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arXiv research

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.

168,742 papers · 148 categories

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22446587 · May 202619922001200920172026
48 results for equal-risk pricing

The paper extends ERP framework to non-monotonic payoffs and short selling bans.

problem Valuation of contingent claims with short selling bans under ERP framework.
method Unified framework for ERP pricing, extending to non-monotonic payoffs, and comparing with Black-Scholes.
result Equal-risk prices differ from Black-Scholes prices under short selling bans.

Paper proposes equal risk pricing for financial derivatives using convex risk measures.

problem Equal risk pricing and hedging in financial derivatives with convex risk measures.
method Established that the problem reduces to solving independently hedging problems for writer and buyer with zero initial capital. Provided dynamic programming equations for European and American options under Markovian decompositions of convex risk measures.
result Equal risk pricing leads to more similar and smaller risks for both writer and buyer compared to other pricing methods.

This paper improves financial derivative pricing by incorporating multiple hedging instruments.

problem Valuation of financial derivatives with multiple hedging instruments.
method Deep hedging algorithm and reinforcement learning to solve global hedging problems.
result Including options as hedging instruments can significantly decrease equal risk prices and market incompleteness.

Deep RL solves dynamic risk pricing for complex financial models.

problem Dynamic risk measures in financial derivatives pricing.
method Deterministic actor-critic deep reinforcement learning (ACRL) for time-consistent expectile risk.
result High-quality hedging policies and prices for complex financial instruments.

New method uses non-translation invariant risk measures for fair financial derivative pricing.

problem Inequalities in financial derivative pricing under traditional risk measures.
method Deep reinforcement learning with modified deep hedging algorithm.
result Effective pricing of financial derivatives without price inflation.

Study optimizes investment strategies in volatile markets using machine learning and Bayesian techniques.

problem Enhancing portfolio management in volatile markets.
method Market segmentation into ten volatility-based states, real-time asset allocation adjustments using Bayesian Markov switching model.
result Dynamic portfolio achieves significantly higher risk-adjusted returns and total returns.

A non-trivial predictor satisfies demographic parity and equalizes group risks in regression.

problem Achieving fairness in regression models while maintaining equal risks across groups.
method Provided an explicit example of a non-constant predictor satisfying Demographic Parity and Equal Group-Wise Risks.
result First explicit construction of a non-constant predictor satisfying both fairness notions.

This paper bridges Markowitz planning and deep reinforcement learning for portfolio optimization.

problem Combining Markowitz planning and deep reinforcement learning for portfolio optimization.
method Mapping market conditions to actions using deep reinforcement learning, casting portfolio allocation as a continuous control problem.
result Deep reinforcement learning techniques can provide new insights for portfolio allocation.

This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.

problem Optimizing portfolio allocation with a penalty for deviation from a reference portfolio.
method Formulated as a McKean-Vlasov control problem, provides explicit solutions and asymptotic expansions.
result The penalized portfolio strategy outperforms standard mean-variance and reference portfolios in most cases.

Blockchain funds balance risk and return for various investors.

problem Creating diversified portfolios with risk parity for different risk appetites.
method Developed three funds (Alpha, Beta, Gamma) with distinct risk and return profiles, setting weights inversely proportional to risk.
result Blockchain enables investors to select their preferred risk-return combination and allocate wealth accordingly.

The paper uncovers the impact of price and payoff autocorrelations in multi-period asset pricing models.

problem Hidden dependence of asset pricing models on price and payoff autocorrelations.
method Obtained approximations of the basic pricing equation describing various parameters.
result Valid results for other pricing models like ICAPM and APM.

Quantum theory explains price dynamics in financial markets, capturing bid-ask spread and ergodicity.

problem Nature of price formation in financial markets and bid-ask spread dynamics.
method Developed a quantum coupled-wave theory using a 2x2 price operator with eigenvalues representing bid and ask prices.
result The theory adequately models bid-ask spread and directional price movement due to quantum-chaotic interaction.

New pricing algorithm learns demand curves and optimizes prices in dynamic markets.

problem Dynamic pricing in markets with incomplete demand information and shifting conditions.
method Actor-Critic Information-Directed Pricing (ACIDP) using IDS algorithms and auditing procedures.
result ACIDP outperforms UCB and TS in market environment shifts.

