RELATIONSHIP BETWEEN SPOT AND FUTURE MARKETS (VECTOR ERROR CORRECTION MODEL)

Abstract

The present study attempts to investigate long and short run causality between spot price and future prices of selected Stocks in India. The data looks at the spot and future daily closing prices of TCS, MindTree, Cipla and Dr Reddy. We found strong evidence of cointegration between the daily spot and one-month futures prices of the stocks. After identifying single cointegration vector between spot and future prices of the selected Stocks, the Vector Error Correction Model (VECM) was employed to examine the causal nexus between future and spot market of the selected stocks. Finally, VECM model and Wald test are used to measure long run as well as short run causality among these four commodities .The evidence shows that future leads to spot in case of TCS. Objectives of the project In this report efforts are made to examine the causal nexus between future and spot price of the selected four stocks (TCS, MindTree, Cipla and Dr Reddy). It tries to identify the relationship between spot and future markets price, that who plays a leading role in price discovery in long run and Short Run. Data Collection and Analysis Methods The near month futures contract has been used for the study as they are heavily traded as compared to next month(1month) and far month(2-3 months) future contracts. All the required data information for the study has been retrieved from the National Stock Exchange of India(NSE) website. As a preliminary investigation, Augmented Dickey Fuller tests was employed to test the stationarity of spot and future price series of selected stocks. And then Johansen’s Cointegration test is performed to examine the long-run relationship between spot and future markets of selected stocks. After identifying single cointegration vector between spot and future prices of the selected stocks , the Vector Error Correction Model (VECM) was employed to examine the causal nexus between future and spot market of the stocks .Besides, the vector error correction model is sensitive to the selection of optimal lag length and the necessary lag length of future and spot price series for the selected stocks are determined by the Schwarz Information Criterion (SC).