This article examines the impact of capital formation on economic growth in India covering the period from 1970 to 2012. This paper traces a long-run equilibrium relation between capital formation and economic growth and other control variables by using autoregressive distributed lag (ARDL) model. The error correction (ECM) model shows that the capital formation, trade openness, exchange rate and total factor productivity positively affect the economic growth and the inflation negatively affects the economic growth in the short run. It is recommended that government increases the level of capital formation in order to achieve a higher level of economic growth.

No abstract

No abstract

No abstract

The paper presents the comparative study of the nature of stock markets in shortterm and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time series before structural break (TSB) and time series after structural break (TSA). The empirical mode decomposition based Hurst exponent and variance techniques have been applied to the TSO, TSB and TSA to identify the time scales in shortterm and long-term from the decomposed intrinsic mode functions. We found that for TSO, TSB and TSA the short-term time scales and long-term time scales are within the range of few days to 3 months and greater than 5 months respectively, which indicates that the short-term and long-term time scales are present in the stock market. The Hurst exponent is ∼ 0.5 and ≥ 0.75 for TSO, TSB and TSA in short-term and long-term respectively, which indicates that the market is random in short-term and strongly correlated in long-term. The identification of time scales at short-term and long-term investment horizon will be useful for investors to design investment and trading strategies.Investors adopt different strategies in short-term and long-term depending on the investment time horizon as stock markets show different dynamics in different time scales. Hence identification of time scales in short-term and long-term is very important. The time scales have been identified using the empirical mode decomposition based Hurst exponent and normalised variance technique. The robustness of the analysis is further confirmed with Zivot and Andrews structural trend break model. Stock markets show random nature in short-term with time scales ranging from few days to nearly 3 months, and in long-term it shows long-range correlation with time scales greater than 5 months. We hope that these results may help the investors to take better decision on devising short-term and long-term trading strategies.

No abstract

No abstract

This paper examines the linear and nonlinear relationship between daily confirmed COVID-19 cases and sectoral stock market volatility in India. The linear Granger causality test reveals bidirectional causality. Further, we observe that bidirectional nonlinear Granger causality exists between stock market volatility and COVID-19. This implies that the historical and lagged information can have a significant role in predicting COVID-19 cases and the stock market.