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.
Enhanced options trading strategies using advanced portfolio optimization.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.
Study evaluates hedging strategies for S&P500 index options.
Improved option pricing model with transaction costs.
Paper optimizes stock option forecasting using ML models and improved trading strategies.
Enhanced indexation uses equity and index options for better performance.
Optimal hedging strategies for exotic options using vanilla options.
Optimizes credit index option hedging with reinforcement learning.
DeltaHedge uses AI to optimize portfolio options trading.
New approach minimizes tail risk in option hedging.
The paper considers an investment timing problem appearing in real options theory. Present values from an investment project are modeled by general diffusion process. We prove necessary and sufficient conditions under which an optimal investment time is induced by threshold strategy. We study also the conditions of opt…
Algorithm for hedging American options with transaction costs.
We study the problem of option pricing and hedging strategies within the frame-work of risk-return arguments. An economic agent is described by a utility function that depends on profit (an expected value) and risk (a variance). In the ideal case without transaction costs the optimal strategy for any given agent is fou…
The paper analyzes strategic irreversible investments with novel dynamic strategies.
Study optimizes option pricing with robust strategies, ensuring consistency with vanilla option prices.
Study liquidity impact on spread option pricing.
We prove existence of a self-financing strategy which minimizes shortfall for game options in discrete time
Proposes a neural network for efficient deep hedging strategies.
Recent years have seen an emerging class of structured financial products based on options linked to dynamic asset allocation strategies. One of the most chosen approach is the so-called target volatility mechanism. It shifts between risky and riskless assets to control the volatility of the overall portfolio. Even if …
Under the optimal withdrawal strategy of a policyholder, the pricing of variable annuities with Guaranteed Minimum Withdrawal Benefit (GMWB) is an optimal stochastic control problem. The surrender feature available in marketed products allows termination of the contract before maturity, making it also an optimal stoppi…
This study deals with the problem of pricing European currency options in discrete time setting, whose prices follow the fractional Black Scholes model with transaction costs. Both the pricing formula and the fractional partial differential equation for European call currency options are obtained by applying the delta-…
The purpose of this note is to reconcile two different results concerning the model-free upper bound on the price of an American option, given a set of European option prices. Neuberger (2007, `Bounds on the American option') and Hobson and Neuberger (2016, `On the value of being American') argue that the cost of the c…
In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate …
Study evaluates three position sizing methods for put-writing on S&P 500 Index options.
Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that underva…
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
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…
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
Deep learning improves options trading without market assumptions.
This paper examines pricing and hedging strategies for cross-currency equity protection swaps.
Paper analyzes liquidity for everlasting options in DeFi, offering strategies to reduce costs.
We construct algorithms for computation of prices and superhedging strategies for game options in general discrete markets both from the seller and the buyer points of view.
Novel signature approach for pricing and hedging path-dependent options with market frictions.
Neural-SDE models improve option hedging with lower errors and robustness.
Proposes deep hedging for index options using implied volatility surface.
Semi-static trading strategies make frequent appearances in mathematical finance, where dynamic trading in a liquid asset is combined with static buy-and-hold positions in options on that asset. We show that the space of outcomes of such strategies can have very poor closure properties when all European options for a f…
This paper analyzes hedge errors in Black-Scholes models using finite difference techniques.
In this paper we study the existence of an optimal hedging strategy for the shortfall risk measure in the game options setup. We consider the continuous time Black--Scholes (BS) model. Our first result says that in the case where the game contingent claim (GCC) can be exercised only on a finite set of times, there exis…
We analyze the errors arising from discrete readjustment of the hedging portfolio when hedging options in exponential Levy models, and establish the rate at which the expected squared error goes to zero when the readjustment frequency increases. We compare the quadratic hedging strategy with the common market practice …
American put options are among the most frequently traded single stock options, and their calibration is computationally challenging since no closed-form expression is available. Due to the higher flexibility in comparison to European options, the mathematical model involves additional constraints, and a variational in…
The pricing and hedging of a general class of options (including American, Bermudan and European options) on multiple assets are studied in the context of currency markets where trading is subject to proportional transaction costs, and where the existence of a risk-free numéraire is not assumed. Constructions leading t…
Paper uses deep learning to price and hedge options in incomplete markets.
A new hedging strategy uses deep reinforcement learning to manage gamma and vega risks.
A new mathematical model for the Black-Scholes equation is proposed to forecast option prices. This model includes new interval for the price of the underlying stock as well as new initial and boundary conditions. Conventional notions of maturity time and strike prices are not used. The Black-Scholes equation is solved…
I explicitly work out closed form solutions for the optimal hedging strategies (in the sense of Bouchaud and Sornette) in the case of European call options, where the underlying is modeled by (unbiased) iid additive returns with Student-t distributions. The results may serve as illustrative examples for option pricing …
Algorithm solves American options with regime-switching using multigrid and compact finite difference.
Study compares volatility models for Bitcoin, finds GARCH and EGARCH outperform.