2 research outputs found
Forecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network
A large part of the workforce, and growing every day, is originally from
India. India one of the second largest populations in the world, they have a
lot to offer in terms of jobs. The sheer number of IT workers makes them a
formidable travelling force as well, easily picking up employment in English
speaking countries. The beginning of the economic crises since 2008 September,
many Indians have return homeland, and this has had a substantial impression on
the Indian Rupee (INR) as liken to the US Dollar (USD). We are using
numerational knowledge based techniques for forecasting has been proved highly
successful in present time. The purpose of this paper is to examine the effects
of several important neural network factors on model fitting and forecasting
the behaviours. In this paper, Artificial Neural Network has successfully been
used for exchange rate forecasting. This paper examines the effects of the
number of inputs and hidden nodes and the size of the training sample on the
in-sample and out-of-sample performance. The Indian Rupee (INR) / US Dollar
(USD) is used for detailed examinations. The number of input nodes has a
greater impact on performance than the number of hidden nodes, while a large
number of observations do reduce forecast errors.Comment: 12 Pages, 3 Figures, ISSN:2230-9616(Online);2231-0088(Print
Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework
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