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The present study examines the long-run and short-run effects of monetary factors (money supply, interest rate, inflation and foreign currency exchange rate) on the Indian stock market.

The study used sophisticated econometric tools to analyse monthly observations from January 1993 to December 2019.

The augmented Dickey–Fuller (ADF) test indicates that the variables involved in the present study are either I(0) or I(1). The Bai–Perron test multiple break point test identifies four breakpoint dates in the Indian stock market index series. The breakpoint dates are incorporated as different dummy variables in the autoregressive distributed lag-error correction model (ARDL-ECM) regression. The F-bounds test reveals that the variables in the study are cointegrated within the time period under consideration. This study’s findings show that the interest rate, which is a proxy for monetary policy instrument, and the foreign currency exchange rate have a negative impact on the Indian stock market. Furthermore, the authors find that structural changes significantly affect the performance of Indian stock market.

The study's outcomes indicate that economic factors should be taken into account by investors and portfolio managers when formulating long-term investment strategies. The government, through the Reserve Bank of India, should exercise caution in avoiding discretionary actions that could increase interest rates since the flow of funds to the stock market will be disrupted. To reduce risk, investors should keep a close eye on how interest rates and foreign exchange rates are rising.

The study covers a long period of time, which the majority of previous work did not consider. Furthermore, the study uses different dummy variables in the ARDL model to represent structural breaks (as determined by the Bai–Perron multiple break point test).

The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

The purpose of this research is to study the relationship between exchange rate fluctuations and stock market returns of the seven highest economic performing emerging countries (E7).

The study is conducted using the daily data for exchange rates and stock market returns in each of the E7 countries from January 1, 2019, to January 1, 2022. The study employs the ordinary least squares, autoregressive distributed lag error correction regression and generalized autoregressive conditional heteroskedasticity (GARCH (1,1)) regression models to fully investigate the impact of exchange rate on stock markets. For further investigation, the GARCH (1,1) model is run twice for each country with and without the inclusion of exchange rate to determine its effect on the volatility of stock returns.

The findings support the presence of cointegration relationship between the variables for all countries. The results reveal significant positive long-run relationship between exchange rates and stock market returns in all countries except for Indonesia, which evidenced a significant negative impact. The results of the GARCH (1,1) add that the inclusion of exchange rate in the model accounts for a slight change in the volatility of stock returns.

The research provides empirical evidence that appreciating currencies are perceived positively by investors leading to better performing capital markets. The outcomes of this study may assist policy makers in understanding to what degree changes in exchange rates can influence capital markets, as well as narrow the gap in literature regarding which theory is more relevant in explaining how exchange rate fluctuations impact market values.

The aim of this paper is to make a descriptive exploratory effort to discern the role of IT exports in India's macro-economic indicators, like national income, employment and balance of payment in the post-Liberalization, Privatization and Globalization strategy in the 1990s. The paper also explores the vital historical developments of various dimensions of IT, such as its export growth, major software and services exports destinations, compositions of IT exports and domestic growth in India.

This study is based on secondary data, which were collected from Balance of Payment Statistics Reserve Bank of India (RBI) and Handbook of Statistics on Indian Economy, National Association of Software and Service Companies (NASSCOM),rtd and Department of Electronics and Information Technology (DEITY). This study has used descriptive analysis and growth models for studying the objectives. Major IT sector dimensions, such as total output, exports revenue, domestic revenue, gross domestic product, employment and exports of the software and service industry, have been examined for the period 1991–2016.

The findings suggest that over the last 26 years, the information technology industry's economic footprint has extended by more than seven times. Over the same period, direct employment in the information technology sector increased at an average growth rate of around 17%. Software and services exports earn, on average, about three times greater than the other three major services of India's current account of the balance of payment.

This study focuses on originality in examining the role of IT exports in India's macro-economic indicators economic reforms of the 1990s and also explores the historical developments of various dimensions of IT exports and domestic growth in India. All the work has been done in original by the authors, and the work used has been acknowledged properly.

Credit default swaps (CDSs) are among the most widely used credit derivatives since their innovation and designed to hedge the credit risk of reference entities. They were exposed after the global financial crisis of 2007–08, and were blamed for its occurrence. This paper aims to describe the fundamental mechanism of CDSs, demonstrating how a CDSs contract works. Further, this study explores the growth of the global and Indian CDS market by taking a holistic perspective.

An objective-driven descriptive research design is adopted to achieve a rigorous and accurate analysis of the study. Therefore, research papers from high-impact journals have been carefully reviewed to achieve the aim of the study.

The study shows that CDSs are still in their infancy in India. Banks are the primary market makers and users in the Indian CDSs market; therefore, regulatory authorities must assist them to boost the market. For banks to become more confident, they should gain experience and knowledge from other active CDSs markets around the world.

This study attempts to provide insights into the current state of the global as well as the Indian CDS market. Further, this study suggests approaches for the Indian banking sector to play an active role in the Indian CDSs market.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This paper aims to investigate simultaneously the causality and the dynamic links between exchange rates and stock market indices. It attempts to identify the short- and long-term effect of the US dollar on major stock market indices of Brazil, Russia, India, China and South-Africa (BRICS) nations.

This paper applies a new methodology combining the panel generalized method of moments model and the panel auto-regressive distributed lag (ARDL) method to investigate the existence of a causal short-/long-run relationships and dynamic dependence among all stock market returns and exchanges rates changes of BRICS countries.

Results show that exchange rate changes have a significant effect on the past and the current volatility of the BRICS stock indices. Besides, ARDL estimations reveal that exchange rate movements have a significant effect on short- and long-term stocks market indices of all BRICS countries

The findings have implications for policymakers and market participants who try to manage the exchange rate will have a different dose of intervention if they know that the effects of currency depreciation are different than appreciation. These results have important implications that investors should take into account in frequency-varying exchange rates and stock returns and regulators should consider developing sound policy measures to prevent financial risk.