia Capital Market Institute) period 2011-2016, consists of 560 issuers, it was found that there were 10 segments that could be identified as 5 bullish periods for 30 weeks , and 5 bearish periods for 8 weeks. Other finding indicates that the probability of switching from bullish to bearish is 3.33% and from bearish to bullish is 12.14%. That means there are positive sentiments that the market tends to be bullish rather than vice versa. The result of beta or systematic risk identification indicates that during bullish and bearish period the market proved to be different risk. Other interesting findings, in both these two different conditions there are negative betas exist that still gives a positive yield.</em></p><p> </p><p>ABSTRAK</p><p>Berbagai riset termasuk Panggabean (2010) dan Usman (2016) menunjukkan bahwa kecenderungan jangka panjang pasar modal Indonesia berada pada kecenderungan naik (uptrend), ditandai dengan periode bullish lebih banyak, dan durasi lebih panjang, daripada bearish.  Perkembangan perkembangan itu dipicu oleh kenaikan tingkat imbalan, alih-alih suku bunga dan nilai tukar (Defrizal et al 2015). Namun riset-riset tersebut tidak mengidentifikasi eksistensi kondisi bullish dan bearish dan berdampak perbedaan risiko, terutama risiko sistematis atau risiko pasar, kecuali mengasumsikan saja keberadaannya.  Penelitian ini bertujuan 1) mengidentifikasi segmentasi periode bullish dan bearish dengan menggunakan model perpindahan Markov (Markov Switching), dan mengukur risiko sistematis menggunakan model penilaian modal (capital assets pricing model, CAPM) dengan indikator beta Sharpe.  Menggunakan data indeks harga saham gabungan (IHSG) serta data perdagangan bersumber dari TICMI (The Indonesia Capital Market Institute) periode 2011-2016 yang mencakup 560 emiten, diperoleh hasil bahwa dalam periode tersebut terdapat 10 segmen yang dapat diidentifikasi sebagai 5 periode bullish selama 30 pekan, dan 5 periode bearish selama 8 pekan.  Temuan lain menunjukkan bahwa peluang perpindahan dari kondisi bullish ke bearish sebesar 3,33% dan dari kondisi bearish ke bullish sebesar 12,14%. Artinya terdapat sentimen positif bahwa pasar cenderung menjadi bullish daripada sebaliknya.  Hasil identifikasi risiko sistematis menunjukkan, berbeda dengan konsep dasar CAPM, bahwa beta pada periode bullish dan bearish tidak sama.  Temuan menarik lainnya, pada kedua kondisi tersebut terdapat beta negatif yang dapat memberikan tingat imbalan positif.</p>

scholarly journals Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Luca Di Persio ◽  
Samuele Vettori
Keyword(s):  
Regime Switching ◽  
Implied Volatility ◽  
Financial Time Series ◽  
Markov Switching ◽  
Sampling Procedure ◽  
Model Parameters ◽  
Classical Approach ◽  
Markov Switching Model ◽  
Discrete State ◽  
Switching Model

We adopt aregime switchingapproach to study concrete financial time series with particular emphasis on their volatility characteristics considered in a space-time setting. In particular the volatility parameter is treated as an unobserved state variable whose value in time is given as the outcome of an unobserved, discrete-time and discrete-state, stochastic process represented by a suitable Markov chain. We will take into account two different approaches for inference on Markov switching models, namely, the classical approach based on the maximum likelihood techniques and the Bayesian inference method realized through a Gibbs sampling procedure. Then the classical approach shall be tested on data taken from theStandard & Poor’s 500and theDeutsche Aktien Indexseries of returns in different time periods. Computations are given for a four-state switching model and obtained numerical results are put beside by explanatory graphs which report the outcomes obtained exploiting both smoothing and filtering algorithms used in the estimation/calibration procedures we proposed to infer on the switching model parameters.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Bayesian Information Criterion ◽  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect

Abstract Objective: Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model. This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state Markov switching model with time lags of zero, one and two was fitted to the data. The Bayesian information criterion was used to evaluate the models. Results: According to the Bayesian information criterion, the two-state Markov switching model with a one-month lag is a suitable model. The month, the average wind speed, the minimum temperature have a positive effect on the number of Brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

Estimasi Bullish dan Bearish dengan Model Perpindahan Markov dan Risiko Sistematis (beta) dengan Model Penilaian Modal Sharpe dalam Investasi Saham di Bursa Efek Indonesia, Tahun 2011 - 2016

2018 ◽  
Vol 8 (3) ◽  
pp. 221
Author(s):  
Prima Respati ◽  
Budi Purwanto ◽  
Abdul Kohar Irwanto
Keyword(s):  
Interest Rates ◽  
Capital Market ◽  
Stock Price ◽  
Markov Switching ◽  
Systematic Risk ◽  
Risk Identification ◽  
Pricing Model ◽  
Markov Switching Model ◽  
Switching Model ◽  
Capital Assets

