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Kunal Bansal

Publications and source records attributed to Kunal Bansal.

2 recordsLinked to original sources
arXiv2024

Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing

This technical report presents a stochastic model for pricing weather derivatives and devising hedging strategies tailored to Indian markets. We model temperature dynamics using a modified Ornstein-Uhlenbeck process with jumps to account for sudden shocks, such as heatwaves and coldwaves. Historical data from 12 Indian states (1951-2023) is used for calibration, and Monte Carlo simulations are employed under the risk-neutral measure to price Heating Degree Days (HDD), Cooling Degree Days (CDD), and extreme event options. Sensitivity analysis reveals that a 20% increase in volatility leads to an approximate 4.2% increase in option prices, highlighting the critical impact of volatility on derivative pricing. Results show that HDD options in colder states like Himachal Pradesh are significantly more expensive, with prices reaching up to INR 684,693, while CDD options in hotter states like Gujarat are priced higher, up to INR 262,986. A comprehensive portfolio analysis indicates that investing INR 120,000 in HDD put options in Uttar Pradesh yields an expected payoff of INR 132,369, resulting in a return on investment (ROI) of 10.3%. Conversely, a similar investment in Karnataka yields a negative ROI of -66.7% due to its milder climate. Hedging strategies are tailored to each state's climatic risk, with recommendations to buy 90.66 HDD put options at a strike of 90.89 in Uttar Pradesh and invest in CDD call options in Gujarat. These insights offer practical solutions for managing temperature-related financial risk in energy and agriculture, providing actionable, state-specific hedging strategies for diverse climatic scenarios in India.

q-fin.RM↗
arXiv2019

On-Device User Intent Prediction for Context and Sequence Aware Recommendation

The pursuit of improved accuracy in recommender systems has led to the incorporation of user context. Context-aware recommender systems typically handle large amounts of data which must be uploaded and stored on the cloud, putting the user's personal information at risk. While there have been previous studies on privacy-sensitive and context-aware recommender systems, there has not been a full-fledged system deployed in an isolated mobile environment. We propose a secure and efficient on-device mechanism to predict a user's next intention. The knowledge of the user's real-time intention can help recommender systems to provide more relevant recommendations at the right moment. Our proposed algorithm is both context and sequence aware. We embed user intentions as weighted nodes in an n-dimensional vector space where each dimension represents a specific user context factor. Through a neighborhood searching method followed by a sequence matching algorithm, we search for the most relevant node to make the prediction. An evaluation of our methodology was done on a diverse real-world dataset where it was able to address practical scenarios like behavior drifts and sequential patterns efficiently and robustly. Our system also outperformed most of the state-of-the-art methods when evaluated for a similar problem domain on standard datasets.

cs.IR↗