Does Covid-19 Change The Stock Market Relationship With Interest-Exchange Rate?

This study aimed to assess the impact of inflation, exchange rate, and interest rate on stock market returns and volatility in Nigeria using data from 2000M1 to 2022M9. The Autoregressive Distributed Lag (ARDL) model was employed to investigate the relationship between stock market returns and the independent variables, while the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model was used to examine the impact on stock market volatility. Preliminary tests indicated no serial correlation but heteroskedasticity among the independent variables, which was addressed using the ARDL model with a HAC (Newey-West) covariance matrix adjustment. The results revealed significant stock return effects at a 10% level of significance with a lag of 4, suggesting a link to past performance up to four months. Additionally, the prime lending rate exhibited significance at a lag of 1, indicating the stock market's response to changes in the prime lending rate after one month. However, no significant response was found for changes in the exchange rate and inflation during the study period. The GARCH model showed that all variables, except inflation, significantly impacted stock market volatility. Notably, the maximum lending rate, prime lending rate, interbank rate, and Treasury bill rate had substantial effects on stock market volatility. The study suggests that the monetary authority should focus on interest rate mechanisms for more effective and responsive monetary policy decisions, particularly regarding the stock market.

Stock market performance have a significant impact on economic development of a country. Stock markets are supposed to be affected by different financial and macro-economic variables such as gold and oil prices, inflation rate, interest rate, exchange rate and unemployment rate etc. This study is an attempt to find the impact of financial and macro-economic variables on the Karachi Meezan Index 30, the first Islamic Stock index in Pakistan. Monthly data from July 2011 till June 2016 of the variables KMI Index 30, Interest rate, Inflation rate Exchange rate and Gold price is used in this study to find the impact of Interest rate, Inflation rate, Exchange rate and Gold prices on KMI 30 by using Multiple regression model. The empirical result shows that there is a negative relationship of KMI 30 index with interest rate and gold prices whereas a positive relationship exist between exchange rate and KMI 30 Index. Inflation rate did not show a significant relationship with KMI 30 Index in our regression model.

One of the trickiest problems for investors is how the financial and commodity markets interact with each other. The volatility in one market might affect the price of the other market. This essay aims to clarify the relationship between gold, crude oil, exchange rates, and Chinese stock market indices. In order to do this, the Shanghai Stock Exchange Index and the China Industrial Index, two indices that reflect the Chinese financial market, were subjected to the DCC-GARCH model (Generalized Autoregressive Conditional Heteroskedasticity Model). By capturing the dynamic correlations of the time series' variance, covariance, and correlation coefficient, this model aids in illuminating the long-run dynamic correlation between returns. The findings show that gold and crude oil are positively correlated with both the Shanghai Stock Exchange Index and the China Industrial Index, while the correlations between the USD CNY exchange rate and the two Chinese stock indices are negatively correlated, using monthly data from 2000-01-03 to 2022-08-30. This paper shows that stock prices in China are weakly linked to international commodity markets and exchange rate with the US. Additionally, throughout the observation period, financial crisis (2008) and the COVID-19 pandemic breakout (2020) enhanced comovement between the Chinese stock market, commodities, and exchange rate in general.

From the very beginning, this article makes a brief exposition of the relationship and the transmission mechanism between interest rate, exchange rate and stock price. As to the empirical part, we establish the TVP-SV-VAR model to figure out the correlation between the interest rates, exchange rates, and stock prices by using the monthly data from July, 2005 to December, 2017 in China. The results show that the volatility of the stock price under the impact of interest rate has time-varying features and structural changes, the volatility of the exchange rate has time-varying features under the influence of stock index variables, and the Shanghai Composite Index has time-varying features under the impact of exchange rate, but it has a less significant effect between with them. In conclusion, we analyze the reasons for this phenomenon by combining with the reality in China, and give some suggestions.

PurposeSeveral theoretical and empirical studies have shown the significant effects of economic and environmental factors on a large number of financial indicators. In this paper the authors are going to study whether the main stock market index, is impacted by the variations of the exchange rate and the interest rates.Design/methodology/approachThis paper studies the response of the index market return to fluctuations in the interest rate and the exchange rate in five countries from the MENA region (Tunisia, Morocco, Egypt, Turkey and Jordan). To investigate whether this impact exists, the authors used the non-linear autoregressive distributed lag (NARDL) model with daily data from June 1998 to June 2018.FindingsThe application of the non-linear ARDL model confirms the presence of cointegration between return index, interest rate and exchange rate. The results show that the asymmetry hypothesis is only valid for the short run which suggests that the market index is sensitive to the variation in the interest rate and exchange rate. This means that these macroeconomic factors play an important role in the MENA region stock markets.Originality/valueThe findings confirm that the index returns in the MENA region stock markets are related to macroeconomic fundamentals such as the exchange rate and the real interest rate. The reaction of some indices is sensitive to whether the shocks are positive or negative. This finding may help investors to choose their strategies starting from these changes. Accordingly, policy makers must pay attention to the development progress of stock market.

