The dynamic interaction between macroeconomic factors and stock market performance is a central concern in financial decision-making. This study examines the short-term and long-term effects of selected macroeconomic variables, the Consumer Price Index (CPI), USD/VND exchange rate, gold price, WTI crude oil price, and Brent crude oil price, on the Vietnamese stock market, proxied by the VN Index. Using monthly data (January 2010–December 2023) for long-term analysis and daily data for short-term analysis, the study employs a comprehensive econometric framework including the Augmented Dickey–Fuller stationarity test, Johansen cointegration test, Granger causality test, Vector Autoregression (VAR) model, and the Autoregressive Distributed Lag (ARDL) model. The findings reveal that in the short run, CPI, WTI oil price, and Brent oil price exert significant impacts on the VN Index, whereas the USD/VND exchange rate and gold price do not produce immediate effects. In the long run, the ARDL model indicates a negative relationship between CPI and the VN Index, a mild lagged influence from the exchange rate, and a complex, nonlinear effect of Brent oil prices. These results offer actionable insights for investment decision-making: investors should closely monitor inflation and oil price movements to adjust portfolio strategies, while policymakers should prioritize effective inflation control and exchange rate management to enhance stock market stability. By identifying which macroeconomic variables carry predictive power for stock market fluctuations, this study contributes empirical evidence to support more informed and resilient financial decision-making in emerging market contexts.

Oil price markets can benefit from a better considerate of how shocks can affect volatility through time. This study assesses the impact of structural changes and outliers on volatility persistence of two crude oil markets WTI and Brent oil price between January 1, 1996 and March 17, 2014. First, we identify the FIGARCH process proposed by Baillie et al. (1996) [Baillie, R.T., Bollerslev, T., & Mikkelsen, H.O., (1996), Fractionally integrated generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 74, 3-30.] and investigate some of its statistical proprieties and then incorporate this information into the volatility modelling. We also show that outliers can bias the estimation of the persistence of the volatility. Taking into account outliers on the volatility modelling process improve the understanding of volatility in crude oil markets.

The capital market, as one of the main components of the financial markets, plays an important role in the economic development of countries. As financial markets become more globalized through the free flow of capital and international trade, price fluctuations in financial assets also affect other assets and markets. Due to the high impact of foreign exchange, gold, and oil markets on other financial markets, this study examined the impact of these markets on the Tehran Stock Exchange market from April 2015 to March 2021. In this regard, US dollar, ounces of gold, and crude oil are used as independent variables, and Tehran Price Index (TEPIX) is considered as a dependent variable. The results of the final model indicate that the prices of dollars, gold, and oil had significant effects on the total price index of the Tehran Stock Exchange.

During the past few months, there has been a steady downside trend on oil price. From summer, 2015 to winter, 2016, the oil price has declined to the $20 range. Its rate of decline is amazing, having lost $10, one third of its value, in just one month. This paper presents a survey on recently published studies on relationship between oil price and stock market. The study covers several studies on the effects of oil price on China, India, Lebanon, United States and some other G7 stock markets. We also review the effects of oil price volatility on stock market. The results of most studies have indicated some strong relationships between oil price volatility and stock market return.