Fisher’s z Distribution-Based Mixture Autoregressive Model

Solikhah, Arifatus; Kuswanto, Heri; Iriawan, Nur; Fithriasari, Kartika

doi:10.3390/econometrics9030027

Cited by 6 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since Russia is one of the main suppliers of oil to the world market, oil export revenues make up a significant part of Russian budget revenues [41]. The level of income as one of the most important sources of economic growth will be determined by the global price environment, which is based on the price of Brent oil [42].…”

Section: Methodsmentioning

confidence: 99%

Banking Sector Profitability: Does Household Income Matter?

2022

View full text Add to dashboard Cite

Household incomes, their level and dynamics are one of the factors that ensure the achievement of the Sustainable Development Goals. At the same time, stable development of the banking sector, which is impossible without steady earnings, determines economic growth, which also positively affects reaching the Sustainable Development Goals. The paper examines the impact of household income on the return on assets of the banking sector in Russia using annual time series from 2003 to 2019. The study was conducted using formalized economic and mathematical methods of analysis by linear regression with least squares tests on the significance of the model, with tests for redundancy of insignificant variables (Wald test), Ramsey test on the functionality of the model, White and Breusch Pagan test for heteroscedasticity (heterogeneity of observations) and multicollinearity by method of inflation factors, graphic method. The Multiple Linear Regression (MLR) model was used. The results show that (1) an increase in the growth rate of household income and deposits in the non-financial sector has a positive effect on the return on assets of the banking sector; (2) an increase in the growth rate of the price of Brent crude oil and non-performing loans negatively affect the dependent variable; and (3) the regions that have the greatest (positive and negative) impact on profitability of whole bank sector in Russia were identified. Increasing household incomes and eliminating inequality in the incomes of the population of different regions will have a positive impact not only on social well-being but will also provide the banking sector with the opportunity for profitable operation and create conditions for sustainable growth. Our conclusions are useful for the regulator and individual banks and can be taken into account when developing and implementing policies aimed at sustainable development.

show abstract

Section: Methodsmentioning

confidence: 99%

Banking Sector Profitability: Does Household Income Matter?

2022

View full text Add to dashboard Cite

show abstract

“…HMC requires the gradient of the ln-posterior's density. In practice, the gradient must be computed analytically [26,27].…”

Section: Hamiltonian Monte Carlo (Hmc)mentioning

confidence: 99%

On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

et al. 2021

Self Cite

View full text Add to dashboard Cite

The model developed considers the uniqueness of a data-driven binary response (indicated by 0 and 1) identified as having a Bernoulli distribution with finite mixture components. In social science applications, Bernoulli’s constructs a hierarchical structure data. This study introduces the Hierarchical Bernoulli mixture model (Hibermimo), a new analytical model that combines the Bernoulli mixture with hierarchical structure data. The proposed approach uses a Hamiltonian Monte Carlo algorithm with a No-U-Turn Sampler (HMC/NUTS). The study has performed a compatible syntax program computation utilizing the HMC/NUTS to analyze the Bayesian Bernoulli mixture aggregate regression model (BBMARM) and Hibermimo. In the model estimation, Hibermimo yielded a result of ~90% compliance with the modeling of each district and a small Widely Applicable Information Criteria (WAIC) value.

show abstract

Functional Volatility Relationship Analysis and Prediction in International Crude Oil Futures Markets

Sun,

Li,

et al. 2024

Journal of Futures Markets

View full text Add to dashboard Cite

To measure intraday volatility in international crude oil futures markets, we use the functional conditional variance to measure volatility and focus on volatility relationship analysis and prediction. This paper analyzes the simultaneous and predictive volatility relationships in crude oil futures markets. For covariate markets with significantly positive predictive volatility relationships, this paper empirically extends the fGARCH‐X model so that it can introduce the volatility characteristics of covariate markets. The empirical application shows that using the fGARCH‐X model can generally improve the predictive effects of functional volatility in crude oil futures markets. The robustness results indicate that the improvement in volatility prediction is significant. This study is beneficial for the stable development of international crude oil futures markets and is valuable for investors' investment decision‐making.

show abstract

Fisher’s z Distribution-Based Mixture Autoregressive Model

Cited by 6 publications

References 37 publications

Banking Sector Profitability: Does Household Income Matter?

Banking Sector Profitability: Does Household Income Matter?

On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

Functional Volatility Relationship Analysis and Prediction in International Crude Oil Futures Markets

Contact Info

Product

Resources

About