<p><em>ABSTRACT</em></p><p><em>Various research including Panggabean (2010) and Usman (2016) show that the long-term trend of Indonesia's capital market is on an uptrend, marked by more bullish periods and longer duration than bearish; and the development determined by rising rates of return rather than interest rates and exchange rates (Defrizal et al, 2015). However, the research has not determined yet whether there are any difference risks in bullish and bearish conditions, especially for systematic or market risk. This study aims to 1) identify the bullish and bearish segmentation period using the Markov Switching Model, and 2) measure systematic risk using the capital assets pricing model (CAPM) with the Sharpe beta indicator. Using the composite stock price index (JCI) and trading data from TICMI (The Indonesia Capital Market Institute) period 2011-2016, consists of 560 issuers, it was found that there were 10 segments that could be identified as 5 bullish periods for 30 weeks , and 5 bearish periods for 8 weeks. Other finding indicates that the probability of switching from bullish to bearish is 3.33% and from bearish to bullish is 12.14%. That means there are positive sentiments that the market tends to be bullish rather than vice versa. The result of beta or systematic risk identification indicates that during bullish and bearish period the market proved to be different risk. Other interesting findings, in both these two different conditions there are negative betas exist that still gives a positive yield.</em></p><p><em><br /></em></p><p>ABSTRAK</p><p>Berbagai riset termasuk Panggabean (2010) dan Usman (2016) menunjukkan bahwa kecenderungan jangka panjang pasar modal Indonesia berada pada kecenderungan naik (uptrend), ditandai dengan periode bullish lebih banyak, dan durasi lebih panjang, daripada bearish.  Perkembangan perkembangan itu dipicu oleh kenaikan tingkat imbalan, alih-alih suku bunga dan nilai tukar (Defrizal et al 2015). Namun riset-riset tersebut tidak mengidentifikasi eksistensi kondisi bullish dan bearish dan berdampak perbedaan risiko, terutama risiko sistematis atau risiko pasar, kecuali mengasumsikan saja keberadaannya.  Penelitian ini bertujuan 1) mengidentifikasi segmentasi periode bullish dan bearish dengan menggunakan model perpindahan Markov (Markov Switching), dan mengukur risiko sistematis menggunakan model penilaian modal (capital assets pricing model, CAPM) dengan indikator beta Sharpe.  Menggunakan data indeks harga saham gabungan (IHSG) serta data perdagangan bersumber dari TICMI (The Indonesia Capital Market Institute) periode 2011-2016 yang mencakup 560 emiten, diperoleh hasil bahwa dalam periode tersebut terdapat 10 segmen yang dapat diidentifikasi sebagai 5 periode bullish selama 30 pekan, dan 5 periode bearish selama 8 pekan.  Temuan lain menunjukkan bahwa peluang perpindahan dari kondisi bullish ke bearish sebesar 3,33% dan dari kondisi bearish ke bullish sebesar 12,14%. Artinya terdapat sentimen positif bahwa pasar cenderung menjadi bullish daripada sebaliknya.  Hasil identifikasi risiko sistematis menunjukkan, berbeda dengan konsep dasar CAPM, bahwa beta pada periode bullish dan bearish tidak sama.  Temuan menarik lainnya, pada kedua kondisi tersebut terdapat beta negatif yang dapat memberikan tingat imbalan positif.</p>

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract Objective Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models.Results According to the BIC, the two-state MSM with a one-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the incidence of brucellosis in Western Iran using Markov switching model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract Objective Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models. Results According to the BIC, the two-state MSM with a 1-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Bayesian Information Criterion ◽  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect

Abstract Objective Brucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model. This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state Markov switching model with time lags of zero, one and two was fitted to the data. The Bayesian information criterion was used to evaluate the models. Results According to the Bayesian information criterion, the two-state Markov switching model with a one-month lag is a suitable model. The month, the average wind speed, the minimum temperature have a positive effect on the number of Brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

scholarly journals Predicting the Incidence of Brucellosis in Western Iran using Markov switching model

2020 ◽  
Author(s):  
Maryam Mohammadian-Khoshnoud ◽  
Majid Sadeghifar ◽  
Zahra Cheraghi ◽  
Zahra Hosseinkhani
Keyword(s):  
Climatic Factors ◽  
Markov Switching ◽  
Information Criterion ◽  
Suitable Model ◽  
Time Lags ◽  
Markov Switching Model ◽  
Average Wind Speed ◽  
Switching Model ◽  
Negative Effect ◽  
Secondary Study

Abstract ObjectiveBrucellosis is a zoonosis almost chronic disease. Brucellosis bacteria can remain in the environment for a long time. Thus, climate irregularities could pave the way for the survival of the bacterium Brucellosis. Brucellosis is more common in men 25 to 29 years of age, in the western provinces, and in the spring months. The aim of this study is to investigate the effect of climatic factors as well as predicting the incidence of Brucellosis in Qazvin province using the Markov switching model (MSM). This study is a secondary study of data collected from 2010 to 2019 in Qazvin province. The data include Brucellosis cases and climatic parameters. Two state MSM with time lags of 0, 1 and 2 was fitted to the data. The Bayesian information criterion (BIC) was used to evaluate the models.ResultsAccording to the BIC, the two-state MSM with a one-month lag is a suitable model. The month, the average-wind-speed, the minimum-temperature have a positive effect on the number of brucellosis, the age and rainfall have a negative effect. The results show that the probability of an outbreak for the third month of 2019 is 0.30%.

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