The paper analyzes the relation between the movement of Indian rupee and movement of BSE IT sector index over the five-year calendar period (2009-2013) by taking monthly closing returns over the study period. The BSE IT Index is a sector-specific index of BSE and includes leading IT companies from India. The tools used for the purpose of studying this relation include Granger Causality (F Wald) restricted-unrestricted approach test which has been done bilaterally, Dickey-Fuller tests for stationarity of variables, Engle Granger Cointegration and Vector Error Correction Model to establish the long-term relation between the variables and variance decomposition of the error term. The model uses an autoregressive regression and has a number of lags and therefore optimal lag length has been determined using AIC and SIC criteria. The results of the study clearly show that BSE IT Index is causing movement in rupee, however, this is only from 4th lag onwards. On the other hand, the results could not establish a reverse relation or rupee causing movement in BSE IT Index for the period of study. The AIC and SIC results for model testing showed that the 4 lag model was the most appropriate model under study out of the 8 lags that were considered. The coefficient of the error term in ECM regression was negative, showing stable long-run equilibrium. Moreover, the result also showed that adjustment mechanism was backwards.

The financial markets of today are witnessing capital movements which has resulted in integration of domestic markets with international markets. Any news originating in any part of the world is quickly assimilated and reflected through prompt reaction in international financial markets thereby indicating that the linkages between different financial markets around the world are growing stronger day by day (Garg et al., 2012).

Many researchers have tried to study these inter-linkages, a lot of studies have focused on linkages between same segment of the financial markets, especially stock markets, wherein they tried to relate the movement of different stock indices (Garg et al., 2012; and Aanchal et al., 2014, etc.). On the other hand, there has also been considerable research on spillover effect from one market to another, and one such area is the relation between the stock indices and currency movements. This spillover effect has actually gained much importance particularly after the East Asian Crisis of 1997 when the world witnessed a collapse of both these markets (Bagchi, 2014). According to Stavarek (2004), the relation between stock market movements and currency movements follows a portfolio approach. An increase in the domestic stock markets results in investors’ shifting their portfolio positions from foreign to domestic securities which increases demand for domestic currency, resulting in its appreciation. Thus, what we see is a one-way causality: from stock prices to currency movements. Volatility spillovers from one market to another have been studied by many researchers, Fedorova and Saleem (2010) found strong spillover impact in East European Markets reflecting interdependence of stock and currency markets. Cumperayot et al. (2006) showed how an extreme stock market decline did result in increase in the probability of currency depreciation to the extreme within a short period of time. Sheng and Doong (2004) proved that volatility transmission effect was from stock prices influencing exchange rate movements, but not vice versa. However, Fedorova and Saleem (2010) proved it otherwise, i.e., in Emerging European stock markets, volatility spillovers occurred from currency to stock markets. Baig and Goldfajn (1998) showed that during financial market instability, market participants across countries tend to move together and shocks from one market are readily transmitted to other markets, thus adding to substantial instability.

Applied Finance Journal, Inter-Linkages, Indian Rupee, BSE Sector-Specific (IT), Granger Causality (F Wald), Investigation.