Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model

Xaba, Diteboho; Moroke, Ntebogang Dinah; Rapoo, Ishmael

doi:10.22610/jebs.v11i3(j).2865

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The second is a probit model to determine factors that might increase the probability of happiness during a pandemic. We build on the framework provided by Xaba et al [26] and Ismail and Isa [27] to test for structural breaks in the data, given that a regime-switching model is the most appropriate in the presence of structural breaks.…”

Section: Methodsmentioning

confidence: 99%

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model

2021

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Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state’s mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states’ evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand’s daily happiness data for May 2019 –November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.

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Section: Methodsmentioning

confidence: 99%

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model

2021

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“…For instance, Shumway and Stoffer 40 applied a dynamic linear model that switch endogenously based on the independent random process. Other studies that combined the dynamic effect into the MS framework include Kim and Yoo, 41 Chauvet, 42 and Xaba et al 43 Next, considering the data with heteroscedasticity such as stock prices, there are studies to apply a change in variance with no AR effect. For instance, Kim and Nelson 44 demonstrated a three-stateregime-switching regression with regime heteroskedasticity (switching of variances) to a stock market data.…”

Section: Markov-switching Regression -A Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A new look at asymmetric effect of oil price changes on inflation: Evidence from Malaysia

Sek

2022

Energy & Environment

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The existing literature that examined the effect of oil price on domestic price inflation only focused on consumer price at an aggregate level. The studies that focused on producer and production levels, and based on disaggregated data are very lack. Besides, previous studies also mainly applied the linear regression approach in studying the effect of oil price. This study seeks to explore the mentioned issues by focusing on sectoral consumer (CPI), industrial (IPI), and producer (PPI) prices of Malaysia. The Markov-switching (MS) regression technique is applied. The models are innovated by incorporating the asymmetric effects of oil price changes. The results reveal different reactions of sectoral domestic price inflation to oil price changes. The oil price has asymmetric effects on domestic price inflation with higher impacts on industrial and producer prices than the consumer price. The effect is larger in sectors that have higher linkages with oil/ energy resources. These sectors are oil-intensive and are sensitive to oil price changes. Among these sectors are the CPI transportation sector, IPI manufacturing, and electrical sectors as well as PPI fuel, chemicals, and manufacturing sectors. However, oil is not the main factor causes to domestic inflation. The main determinants of inflation are real exchange rate, aggregate supply, and demand. Besides, the policy decisions are also influential on price stability. The sectors of CPI transportation, PPI animals & vegetable oils, and PPI fuel have a high tendency to increase prices and should be well-monitored.

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Dynamic regime differences in the market behavior of primary natural resources in response to geopolitical risk and economic policy uncertainty

Muğaloğlu,

Kuşkaya,

Aldieri

et al. 2023

Resources Policy

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Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model

Cited by 5 publications

References 17 publications

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model

A new look at asymmetric effect of oil price changes on inflation: Evidence from Malaysia

Dynamic regime differences in the market behavior of primary natural resources in response to geopolitical risk and economic policy uncertainty

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