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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.

168,657 papers · 148 categories

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48 results for price variations

Generative model simulates financial market price variations from order flow.

problem Simulating intra-day price variations driven by order flow.
method Sequence Generative Adversarial Networks framework applied to model order flow.
result Generated price sequences from generative model better match real price variations.

Study reveals that cryptocurrency price variations follow power-law distributions, influenced by age and market capitalization.

problem Understanding the statistical properties of cryptocurrencies, especially their price variations.
method Comprehensive investigation of over 7000 digital currencies, analyzing their price returns over time.
result Cryptocurrency price returns follow power-law distributions, with age and market capitalization influencing these distributions.

Using Vovk's outer measure, which corresponds to a minimal superhedging price, the existence of quadratic variation is shown for "typical price paths" in the space of càdlàg functions possessing a mild restriction on the jumps directed downwards. In particular, this result includes the existence of quadratic variation …

2016-09-08abs ↗pdf ↗

Develops numerical methods for pricing exchange options in a market with limited liquidity.

problem Pricing European style exchange options in a market with finite liquidity.
method Integrates price impact into the dynamics of correlated assets using a controlled variate approach.
result Numerical pricing methods for exchange options are developed and validated.

An analytic method for pricing American call options is provided; followed by an empirical method for pricing Asian call options. The methodology is the pricing theory presented in "A Modern Theory of Random Variation", by Patrick Muldowney, 2012.

2015-07-11abs ↗pdf ↗

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion funct…

2019-03-11abs ↗pdf ↗

Optimal hedging framework with variational preferences under convex risk measures.

problem Optimal hedging with variational preferences under convex risk measures.
method Theoretical hedging optimization framework with dual representation of risk measures and utilities.
result Derivation of optimality and indifference pricing conditions.

This paper considers possible price paths of a financial security in an idealized market. Its main result is that the variation index of typical price paths is at most 2, in this sense, typical price paths are not rougher than typical paths of Brownian motion. We do not make any stochastic assumptions and only assume t…

2010-05-03abs ↗pdf ↗

New model predicts energy prices volatility by smoothing time variation and persistence.

problem Separate study of volatility's time variation and persistence.
method Dynamic persistence model that allows shocks with heterogeneous persistence to vary smoothly over time.
result Significantly improves volatility forecasts over state-of-the-art models.

LLM-as-a-service prices vary arbitrarily due to tokenization multiplicity.

problem Arbitrary price variation in LLM-as-a-service due to multiple tokenizations of the same output.
method Introduce canonical generation to restrict LLMs to unique tokenizations and develop an efficient sampling algorithm.
result Our sampling algorithm for canonical generation solves tokenization multiplicity and maintains comparable performance and runtime to standard sampling.

We consider the pricing of derivatives written on the discretely sampled realized variance of an underlying security. In the literature, the realized variance is usually approximated by its continuous-time limit, the quadratic variation of the underlying log-price. Here, we characterize the small-time limits of options…

2010-03-29abs ↗pdf ↗

Quantum state preparation framework speeds up basket option pricing.

problem Limited practical benefit of quantum amplitude estimation due to state-preparation depth.
method Structure-aware tensor-train rank-based variational state preparation.
result State-preparation depth scaling replaced with linear scaling, maintaining low basket-pricing errors.

We consider idealized financial markets in which price paths of the traded securities are cadlag functions, imposing mild restrictions on the allowed size of jumps. We prove the existence of quadratic variation for typical price paths, where the qualification "typical" means that there is a trading strategy that risks …

2011-08-03abs ↗pdf ↗

Modeling cryptocurrency spot-quotient variation as a diffusion process.

problem Intraday variation between ETHBTC spot and quotients on Binance.
method Modeling variation as an Ornstein-Uhlenbeck process, testing for mean-reversion, using maximum likelihood estimation.
result Intraday variation is not constant at 0, showing mean-reversion behavior with larger deviations in the first year.

We prove that the model-free typical (in the sense of Vovk) càdlàg price paths with mildly restricted downward jumps possess quadratic variation which does not depend on the specific sequence of partitions as long as these partitions are obtained from stopping times such that the oscillations of a path on the consecuti…

2017-10-22abs ↗pdf ↗

The study assesses how financial networks resist simultaneous price shocks and calculates the worst-case loss.

problem Resilience of financial networks to simultaneous price fluctuations and default contagion.
method Introduced a concept of default resilience margin, ε*, and computed worst-case systemic loss through linear programming.
result Threshold value ε* determines the maximum amplitude of asset price fluctuations the network can tolerate.

