A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study finds optimal boundaries for hedging a perpetual American put option.
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
Discrete time hedging in a complete diffusion market is considered. The hedge portfolio is rebalanced when the absolute difference between delta of the hedge portfolio and the derivative contract reaches a threshold level. The rate of convergence of the expected squared hedging error as the threshold level approaches z…
We study option pricing and hedging with uncertainty about a Black-Scholes reference model which is dynamically recalibrated to the market price of a liquidly traded vanilla option. For dynamic trading in the underlying asset and this vanilla option, delta-vega hedging is asymptotically optimal in the limit for small u…
Deep Hedging removes drift for cleaner option pricing.
Study optimizes Bitcoin futures hedging to reduce liquidation risk.
We consider fractional Black-Scholes market with proportional transaction costs. When transaction costs are present, one trades periodically i.e. we have the discrete trading with equidistance between trading times. We derive a non trivial hedging error for a class of European options with convex payoff in the…
We consider a financial market where stocks are available for dynamic trading, and European and American options are available for static trading (semi-static trading strategies). We assume that the American options are infinitely divisible, and can only be bought but not sold. In the first part of the paper, we work w…
Paper presents a machine learning algorithm for hedging ETF options, outperforming static hedging methods.
Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.
QLBS and RLOP methods improve option pricing and hedging performance.
Study on hedging CVA in jump-diffusion setting using Monte Carlo simulations.
The paper solves a utility-based hedging problem with quadratic costs.
Study examines hedging options on asset portfolios against one underlying asset with transaction costs.
This thesis proposes a derivatives hedging framework using deep learning and reinforcement learning.
A new hedging strategy uses deep reinforcement learning to manage gamma and vega risks.
A risk-neutral valuation framework is developed for pricing and hedging in-play football bets based on modelling scores by independent Poisson processes with constant intensities. The Fundamental Theorems of Asset Pricing are applied to this set-up which enables us to derive novel arbitrage-free valuation formulæ for c…
Paper solves trade-off between internalisation and externalisation in stochastic trade flows.
This paper presents hedging strategies for European and exotic options in a Levy market. By applying Taylor's Theorem, dynamic hedging portfolios are con- structed under different market assumptions, such as the existence of power jump assets or moment swaps. In the case of European options or baskets of European optio…
This paper improves financial derivative pricing by incorporating multiple hedging instruments.
In this paper the zero vanna implied volatility approximation for the price of freshly minted volatility swaps is generalised to seasoned volatility swaps. We also derive how volatility swaps can be hedged using a strip of vanilla options with weights that are directly related to trading intuition. Additionally, we der…
Enhances hedging strategies using deep neural networks.
New algorithm reduces training time for deep learning in financial hedging.
Bank behaviour is important for pricing XVA because it links different counterparties and thus breaks the usual XVA pricing assumption of counterparty independence. Consider a typical case of a bank hedging a client trade via a CCP. On client default the hedge (effects) will be removed (rebalanced). On the other hand, …
HedgeAgents boosts financial trading with balanced strategies.
We price and hedge American options robustly in continuous time.
Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or hedging costs to desks on an…
Develops a hedging method for multi-asset derivatives with correlation risk.
We consider the fundamental theorem of asset pricing (FTAP) and hedging prices of options under non-dominated model uncertainty and portfolio constrains in discrete time. We first show that no arbitrage holds if and only if there exists some family of probability measures such that any admissible portfolio value proces…
Unified framework for CVA sensitivities, hedging, and risk assessment.
The paper introduces and studies hedging for game (Israeli) style extension of swing options considered as multiple exercise derivatives. Assuming that the underlying security can be traded without restrictions we derive a formula for valuation of multiple exercise options via classical hedging arguments. Introducing t…
This article considers the pricing and hedging of a call option when liquidity matters, that is, either for a large nominal or for an illiquid underlying asset. In practice, as opposed to the classical assumptions of a price-taking agent in a frictionless market, traders cannot be perfectly hedged because of execution …
AI stocks hedge against AI singularity's economic impact.
Deep Hedging learns risk-neutral vol dynamics for option pricing.
Duality for robust hedging with proportional transaction costs of path dependent European options is obtained in a discrete time financial market with one risky asset. Investor's portfolio consists of a dynamically traded stock and a static position in vanilla options which can be exercised at maturity. Both the stock …
Since most of the traded options on individual stocks is of American type it is of interest to generalize the results obtained in semi-static trading to the case when one is allowed to statically trade American options. However, this problem has proved to be elusive so far because of the asymmetric nature of the positi…
Develops a machine-learning framework for optimal share repurchase hedging.
Study optimal semi-static hedging for illiquid markets using dynamic cash and static quoted derivatives.
We consider the super-hedging price of an American option in a discrete-time market in which stocks are available for dynamic trading and European options are available for static trading. We show that the super-hedging price is given by the supremum over the prices of the American option under randomized models. T…
We consider a financial model with permanent price impact. Continuous time trading dynamics are derived as the limit of discrete rebalancing policies. We then study the problem of super-hedging a European option. Our main result is the derivation of a quasi-linear pricing equation. It holds in the sense of viscosity so…
Value adjustment of uncollateralized trades is determined within a risk-neutral pricing framework. When hedging such trades, investors cannot freely trade protection on their own name, thus facing an incomplete market. This fact is reflected in the non-uniqueness of the pricing measure, which is only constrained by the…
An investor with constant absolute risk aversion trades a risky asset with general Itô-dynamics, in the presence of small proportional transaction costs. In this setting, we formally derive a leading-order optimal trading policy and the associated welfare, expressed in terms of the local dynamics of the frictionless op…
Study on hedging and valuation of basis risk in incomplete markets with partial information.
Study compares model-free valuation to actual financial outcomes, finds it slightly conservative.
Optimal hedging strategies for exotic options using vanilla options.
The problem of quantile hedging for basket derivatives in the Black-Scholes model with correlation is considered. Explicit formulas for the probability maximizing function and the cost reduction function are derived. Applicability of the results for the widely traded derivatives as digital, quantos, outperformance and …
Shorting IG ETFs can hedge bond portfolios during market drawdowns effectively.