Whether the long-memory property is inherent in the movement of the stock market returns and volatility (risk) time series is known as a very important issue practically or theoretically regard to the efficiency of the stock market. The efficient market hypothesis describes that the information obtained from past statistics can not be used to predict the future stock price. This is because when generating the information that may affect the price of the stock reflects this value of the information on the price quickly and enough. Since the future information is unknown, the future stock price will not be predictable if the stock market is efficient. However, if the long-memory property exists in the stock market returns and volatility time series, it could predict a certain portion of the future returns and risks by using past data. This predictability means the assumption of classical investment theory, that the stock market is efficient, may not be proper. Thus, the existence of long-memory property has been addressed as an important research topic by the financial investment researchers and stock market investors. By using the stock prices of 50 stocks representing the Chinese stock market and their weighted average statistical index - SSE 50 Index, this study analyzes whether the long-memory property is inherent in Chinese stock market price movement as well as explains whether the existence of long-memory property is spurious result of the contemporaneous aggregation. The Chinese stock market is extremely proper market to perform the research related to the long-memory property because it is large and highly dynamic market. Using the returns and volatility of daily closing price (i.e. the absolute value of returns and its squared value) from January 2, 2004 to December 10, 2014 to conduct the Lo’s modified (R/S) analysis and the Geweke-Porter-Hudak (GPH) test. The main results of the empirical analysis from this study are as follows. First, although SSE 50 Index return series has long-memory property, there are not many evidences for its 50 constituent company stock prices. This means that predicting the return series for SSE 50 Index is relatively easier than individual stock prices. Second, in the case of volatility, both of the SSE 50 Index and its 50 individual stock prices have the presence of a long-memory property. Third, most of the 50 individual stock prices in Chinese market have the long-memory property. These are the unique properties inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. Fourth, volatility has the stronger presence of a long-memory property than returns. This means that predicting the risk is relatively easier than returns due to volatility clustering. Based on the overall statistical test results, volatility has the stronger presence of a long-memory property than returns. The long-memory property exists in the Chinese stock market and this is the unique property inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. These analytical findings indicate that the Chinese stock market is not fully efficient due to the existence of the long-memory property. The reasons that Chinese stock market is not efficient enough are that many Chinese investors have the speculative purposes and market information is not delivered transparently and quickly. Because of these characteristics, global investors will have room to reduce the risk and increase profits by leveraging long-memory properties in the Chinese stock market.

In 2016, the performance of Capital markets in Africa experienced a downward trend. Notable among them were Zambia, Ghana and Nigeria (http://www.african-markets.com/). The Lusaka Securities Exchange (LuSE) share price index was the worst performer year-to-date, down 26.83% in local currency. Among the contributing factors cited were commodity prices as the country relies on copper for more than 70% of its export revenue and had therefore suffered from commodity prices plunged which led to weakened currencies and widened budget shortfalls (http://www.african-markets.com/). Although the contributing factors are known, their impact on the Zambian stock market is not known. This study was an attempt to establish the impact of commodity prices and macroeconomic factors on the stock market returns. Like in similar studies, Nordin et al. (2014) and Alam & Uddin (2009), we used Auto regression Distribution Lag and Cointegration analysis and the Vector Error Correction model on the variables (stock price index, copper price, oil price, interest rates, and exchange rates) for the period 2004-2016. The findings of the study revealed Interest rates, exchange rates, copper and Oil price jointly have the long and short Run impact on the Lusaka Stock Market, but individually, only interest rates and copper prices had a significant long term impact on the stock market, but in the short run only Copper and Exchange rates had an immediate impact on the stock market. One important policy implication of this study is that it will alert the authorities and the investors on the impact of commodity prices, interest rate, and the exchange rate on the Lusaka Securities Exchange stock market performance.

This paper explores dynamic relation between macroeconomic variables and stock prices in India under a Vector Auto Regression framework during 2000- 2012 using monthly data. Variance Decompositions and Impulse responses have been employed to determine how long it would take for the effect of variables to work through the system. The study found money supply, interest rate and exchange rate are the prime economic variables which could make significant impact on stock return volatility. Inflation rate and Index of Industrial Production have shown very negligible influence on the stock prices. Variance Decompositions and Impulse Responses found money supply as the strongest factor influencing stock market in the long-run. When the impact of interest rate and exchange rate are found of very short term, the shocks put forth by FIIs do not cause any significant variation in the movement of nifty prices. Highly integrated structure of monetary system and its linkage with financial sector leads to the conclusion that any variable which has a negative effect on cash flows shall be in a negative relationship with the stock prices. So the monetary policy could be viewed as an effectual instrument in the hands of the government to stabilize stock market.

