This paper investigates the hedging effectiveness of the S&P CNX Nifty index futures by employing four competing models, viz., the simple Ordinary Least Squares (OLS) method, the Bivariate Vector Autoregressive (BVAR) model, the Vector Error Correction Model (VECM), and the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) with error correction model. The hedge performances obtained from the different econometric models for the in-sample and out-of-sample periods are compared in terms of variance minimization criterion.

Derivatives in emerging markets possess most important economic functions such as price discovery, portfolio diversification and hedging against the adverse price movements. It mainly acts as a risk management tool in reducing risk and leading to higher returns through hedging process. Hedging with futures contracts is perhaps the simplest method for managing market risk arising from adverse movements in the price of various assets. Hedgers usually short an amount of futures contracts if they hold the long position of the underlying assets and vice versa. An important question is how many futures contracts are needed. In other words, investors have to decide on the optimal hedge ratio, that is, how many futures contracts should be held for each unit of the underlying asset, as well as the effectiveness measure of that ratio. The hedge ratio is defined by Hull (2003, p. 750) as "the ratio of the size of the portfolio taken in futures contracts to the size of the exposure." The hedge ratio provides information on how many futures contracts should be held, whereas its effectiveness evaluates the hedging performance and the usefulness of the strategy. In addition, the hedgers may use the effectiveness measure to compare the benefits of hedging a given position from many alternative futures contracts.

The earlier form of hedge ratio is the 1:1 hedge or the naïve strategy. This strategy suggests that an investor who has a long position in the spot market should sell a unit of futures today and buy it back when he sells the spot. Hence, the Optimal Hedge Ratios (OHRs) of the naïve model are always one. This strategy represents the perfect hedge since it assumes that both spot and futures prices change by the same amount at all times. However, the strategy failed due to the existence of market frictions such as transaction costs, margin requirements, short-sale constraints, liquidity differences and non-synchronous trading effects which may induce the futures and spot prices to behave differently. This has brought a renewed interest at the theoretical level by the works of Working (1953), Johnson (1960), Stein (1961) and Ederington (1979). However, the objectives of hedging have proved controversial.

Applied Finance Journal, Time-Varying Hedge Ratios, Indian Stock Index Futures Market, National Stock Exchange, Vector Error Correction Model, Emerging Markets, Optimal Hedge Ratios, Generalized Autoregressive Conditional Heteroscedasticity, Error Correction Mechanism, Traditional Regression Method, Econometric Model.