Browsing by Author "Sapna, S."

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A Synergetic Approach to Ethereum Option Valuation Using XGBoost and Soft Reordering 1D Convolutional Neural Networks
(Springer, 2025) Sapna, S.; Mohan, B.R.
In an ever-evolving realm of cryptocurrencies, Ethereum has emerged as a prominent player, captivating both investors and enthusiasts alike. Within the diverse financial landscape of cryptocurrencies, options stand out as a versatile tool, offering flexibility and hedging opportunities. This paper introduces a cutting-edge approach to pricing Ethereum options, harnessing the formidable power of XGBoost and the visionary capabilities of Convolutional Neural Networks (CNN). This research proposes a novel method that utilizes XGBoost for implied volatility estimation by integrating historical volatility, and generalized auto-regressive conditional heteroscedasticity (GARCH) model-predicted volatility. Subsequently, a soft reordering 1-dimensional CNN (1D-CNN) model is employed to enhance the pricing accuracy of Ethereum options. The soft reordering mechanism is used to dynamically rearrange the initial tabular dataset, optimizing it for enhanced learning within the CNN framework. The outcome indicates the ability of the proposed model in estimating implied volatility and pricing options with remarkable accuracy, outperforming traditional option pricing models and data-driven models documented in literature. The proposed model also exhibits the lowest pricing error across all maturities and various moneyness criteria, with the exception of long term put and deep out of the money (DOTM) options. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Comparative Analysis of Root Finding Algorithms for Implied Volatility Estimation of Ethereum Options
(Springer, 2024) Sapna, S.; Mohan, B.R.
In this paper, a comparative analysis of traditional and hybrid root finding algorithms is performed in estimating implied volatility for Ethereum Options using the Black–Scholes model. Results indicate the efficiency of Newton–Raphson method in terms of algorithmic convergence as well as computational time. Since Newton–Raphson method may not always lead to convergence, the best approximation technique is chosen from the convergent bracketed methods. The hybrid Bisection–Regula Falsi method serves as the best choice for root estimation among the bracketed methods under consideration. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Estimation of Implied Volatility forÂ Ethereum Options Using Numerical Approximation Methods
(Springer Science and Business Media Deutschland GmbH, 2023) Sapna, S.; Mohan, B.R.
This study demonstrates the use of numerical approximation techniques like Newton-Raphson Method, Bisection Method, Brent Method, and Secant Method to estimate the market implied volatility for short-dated Ethereum options with 21-day maturity, obtained from Deribit Crypto Options and Futures Exchange. The numerical approximation techniques are compared based on their convergence and time taken for execution. It is found that Newton-Raphson Method converges faster and performs computation in the least time in comparison to the other methods under consideration. This study further focuses on the determination of implied volatility structure for short maturity Ethereum options. The results show that the implied volatility assumes a deep smile far from the day of expiry and as we approach the expiry date, the volatility smile broadens. To the best of our knowledge, this is the first work to use approximation techniques to estimate the implied volatility for Ethereum options. Â© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Futures Options Pricing in the Indian Commodity Market Using Univariate GARCH Models and Particle Swarm Optimization
(Springer, 2025) Sapna, S.; Mohan, B.R.
In the ever-changing commodity markets, accurate futures option pricing is critical for investors and traders for managing risks and making sound decisions. Traditional models like Black-76, often fall short in capturing the complex volatility patterns observed in these markets because of the constant volatility assumption. Furthermore, while the generalized autoregressive conditional heteroskedasticity (GARCH) model effectively captures time-varying volatility, its estimation of parameters using maximum likelihood estimation (MLE) with gradient based methods, is highly sensitive to initial parameter values and can suffer from issues like local minima, leading to poor estimates. To address these issues, this study combines Particle Swarm Optimization (PSO) algorithm with GARCH model to improve the parameter estimation process. PSO algorithm explores the parameter space dynamically, allowing for a better fitting to the underlying dynamics of market volatility. This paper uses several variants of GARCH models, such as GARCH, GJR-GARCH, and E-GARCH to account for different features of volatility behavior. Empirical analysis shows that the PSO-variant is better than the Black-76 model and MLE-variant for all GARCH models in terms of pricing accuracy. Among the PSO-variants, the PSO-GARCH model provides the best option pricing fit to the Indian commodity market. These findings highlight the practical significance of PSO-enhanced GARCH models in emerging markets, providing reliable and adaptable tools for precise option pricing and effective volatility estimation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Univariate GARCH Model for Futures Option Pricing: Application to Silver Mini Futures in Indian Commodity Market
(Science and Technology Publications, Lda, 2024) Sapna, S.; Mohan, B.R.
This research investigates the pricing of options related to silver commodity futures within the Indian market, employing a standard univariate Generalized Autoregressive Conditional Heteroscedastic (GARCH) model with a symmetric normal distribution for return modelling. The study evaluates the performance of this option pricing model specifically for silver mini futures options traded on the Multi Commodity Exchange. Furthermore, it compares the option prices determined using the GARCH model parameters with those calculated using the Black-76 model. The findings demonstrate that the option prices derived from the GARCH model fall consistently within the bid-ask price range and significantly outperform the Black-76 model in terms of option pricing accuracy. This underscores the practical utility of GARCH models in the context of the Indian commodity market. To the best of our knowledge, this research marks the pioneering attempt to incorporate parameters generated by the GARCH model for futures option pricing within the Indian commodity market. Â© Â© 2024 by SCITEPRESS - Science and Technology Publications, Lda.
YOLOv5 Model-based Ship Detection in High Resolution SAR Images
(Institute of Electrical and Electronics Engineers Inc., 2023) Sapna, S.; Sandhya, S.; Shetty, R.D.; Pais, S.M.; Bhattacharjee, S.
Detection of ships in Synthetic Aperture Radar (SAR) images play a crucial role in maritime surveillance, most importantly under complex sea conditions. SAR permits observation in any weather conditions, at all hours of the day and night. At present, the ship detection from SAR images is a notable area of research since it is very difficult to detect the ships in the SAR images using traditional object or target detection algorithms. In this work, a You Only Look Once version 5 (YOLOv5) based ship detection model from SAR images with faster training speed and higher accuracy is implemented and tested. This model achieved a mean average precision (mAP) of 96.2% with a training time of 8.63 hours. This work also provides a comparative analysis with the existing methods for detection of ships in SAR images. The comparison shows that the YOLOv5 based model performs better in terms of both mean average precision and training time when compared to the existing models. Â© 2023 IEEE.