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.
Improved bounds for Black-Scholes volatility lead to faster root-finding.
A deep learning model speeds up computation of numerous implied volatilities.
Develops computational methods for simulating rigid body dynamics on SO(3).
Private minimum Hellinger distance estimators maintain robustness and efficiency while ensuring privacy.
We introduce a new method of delta hedging. In many cases, this method results in a lower cost than the Black-Scholes method. To calculate the cost of hedging, we develop a Mathematica program that include the two-dimensional Newton-Raphson method.
New method improves accuracy in computing implied volatility.
We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced t…
A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressi…
Usually, in the Black-Scholes pricing theory the volatility is a positive real parameter. Here we explore what happens if it is allowed to be a complex number. The function for pricing a European option with a complex volatility has essential singularities at zero and infinity. The singularity at zero reflects the put-…
Consider a process, stochastic or deterministic, obtained by using a numerical integration scheme, or from Monte-Carlo methods involving an approximation to an integral, or a Newton-Raphson iteration to approximate the root of an equation. We will assume that we can sample from the distribution of the process from time…
Efficient oblique RSF method improves prediction and interpretability.
Paper solves convertible bond valuation using finite elements with penalty method.
Paper develops MMOT framework for financial applications with neural acceleration.
Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first order method and therefore …
A new method for robust product Markovian quantization overcomes numerical instabilities.
New method predicts neural network performance using free probability theory.
Transformer improves parameter estimation without needing closed-form solutions.
In Neri and Schneider (2012) we presented a method to recover the Maximum Entropy Density (MED) inferred from prices of call and digital options on a set of n strikes. To find the MED we need to numerically invert a one-dimensional function for n values and a Newton-Raphson method is suggested. In this note we revisit …
Estimates change points in Weibull time series with copulas.
Enhanced options trading strategies using advanced portfolio optimization.
Efficiently models categorical data with low to medium class overlap, improving accuracy over standard distributions.
This paper presents a unified framework for smooth convex regularization of discrete optimal transport problems. In this context, the regularized optimal transport turns out to be equivalent to a matrix nearness problem with respect to Bregman divergences. Our framework thus naturally generalizes a previously proposed …
Local laGPR speeds up multiscale mechanics simulations without neural networks.
Single-site Markov Chain Monte Carlo (MCMC) is a variant of MCMC in which a single coordinate in the state space is modified in each step. Structured relational models are a good candidate for this style of inference. In the single-site context, second order methods become feasible because the typical cubic costs assoc…