Michał Barski, Jacek Jakubowski, Jerzy Zabczyk
The completeness of a bond market model with infinite number of sources of randomness on a finite time interval in the Heath-Jarrow-Morton framework is studied. It is proved that the market is not complete. A construction of a bounded contingent claim, which can not be replicated, is provided.
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We introduce a theory of stochastic integration with respect to a family of semimartingales depending on a continuous parameter, as a mathematical background to the theory of bond markets. We apply our results to the problem of super-replication and utility maximization from terminal wealth in a bond market. Finally, we compare our approach to those already existing in literature.
This papers addresses the stock option pricing problem in a continuous time market model where there are two stochastic tradable assets, and one of them is selected as a numéraire. It is shown that the presence of arbitrarily small stochastic deviations in the evolution of the numéraire process causes significant changes in the market properties. In particular, an equivalent martingale measure is not unique for this market, and there are non-replicable claims. The martingale prices and the hedging error can vary significantly and take extreme values, for some extreme choices of the equivalent …
BondBERT: What we learn when assigning sentiment in the bond market
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In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for a large number of bonds to asset managers from all around the globe. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. Useful models exist, most of them inspired by that of Avellaneda and Stoikov. These models describe the complex optimization problem faced by market makers: proposing bid and ask prices in an optimal way for making money out of the difference between bid and ask prices while mitig…
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CATNet通过几何深度学习方法，利用图结构建模CAT债券市场，提升预测性能并揭示网络特性对价格的影响。
Defaultable bond liquidity spread estimation: an option-based approach
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Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective
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Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning
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Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective
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Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
本文提出利用因果GANs和SAC强化学习生成高保真的合成债券收益率数据，结合LLM评估提升预测性能，通过统计验证和专家评估证明其有效性。
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