Modelling volatility and forecasting of exchange rate of British pound sterling and Indian rupee
| Autor: | Sanjeev Gupta, Sachin Kashyap |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Heteroscedasticity
050208 finance Financial economics Strategy and Management Autoregressive conditional heteroskedasticity 05 social sciences General Decision Sciences Rupee Management Science and Operations Research Exchange rate Volatility swap 0502 economics and business Economics Econometrics Forward volatility 050207 economics Volatility (finance) Pound Sterling |
| Zdroj: | Journal of Modelling in Management. 11:389-404 |
| ISSN: | 1746-5664 |
| DOI: | 10.1108/jm2-04-2014-0029 |
| Popis: | Purpose The paper aims to analyse the extent of volatility and generating forecasts of exchange rates of British pound and Indian rupees in US terms. Design/methodology/approach This study applies different combinations of GARCH and EGARCH models suggested in the Econometric literature to capture the extent of volatility. The forecast of exchange rates of British Pound and Indian Rupees in US terms are generated applying artificial neural network (ANN) technique using different combination of networks with hyperbolic tangent function at hidden and output stage of the model. Findings The presence of volatility depicts that there is noise and chaos in the forex market. Prediction of exchange rate of the respective currencies underscores that exchange rates will increase marginally in near future. Practical Implications The results proposed in this study will be benchmark for the hedgers, investors, bankers, practitioners and economists to foresee the exchange rate in the presence of volatility and design policies accordingly. Originality/value In literature, no study has applied ANN for forecasting exchange rate after measuring the extent of volatility. The present study is a unique contribution in the existing pool of literature to forecasts the concerned variable(s) after ascertaining the noise and chaos in the data by applying GARCH family models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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