New quantum algorithm simplifies complex financial derivatives pricing.

problem Complex financial derivatives pricing with high dimensionality.
method Quantum-inspired variational algorithms combined with neural-network quantum states.
result Simplified pricing of European options with many correlated assets.

We derive a recursive formula for arithmetic Asian option prices with finite observation times in semimartingale models. The method is based on the relationship between the risk-neutral expectation of the quadratic variation of the return process and European option prices. The computation of arithmetic Asian option pr…

2013-11-20abs ↗pdf ↗

Study near-maturity convergence rates of American put prices in Lévy models.

problem Analyzing convergence rates of optimal exercise prices in Lévy models.
method Examined two settings: jumps of unbounded and bounded variation, deriving near-maturity expansions.
result Near-maturity convergence rate of optimal exercise price is of order √(T-t).

This paper proposes new get-rich-quick schemes that involve trading in a financial security with a non-degenerate price path. For simplicity the interest rate is assumed zero. If the price path is assumed continuous, the trader can become infinitely rich immediately after it becomes non-constant (if it ever does). If i…

2016-04-03abs ↗pdf ↗

Improved VI with Price's gradient estimator for target log-density.

problem Approximating target distributions from unnormalized log-densities.
method Stochastic gradient-based variational inference with Price's gradient estimator.
result Identifies Price's gradient as the key to WVI's superior performance.

A hybrid framework prices options using neural networks and VAE latent space.

problem Lack of explicit asset dynamics information in compressed volatility surfaces.
method Combining Weighted Monte Carlo with neural networks trained on VAE latent space.
result Effective pricing of vanilla and exotic options on idealized vol surface.

This paper establishes a non-stochastic analogue of the celebrated result by Dubins and Schwarz about reduction of continuous martingales to Brownian motion via time change. We consider an idealized financial security with continuous price path, without making any stochastic assumptions. It is shown that typical price …

2009-04-28abs ↗pdf ↗

New framework estimates demand responses across multiple contexts with limited price variation.

problem Estimating heterogeneous linear price-response functions across multiple contexts with limited price variation and confounding.
method Meta-learning framework that identifies conditional mean of task-specific causal demand parameters given a subset of task-specific observables.
result Improved recovery of demand responses relative to standard transfer-learning baselines.

Study shows how macroeconomic news affects intraday price and order flow dynamics.

problem Understanding how macroeconomic news impacts intraday price and order flow dynamics.
method Structural VAR model identified through heteroskedasticity, estimated at one-second frequency for each 15-minute interval.
result Macroeconomic news announcements reshape price-flow dynamics, with significant impacts on price and flow impacts at the one-second horizon.

Study optimizes pricing under uncertainty and capacity constraints.

problem Optimizing pricing decisions under demand uncertainty and capacity constraints.
method Analyzes linear demand, stochastic noise, and finite capacity; uses certified demand forecasts and control variates.
result Certified demand forecasts reduce regret from O(T)O(\sqrt{T}) to O(logT)O(\log T) under certain conditions.

Employee stock options (ESOs) are American-style call options that can be terminated early due to employment shock. This paper studies an ESO valuation framework that accounts for job termination risk and jumps in the company stock price. Under general Lévy stock price dynamics, we show that a higher job termination ri…

2015-04-30abs ↗pdf ↗

Quantum computer method for pricing lookback options with jumps.

problem Pricing lookback options with discrete monitoring and jump conditions.
method Variational Quantum Imaginary Time Evolution (VarQITE) method to solve non-Hermitian Schrodinger equation.
result Quantum algorithm can handle jump conditions in lookback options pricing.

Paper proposes a closed-form formula for geometric Istanbul call options.

problem Pricing geometric Istanbul call options under the Black-Scholes model.
method Second-order Taylor expansion to derive a closed-form approximation.
result The proposed formula accurately approximates GIC values compared to Monte-Carlo simulations.

This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. …

2014-10-27abs ↗pdf ↗

This note continues investigation of randomness-type properties emerging in idealized financial markets with continuous price processes. It is shown, without making any probabilistic assumptions, that the strong variation exponent of non-constant price processes has to be 2, as in the case of continuous martingales.

2007-12-10abs ↗pdf ↗