Study utility indifference pricing in a Bachelier model with small linear price impact.

problem Utility indifference pricing in a model with linear price impact.
method Analyzes the Bachelier model with exponential utility indifference prices for vanilla European options.
result Computes the scaling limit of utility indifference prices for a vanishing price impact inversely proportional to risk aversion.

A pricing principle is introduced for non-attainable claims in incomplete markets.

problem Pricing non-attainable contingent claims in incomplete markets.
method Distorted Radon-Nikodym derivative and Tsallis relative entropy over a family of equivalent martingale measures.
result The pricing principle is closely related to backward stochastic differential equations and is arbitrage-free and time-consistent.

Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To expl…

2019-03-05abs ↗pdf ↗

Revisits behavioral finance option pricing model to align with rational asset pricing theory.

problem Inconsistency between behavioral finance and rational asset pricing models in option pricing.
method Introduces arbitrage transaction costs to modify the behavioral finance option pricing formula.
result Modifies behavioral finance option pricing formula to be consistent with rational asset pricing theory.

The paper extends option pricing theory for markets with informed traders.

problem Discontinuity in option pricing for markets with informed traders.
method New models for option pricing in complete markets considering informed traders' information on stock price direction and return mean.
result The discontinuity puzzle in option pricing is resolved using continuous diffusion price processes.

How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…

2011-10-25abs ↗pdf ↗

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learn…

2018-02-08abs ↗pdf ↗

A statistical decision problem is hidden in the core of option pricing. A simple form for the price C of a European call option is obtained via the minimum Bayes risk, R_B, of a 2-parameter estimation problem, thus justifying calling C Bayes (B-)price. The result provides new insight in option pricing, among others obt…

2013-04-18abs ↗pdf ↗

The paper addresses pricing interest rate derivatives in markets with volatility uncertainty.

problem Pricing interest rate derivatives under uncertainty about volatility.
method Modeling volatility uncertainty with G-Brownian motion and defining forward sublinear expectation.
result Developed robust pricing formulas for interest rate derivatives.

The paper reviews historical and modern approaches to asset pricing probability measures.

problem Constructing or selecting probability measures for asset pricing.
method Historical review of various approaches including state price theory, martingale measures, and modern data-driven methods.
result Modern asset pricing involves constructing, transforming, or selecting probability measures to represent market prices.

Introduces an unobservable intrinsic electricity price to link storage theory with risk premium.

problem Connecting storage theory with risk premium in electricity markets.
method Introduces an unobservable intrinsic electricity price and derives prices for various contracts.
result Finds an overall negative risk premium in empirical analysis.

Data-driven method for option pricing using historical asset prices.

problem Tackling the gap between historical asset prices and risk-neutral option pricing.
method Identifying a pricing kernel process, solving utility maximization and functional optimization problems using deep learning.
result Demonstrated the efficiency of the data-driven option pricing methodology.

Study compares price limit and circuit breaker effects in stock markets.

problem Preventing rapid and steep price drops in stock exchanges.
method Agent-based model for financial market simulation.
result Price limit and circuit breaker have similar effects under same conditions, but price limit less effective with shorter limit time range.

Paper prices geometric Asian options using a multifactor stochastic volatility model.

problem Pricing continuous geometric Asian options under multifactor stochastic volatility.
method Asymptotic expansion and perturbation techniques for both floating and fixed strike GAOs.
result Simplified pricing formulae for GAOs derived in a multifactor stochastic volatility framework.

We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products We discuss the pricing metho…

2016-07-31abs ↗pdf ↗

The paper addresses fairness in dynamic pricing for strategic buyers.

problem Price disparities among specific groups can lead to unfair perceptions and legal violations.
method Proposes a dynamic pricing policy that achieves fairness and discourages strategic behavior.
result Achieves an upper bound of O(T+H(T))O(\sqrt{T}+H(T)) regret over TT time horizons, reducing regret by 35.06% compared to a benchmark policy.

FINN learns option pricing and hedging using financial theory.

problem Learning accurate option prices and sensitivities from financial theory.
method Self-supervised replication objective based on dynamic hedging.
result FINN accurately recovers classical Black--Scholes prices and performs robustly in stochastic volatility environments.

Derives pricing formulas for perpetual futures contracts.

problem Ensuring fair pricing of perpetual futures contracts without expiration.
method Explicit expressions derived for various types of perpetual contracts, including linear, inverse, and quantos futures.
result Futures price is the risk-neutral expectation of the spot price sampled at a random time reflecting funding payments.