The Federal Reserve announced a 75-basis point rate hike to raise the benchmark rate to a range of 2.25%-2.50% to curb another spike in inflation on July 28. Since 2022, the Fed has announced four rate hikes, with the cumulative increase reaching 150 basis points in June-July alone, the largest since Volcker took the helm of the Fed in the early 1980s, which might be reflected in the volatility of stock prices in China and the U.S. market. This paper assesses the impact of fed rate hikes on the Chinese A-share market, specifically the Shanghai Stock Exchange (SSEC) and Standard and Poor's 500 (S&P 500), a stock market index that reflects the U.S. stock market. A VAR model and an ARMA-GARCH model were established to analyze the variations in stock prices caused by changes in foreign exchange rate between CNY (China Yuan) and USD (US dollars). As a result, this paper asserts that a more elevated exchange rate has a relatively negative net effect on the Chinese stock market and a negligible influence on the U.S. stock market.

Fluctuations in exchange rates and interest rates in a dynamic economic environment mean that coal mining companies need to carefully monitor, identify and respond to these changes with appropriate strategies, in order to minimize risks and increase profitability and the company's stock price. This research explores the influence of exchange rates and interest rates on stock prices of coal mining companies through profitability as an intermediary variable. The research method used is an associative method with a sample consisting of 20 coal mining companies listed on the Indonesia Stock Exchange, which were selected through a purposive sampling technique. To achieve the objectives of this research, path analysis was applied to time series data for 2013-2022, obtained from the Indonesian Stock Exchange and Bank Indonesia. The results of this research show that exchange rates and interest rates have a significant influence on company stock prices directly. Meanwhile, profitability is unable to mediate the influence of exchange rates and interest rates on stock prices. This research provides important insights for mining company managers, enabling them to understand the impact of macroeconomic factors, such as exchange rates and interest rates, on stock prices.

This research investigates the impact of inflation, interest rates, and exchange rates on the Composite Index (JCI) through quantitative methods and secondary data analysis. Utilizing a saturated sample method over a five-year period (2017-2021) with 60 monthly observations, the study employs multiple linear regression analysis using EViews 9. The findings reveal that inflation has a positive yet insignificant effect on the JCI, while interest rates exert a positive and significant influence. Additionally, exchange rates display a negative and significant impact on the JCI. Notably, the simultaneous presence of inflation, interest rates, and exchange rates significantly affects the JCI. The results contribute to the understanding of the intricate dynamics between macroeconomic factors and stock market performance, offering valuable insights for investors, policymakers, and market participants worldwide.
 Highlights:
 
 The study examines the impact of inflation, interest rates, and exchange rates on the Composite Index (JCI) using empirical analysis.
 Interest rates are found to have a positive and significant effect on the JCI, while exchange rates show a negative and significant impact.
 The simultaneous presence of inflation, interest rates, and exchange rates has a significant influence on the JCI, providing insights for investors and policymakers.
 
 Keywords: Inflation, Interest Rates, Exchange Rates, Composite Index, Empirical Analysis.

This paper examines the impact of commodity prices (palm oil price, oil price, and gold price), interest rate, and exchange rate on the Malaysian stock market performance. Employing the bounds test approach, the results of the study showed cointegrating relationships among variables. Specifically, the results revealed a significant influence of palm oil price on the stock market index. However, no significant influence was observed for both the oil price and gold price. Interest rate and exchange rate showed significant influences, which are consistent with past empirical studies. One important policy implication from this study is that the authorities should also pay attention to the effect of commodity prices, in addition to macroeconomic variables, in implementing relevant polices, as they may have a negative impact on the Malaysian stock market.

Monetary policy, exchange rate fluctuation, and herding behavior in the stock market

This study aims to analyze the global crisis's effect on the causal relationship between interest rate, exchange rate, and stock price. The sample used in this study is the IDX Composite, rupiah to US dollar exchange rate, and interest rates in Indonesia with monthly data during the observation period from January 2003 until December 2020. The pre-crisis results indicate a one-way causality relationship between exchange rates and interest rates; there is no causal relationship between the IDX Composite and the exchange rate and the IDX Composite and interest rates. The post-crisis results indicate a one-way causality relationship between the IDX Composite and the exchange rate and the IDX Composite and interest rates. Meanwhile, there is a two-way causality between exchange and interest rates. The study results in all periods indicate a one-way causality relationship between the IDX Composite and the exchange rate and IDX Composite and interest rates. Meanwhile, there is no causal relationship between exchange rates and interest rates

The role of the interest and exchange rates in sustaining economic growth has been a highly researched subject. Therefore, this study examines the influence of the monetary policy interest rate, the real exchange rate and the business climate in the Euro area on the economic growth in Romania. For this purpose, we have applied a pre-test for structural breaks to identify the existence of structural breaks, followed by the traditional unit root tests and the unit root tests with structural breaks to verify the stationarity of the variables. The results of the Bound cointegration test led to the autoregressive distributed lag (ARDL) short-run model that measures the short-run impact of the interest rate, exchange rate and the business climate in the Euro area on the economic growth of Romania. Our findings show that in the short run, the economic growth is negatively influenced by the interest rate, and positively by the exchange rate. We also indicate that the business climate in the Euro area has mixed effects on the economic growth. Finally, considering the growing interdependence between the internal and external (European) business environment, the results are highly significant for handling the interest and exchange rates in sustaining economic growth.