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.
Optimal hedging strategies for exotic options using vanilla options.
Vanilla GANs are connected to Wasserstein distance for better understanding.
This paper uses basket option formulas to price vanilla options with discrete dividends.
DDSME outperforms SME in estimating multimodal distributions.
Deep vanilla transformers trained without shortcuts achieve similar performance to standard models.
Vanilla Bayesian optimization performs well in high dimensions.
New method uses neural networks for better financial hedging.
Vanilla SGD learns SIM from anisotropic data without explicit covariance estimation.
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
The Bass model is calibrated to vanilla options using a fixed-point equation.
New FX option interpolations impact implied volatilities.
Generative Adversarial Networks are known for their high quality outputs and versatility. However, they also suffer the mode collapse in their output data distribution. There have been many efforts to revamp GANs model and reduce mode collapse. This paper focuses on two of these models, PacGAN and VEEGAN. This paper ex…
In this paper, we argue that, once the costs of maintaining the hedging portfolio are properly taken into account, semi-static portfolios should more properly be thought of as separate classes of derivatives, with non-trivial, model-dependent payoff structures. We derive new integral representations for payoffs of exot…
This paper examines Bachelier implied volatility at extreme strikes.
Quantization algorithms have been successfully adopted to option pricing in finance thanks to the high convergence rate of the numerical approximation. In particular, very recently, recursive marginal quantization has been proven to be a flexible and versatile tool when applied to stochastic volatility processes. In th…
New method trains deep vanilla networks as fast as ResNets without shortcut connections.
The paper finds optimal strategies for hedging in incomplete markets using derivatives.
Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.
We study option pricing and hedging with uncertainty about a Black-Scholes reference model which is dynamically recalibrated to the market price of a liquidly traded vanilla option. For dynamic trading in the underlying asset and this vanilla option, delta-vega hedging is asymptotically optimal in the limit for small u…
Counterfactual regret minimization (CFR) is the most popular algorithm on solving two-player zero-sum extensive games with imperfect information and achieves state-of-the-art performance in practice. However, the performance of CFR is not fully understood, since empirical results on the regret are much better than the …
Study utility indifference pricing in a Bachelier model with small linear price impact.
Study optimizes option pricing with robust strategies, ensuring consistency with vanilla option prices.
A new relaxed framework for pricing illiquid derivatives using bid-ask spreads.
Large-scale machine learning training suffers from two prior challenges, specifically for nuclear-norm constrained problems with distributed systems: the synchronization slowdown due to the straggling workers, and high communication costs. In this work, we propose an asynchronous Stochastic Frank Wolfe (SFW-asyn) metho…
We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS3L is motivated by the success of negative sampling/contrastive estimation. We demonstrate that adding the NS3L loss to state-of-the-art SSL algorithms, such as the Virtual Adv…
Deep RL policies are vulnerable to adversarial perturbations, but vanilla training yields more robust policies.
We apply supervised deep neural networks (DNNs) for pricing and calibration of both vanilla and exotic options under both diffusion and pure jump processes with and without stochastic volatility. We train our neural network models under different number of layers, neurons per layer, and various different activation fun…
Study optimal liquidation under high risk aversion and small price impact.
We discuss a semi-analytical method for solving SABR-type equations based on path integrals. In this approach, one set of variables is integrated analytically while the second set is integrated numerically via Monte-Carlo. This method, known in the literature as Conditional Monte-Carlo, leads to compact expressions fun…
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we propose a black-box adversarial attack algorithm that can defeat both vanilla DNNs and those generated by various defense techniques develo…
New analysis improves sample complexity for vanilla policy gradient methods.
BEMA reduces bias in EMA, leading to faster convergence and better performance.
Recurrent neural networks have gained widespread use in modeling sequence data across various domains. While many successful recurrent architectures employ a notion of gating, the exact mechanism that enables such remarkable performance is not well understood. We develop a theory for signal propagation in recurrent net…
New bounds for shallow neural networks with deterministic parameters.
Motivated by the model- independent pricing of derivatives calibrated to the real market, we consider an optimization problem similar to the optimal Skorokhod embedding problem, where the embedded Brownian motion needs only to reproduce a finite number of prices of Vanilla options. We derive in this paper the correspon…
We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures. Random matrices play the role of free random variables in this context so the method we…
We show that the frequent claim that the implied tree prices exotic options consistently with the market is untrue if the local volatilities are subject to change and the market is arbitrage-free. In the process, we analyse -- in the most general context -- the impact of stochastic variables on the P&L of a hedged port…
Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference. In this work, we enhance SVGD by leveraging preconditioning matrices, such as the Hessian and Fisher information matrix, to incorporate geometric information into SV…
In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to study the intrinsic capability of recurrent neural networks in sequence learning. Four different recurrent network models are studied both analytically and experimentally using this test. Our results suggest that in general there exists a conflict be…
We characterize the set of market models when there are a finite number of traded Vanilla and Barrier options with maturity written on the asset . From a probabilistic perspective, our result describes the set of joint distributions for when a finite number of marginal law constraint…
Improved Gaussian process inference for spatio-temporal data.
Fourier methods fail to accurately approximate option Greeks in realistic market conditions.
Fractal learning rate schedules accelerate vanilla gradient descent.
It is well known that any sufficiently regular one-dimensional payoff function has an explicit static hedge by bonds, forward contracts and lots of vanilla options. We show that the natural extension of the corresponding representation leads to a static hedge based on the same instruments along with traffic light optio…
Model-free approach to hedge path-dependent options using min-max optimization.
The crisis that affected financial markets in the last years leaded market practitioners to revise well known basic concepts like the ones of discount factors and forward rates. A single yield curve is not sufficient any longer to describe the market of interest rate products. On the other hand, using different yield c…
A new offline RL framework unifies imitation learning and vanilla offline RL.
Geometric interpretation improves VAE performance and robustness.