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.
Approximates derivative pricing under fractional stochastic volatility.
In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payoffs. We show that signature payoffs can be used to nonparametrically price and hed…
We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a result that is exploited to quickly price arbitrary continuous payoffs. This approach …
Quantum computing speeds up pricing multi-asset derivatives.
Improved price bounds for multi-asset derivatives using market option data.
Generic model for commodity derivatives pricing.
Study dynamic trading in options to improve price bounds for exotic derivatives.
New formulas derived for variance gamma model option pricing.
Quantum computing speeds up interest rate derivative pricing using LMM.
The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.
The study examines how including additional call option prices affects model-independent price bounds for exotic derivatives.
Deep learning method for fair derivative pricing.
A common assumption in financial engineering is that the market price for any derivative coincides with an objectively defined risk-neutral price - a plausible assumption only if traders collectively possess objective knowledge about the price dynamics of the underlying security over short time scales. Here we assume t…
Financial derivatives based on road travel times for hedging and pricing.
Model prices commodity futures and index options.
In this paper analytic formulas for electricity derivatives are calculated. To this end, we assume that electricity spot prices follow a 3-regime Markov regime-switching model with independent spikes and drops and periodic transition matrix. Since the classical derivatives pricing methodology cannot be used in case of …
Generative model uses DDPMs for risk-neutral derivative pricing.
Improved path integral method for financial derivatives pricing.
Develops a framework for consistent pricing of interest rate derivatives.
Paper presents a data-driven method for option pricing.
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonst…
We introduce a criterion how to price derivatives in incomplete markets, based on the theory of growth optimal strategy in repeated multiplicative games. We present reasons why these growth-optimal strategies should be particularly relevant to the problem of pricing derivatives. We compare our result with other alterna…
Study pricing derivatives in markets with long-range dependence and jumps.
Overlay framework simplifies exotic derivative pricing.
New method uses quantum simulation to price multi-asset derivatives efficiently.
Estimates cross-impact on derivatives markets using E-Mini futures and options.
Quantum advantage in derivative pricing requires 8k qubits and 54M T-depth.
Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…
The paper provides an efficient method to price path-dependent derivatives using multiscale stochastic volatility models.
Quantum computing improves Monte Carlo option pricing for complex derivatives.
Develops a new model for cross-currency derivatives pricing.
The article provides representations of exchange option prices under SVJD dynamics.
At present, there is an explosion of practical interest in the pricing of interest rate (IR) derivatives. Textbook pricing methods do not take into account the leptokurticity of the underlying IR process. In this paper, such a leptokurtic behaviour is illustrated using LIBOR data, and a possible martingale pricing sche…
In a market with transaction costs, the price of a derivative can be expressed in terms of (preconsistent) price systems (after Kusuoka (1995)). In this paper, we consider a market with binomial model for stock price and discuss how to generate the price systems. From this, the price formula of a derivative can be refo…
This article provides a new representation for pricing adjustments in derivatives.
A pricing formula for discount bonds, based on the consideration of the market perception of future liquidity risk, is established. An information-based model for liquidity is then introduced, which is used to obtain an expression for the bond price. Analysis of the bond price dynamics shows that the bond volatility is…
We study the formation of derivative prices in equilibrium between risk-neutral agents with heterogeneous beliefs about the dynamics of the underlying. Under the condition that the derivative cannot be shorted, we prove the existence of a unique equilibrium price and show that it incorporates the speculative value of p…
We formalize in the proof assistant Isabelle essential basic notions and results in financial mathematics. We provide generic formal definitions of concepts such as markets, portfolios, derivative products, arbitrages or fair prices, and we show that, under the usual no-arbitrage condition, the existence of a replicati…
Revises derivative pricing post financial crisis by defining a discount rate.
Using tools from spectral analysis, singular and regular perturbation theory, we develop a systematic method for analytically computing the approximate price of a derivative-asset. The payoff of the derivative-asset may be path-dependent. Additionally, the process underlying the derivative may exhibit killing (i.e. jum…
This paper highlights the role of risk neutral investors in generating endogenous bubbles in derivatives markets. We find that a market for derivatives, which has all the features of a perfect market except completeness and has some risk neutral investors, can exhibit extreme price movements which represent a violation…
The paper modifies asset pricing models using Taylor series expansions and market-based averages.
Study of gamma-hedging using rough paths for European and exotic options.
We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a…
Derives pricing formulas for liquidity tokens in CPMMs, showing riskless growth.
In this paper, we develop a 4/2 stochastic volatility plus jumps model, namely, a new stochastic volatility model including the Heston model and 3/2 model as special cases. Our model is highly tractable by applying the Lie symmetries theory for PDEs, which means that the pricing procedure can be performed efficiently. …
Volatility clustering, long-range dependence, and non-Gaussian scaling are stylized facts of financial assets dynamics. They are ignored in the Black & Scholes framework, but have a relevant impact on the pricing of options written on financial assets. Using a recent model for market dynamics which adequately captures …
Paper proposes an analytical pricing model for puttable bonds with credit risk.