  Any discussion on exchange rate movements and forecasting should include
explanatory variables from both the current account and the capital account of
the balance of payments. In this paper, we include such factors to forecast the
value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting
political instability and lack of mechanism for enforcement of contracts that
can affect both direct foreign investment and also portfolio investment, have
been incorporated. The explanatory variables chosen are the 3 month Rupee
Dollar futures exchange rate (FX4), NIFTY returns (NIFTYR), Dow Jones
Industrial Average returns (DJIAR), Hang Seng returns (HSR), DAX returns (DR),
crude oil price (COP), CBOE VIX (CV) and India VIX (IV). To forecast the
exchange rate, we have used two different classes of frameworks namely,
Artificial Neural Network (ANN) based models and Time Series Econometric
models. Multilayer Feed Forward Neural Network (MLFFNN) and Nonlinear
Autoregressive models with Exogenous Input (NARX) Neural Network are the
approaches that we have used as ANN models. Generalized Autoregressive
Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive
Conditional Heteroskedastic (EGARCH) techniques are the ones that we have used
as Time Series Econometric methods. Within our framework, our results indicate
that, although the two different approaches are quite efficient in forecasting
the exchange rate, MLFNN and NARX are the most efficient.
