Assessing Volatility Behaviors of Cross-Currency Derivatives in India's Exchange Markets Using Machine Learning Algorithms

Aman Shreevastava, Bharat Kumar Meher, Virgil Popescu, Ramona Birau and Mritunjay Mahato
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Aman Shreevastava: PG Department of Commerce and Management, Purnea University, Purnia, Bihar, India
Bharat Kumar Meher: Department of Commerce, D. S. College, Katihar, Bihar, India
Virgil Popescu: Faculty of Economics and Business Administration, University of Craiova, Romania
Ramona Birau: "Eugeniu Carada" Doctoral School of Economic Sciences, University of Craiova, Romania
Mritunjay Mahato: School of Commerce and Management, Srinath University, India

Economics and Applied Informatics, 2024, issue 3, 146-155

Abstract: Currency Derivatives are very important financial instruments for speculation, hedging and arbitrage opportunities, and among them cross-country futures are one of the important types with a huge research gap. Studying them becomes very imperative. This paper studies the volatility of INR based cross country futures (USD, JPY and EUR) and performs forecasting using ML Algorithm and utilizes LSTM for prediction. The study proves to be a first of its kind study involving cross-country futures and is a beacon of hope for all future research on similar subjects. The study will also be helpful to investors and foreign exchange managers along with monetary and fiscal policymakers. The study consists of total of 674 data points of near-month expiry futures expiring on 29th October, 2024. The span of data was 1 year for JPY and EUR and nearly 11 months for USD. The data were downloaded from NSE website. The USD-INR futures were nearly stable and EUR-INR futures were most volatile. The JPY-INR futures had highest rise in price trends. Prediction of USD/INR future outperformed other two with least error. However, LSTM model that was trained, relatively underperformed in case of JPY-INR.

Keywords: Cross-Currency Derivatives; Futures; Machine Learning (ML) Algorithms; LSTM; Volatility; Neural Network; Forecasting (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ddj:fseeai:y:2024:i:3:p:146-155

DOI: 10.35219/eai15840409439

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