Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

COPULA FRANK UNTUK PERHITUNGAN VALUE AT RISK PORTOFOLIO BIVARIAT PADA MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst loss that occurs in a stock portfolio with a certain level of confidence and within a certain time period. In general, financial data have a high volatility value, which causes the residuals are not normally distributed. ARCH/GARCH modoel is used to solve the heteroscedasticity problem. If the data also have an asymmetric effect, it is modelled with Exponential GARCH model. Copula-Frank is part of the Archimedian copula which is used to solve empirical cases. The data on this study were BBCA and KLBF stock price return data in the observation period 30 December 2011 – 6 December 2019. Furthermore, to test the validity of the VaR model, a backtesting test will be carried out using the Kupiec Test. The results showed that the best model used for BBCA stocks was ARIMA (1,0,1) EGARCH (1,1) and for KLBF stocks was ARIMA (1,0,1) EGARCH (1,2). The amount of risk with a 95% confidence level used a combination of the EGARCH and Copula-Frank models was 2.233% of today's investment. Based on the backtesting test used the Kupiec Test, the VaR model of the portfolio obtained was declared valid.

The Life Cycle Hypothesis and Uncertainty: Analyzing Aging Savings Relationship in Tunisia

This research empirically checks the effect of uncertainty on aging-saving link that is indirectly captured by an auxiliary variable: the unemployment. It looks at the nexus population aging and savings by bringing out the unemployment context importance in determination saving behavior notably in a setting of unavailability of unemployment allowance. To better estimate population aging, it considers the old-age dependency ratio besides the total dependency one, which is the usually indicator used. Applying the Structural VAR model, the variance decomposition technique and the response impulse function, on Tunisia during 1970–2019, it puts on show that elderly do not dissave in a context of enduring unemployment and unavailability of unemployment allowance. Unemployment is an important factor able to shaping the saving behavior and to distort the life cycle hypothesis’s prediction. Consequently, the life cycle hypothesis cannot be validated under uncertainty. Hence, aging does not to alter savings systematically. The nature of aging-saving relationship is upon to social and economic context.

Estimating Vat Refunds the Case of South Africa

This study intends to estimate VAT refund levels in South Africa in an ideal situation where there are well-equipped, incorruptible officials and a proper VAT system is in place. Understanding the dynamics behind the behaviour of VAT and its main drivers is crucial and could have a huge benefit to the country’s economy with regards to closing the tax gap related to this tax type. Using the data from various sources (VAT refunds and some macroeconomic variables), a Vector Autoregression (VAR) model was used to estimate the level of VAT refunds in South Africa. The model estimates VAT refunds for the period 2021/22 to be R242.7 billion, while the VAT refunds forecast for the period 2022/23 and 2023/24 amounts to R254.6 billion and R267.3 billion, respectively. Furthermore, VAT refunds contribute on average 17.5% to the total tax for the forecast period of 2021/22-2023/24. The study also indicates that the growth in VAT refunds is influenced by the growth in domestic VAT collections, increasing employment rate and the growth in both agriculture and construction GDP. The estimated level of VAT refunds can serve as an important consideration in the national budgeting processes in South Africa. Adequate provisions can be made to enable proper planning and distributions to government departments. To our knowledge, this study is the first of its kind for South Africa. In summary, the South African tax authority should not deviate from the primary goal of building sound VAT systems based on improved voluntary compliance through effective systems of self-assessment

The Causal Relation between Revenue and Expenditure of Bangladesh

Bangladesh is a developing country that has been experiencing budget deficits since its independence in 1971. It means the government spending has been exceeding the government revenue. This phenomenon calls for a study of government spending or expenditure and government revenue. This study tries to establish a causal relation between expenditure and revenue of governments of Bangladesh. To accomplish this, this study uses the Vector Autoregressive (VAR) model and the Granger Causality model on the data for the financial year from 1993-1994 to 2017-2018. The study reveals that in the context of Bangladesh, total revenue affects total expenditure, whereas total expenditure does not affect total revenue.

Regularization of the Gravity Field Inversion Process with High-Dimensional Vector Autoregressive Models

Earth’s gravitational field provides invaluable insights into the changing nature of our planet. It reflects mass change caused by geophysical processes like continental hydrology, changes in the cryosphere or mass flux in the ocean. Satellite missions such as the NASA/DLR operated Gravity Recovery and Climate Experiment (GRACE), and its successor GRACE Follow-On (GRACE-FO) continuously monitor these temporal variations of the gravitational attraction. In contrast to other satellite remote sensing datasets, gravity field recovery is based on geophysical inversion which requires a global, homogeneous data coverage. GRACE and GRACE-FO typically reach this global coverage after about 30 days, so short-lived events such as floods, which occur on time frames from hours to weeks, require additional information to be properly resolved. In this contribution we treat Earth’s gravitational field as a stationary random process and model its spatio-temporal correlations in the form of a vector autoregressive (VAR) model. The satellite measurements are combined with this prior information in a Kalman smoother framework to regularize the inversion process, which allows us to estimate daily, global gravity field snapshots. To derive the prior, we analyze geophysical model output which reflects the expected signal content and temporal evolution of the estimated gravity field solutions. The main challenges here are the high dimensionality of the process, with a state vector size in the order of 103 to 104, and the limited amount of model output from which to estimate such a high-dimensional VAR model. We introduce geophysically motivated constraints in the VAR model estimation process to ensure a positive-definite covariance function.

Economic Policy Uncertainty (EPU) and China's Export Fluctuation in the Post-pandemic Era: An Empirical Analysis based on the TVP-SV-VAR Model

In the COVID-19 pandemic, the bidirectional policy adopted by the governments to stimulate domestic economy and reinforce foreign trade control is making the trade environment abnormally complex. China is facing a new challenge in export trade growth. Based on the continuous monthly data from January 2002 to April 2021, this paper uses the time-varying TVP-SV-VAR model to study the impulse response of China's export trade to economic policy uncertainty (EPU). It is found that (1) on the whole, the shock of global EPU and China's EPU on China's export to the OBOR/RCEP member countries is time-varying, different, and structurally significant; (2) during the pandemic, EPU has a significant short-term negative shock on China's gross exports and export to OBOR/RCEP members, and this shock is especially big in the case of global EPU. In the post-pandemic era, China should strengthen pandemic control and economic risk monitoring, continue with execution of multilateral FTAs and create a sustainably stable export trade environment.