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.
The study compares on-chain option prices with a model and finds significant differences.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
This paper designs a new on-chain option that amortizes perpetual options for blockchain environments.
This paper presents a new methodology to compute first-order Greeks for barrier options under the framework of path-dependent payoff functions with European, Lookback, or Asian type and with time-dependent trigger levels. In particular, we develop chain rules for Wiener path integrals between two curves that arise in t…
Method calculates Parisian stopping times and option prices using Markov chains.
In this article, we analyze the application of options contract in special commodity supply chain such as fresh agricultural products. This problem is discussed in the point of the retailer. When spot market and future market are both available, we discuss how the retailer chooses the optimal production. Furthermore, o…
In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the option such as the duration problem, the time non-homogeneity and the …
We create consistent option surfaces without arbitrage.
This paper presents a multinomial method for option pricing when the underlying asset follows an exponential Variance Gamma process. The continuous time Variance Gamma process is approximated by a discrete time Markov chain with the same firsts four cumulants. This approach is particularly convenient for pricing Americ…
LOV model calibrates European and American options with path-dependent volatility.
A new method for creating derivatives without oracles.
Efficient method for lookback option pricing under Markov models.
This paper investigates the pricing of European-style lookback options when the price dynamics of the underlying risky asset are assumed to follow a Markov-modulated Geo-metric Brownian motion; that is, the appreciation rate and the volatility of the underlying risky asset depend on unobservable states of the economy d…
Bayesian inference identifies model parameters from financial data to detect arbitrage opportunities.
We propose a hybrid tree-finite difference method in order to approximate the Heston model. We prove the convergence by embedding the procedure in a bivariate Markov chain and we study the convergence of European and American option prices. We finally provide numerical experiments that give accurate option prices in th…
We explore inverse and quanto inverse crypto options, their pricing, and applications.
We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting -component Gaussian mixture models to option quotes, where is a small integer (here 4 or 5). These densities are…
A model-free framework extracts risk-neutral densities from short-dated options.
In this paper we present an algorithm for pricing barrier options in one-dimensional Markov models. The approach rests on the construction of an approximating continuous-time Markov chain that closely follows the dynamics of the given Markov model. We illustrate the method by implementing it for a range of models, incl…
Cai, Song and Kou (2015) [Cai, N., Y. Song, S. Kou (2015) A general framework for pricing Asian options under Markov processes. Oper. Res. 63(3): 540-554] made a breakthrough by proposing a general framework for pricing both discretely and continuously monitored Asian options under one-dimensional Markov processes. In …
We characterize the price of an Asian option, a financial contract, as a fixed-point of a non-linear operator. In recent years, there has been interest in incorporating changes of regime into the parameters describing the evolution of the underlying asset price, namely the interest rate and the volatility, to model sud…
GG distribution improves option pricing for negatively skewed spot price distributions.
Paper extends Lévy models with memory to better price FX double barrier options.
We present an approach for pricing European call options in presence of proportional transaction costs, when the stock price follows a general exponential Lévy process. The model is a generalization of the celebrated work of Davis, Panas and Zariphopoulou (1993), where the value of the option is defined as the utility …
We propose a new framework for modeling stochastic local volatility, with potential applications to modeling derivatives on interest rates, commodities, credit, equity, FX etc., as well as hybrid derivatives. Our model extends the linearity-generating unspanned volatility term structure model by Carr et al. (2011) by a…
This paper develops methods for pricing American Parisian options under general Markov models.
Algorithm learns mixtures of Markov chains and MDPs from short trajectories.
We consider option pricing in a regime-switching diffusion market. As the market is incomplete, there is no unique price for a derivative. We apply the good-deal pricing bounds idea to obtain ranges for the price of a derivative. As an illustration, we calculate the good-deal pricing bounds for a European call option a…
Paper approximates rough stochastic local volatility models for efficient computation.
STANLEY improves sampling for complex data models.
The paper improves energy contract pricing models by incorporating jumps and varying parameters.
In this paper we consider a jump-diffusion dynamic whose parameters are driven by a continuous time and stationary Markov Chain on a finite state space as a model for the underlying of European contingent claims. For this class of processes we firstly outline the Fourier transform method both in log-price and log-strik…
Novel method recovers market regime changes from option prices.
Classifier chains link binary classifiers for multi-label learning, achieving state-of-the-art performance.
Fast-vollib offers high-performance option pricing and IV computation.
Quantum algorithms for financial derivatives and credit risk.
Efficient method for pricing Bermudan moving average options using GPR-GHQ.
We introduce a new approach to incorporate uncertainty into the decision to invest in a commodity reserve. The investment is an irreversible one-off capital expenditure, after which the investor receives a stream of cashflow from extracting the commodity and selling it on the spot market. The investor is exposed to pri…
New method improves Bayesian inference for large models.
We examine a general multi-factor model for commodity spot prices and futures valuation. We extend the multi-factor long-short model in Schwartz and Smith (2000) and Yan (2002) in two important aspects: firstly we allow for both the long and short term dynamic factors to be mean reverting incorporating stochastic volat…
Study finds on-chain data can proxy off-chain cryptocurrency pricing.
New algorithm broadens BART models applicability.
FedSight AI predicts federal funds rate using LLMs and multi-agent reasoning.
Unified framework for pricing various debt securities.
We construct a statistical indicator for the detection of short-term asset price bubbles based on the information content of bid and ask market quotes for plain vanilla put and call options. Our construction makes use of the martingale theory of asset price bubbles and the fact that such scenarios where the price for a…
We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic programming problem can be …
Deep learning models predict option prices from 3D tensor data.
HO2 learns options from data efficiently, improving robot manipulation tasks.