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Enhancing Black-Scholes Delta Hedging via Deep Learning
This paper proposes a deep delta hedging framework for options, utilizing neural networks to learn the residuals between the hedging function and the implied Black-Scholes delta. This approach leverages the smoother properties o ... Read More >
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CVA Sensitivities, Hedging and Risk
We present a unified framework for computing CVA sensitivities, hedging the CVA, and assessing CVA risk, using probabilistic machine learning meant as refined regression tools on simulated data, validatable by low-cost companion ... Read More >
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Multilevel Monte Carlo in Sample Average Approximation: Convergence, Complexity ...
In this paper, we examine the Sample Average Approximation (SAA) procedure within a framework where the Monte Carlo estimator of the expectation is biased. We also introduce Multilevel Monte Carlo (MLMC) in the SAA setup to enha ... Read More >
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Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow
Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. This paper explores fine-tuning LLMs for stock return forecasting with ... Read More >
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Automated Market Making and Decentralized Finance
Automated market makers (AMMs) are a new type of trading venues which are revolutionising the way market participants interact. At present, the majority of AMMs are constant function market makers (CFMMs) where a deterministic t ... Read More >
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On the Separability of Vector-Valued Risk Measures
Risk measures for random vectors have been considered in multi-asset markets with transaction costs and financial networks in the literature. While the theory of set-valued risk measures provide an axiomatic framework for assign ... Read More >
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Multi-Industry Simplex 2.0 : Temporally-Evolving Probabilistic Industry Classifi ...
Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk ... Read More >
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On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjus ...
The present work addresses the challenge of training neural networks for Dynamic Initial Margin (DIM) computation in counterparty credit risk, a task traditionally burdened by the high costs associated with generating training d ... Read More >
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Reinforcement Learning Pair Trading: A Dynamic Scaling approach
Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around $70 billion worth of crypto-currency is traded daily on exchanges. Trading crypto-currency is difficult due to the inherent volatility o ... Read More >
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Calibrating the Heston Model with Deep Differential Networks
We propose a gradient-based deep learning framework to calibrate the Heston option pricing model (Heston, 1993). Our neural network, henceforth deep differential network (DDN), learns both the Heston pricing formula for plain-va ... Read More >