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.
New method tackles bilevel optimization with polyhedral constraints.
Unified analysis of online optimization with self-concordant barriers, improving regret bounds.
New bounds for online portfolio selection without smoothness assumptions.
L3Ms fine-tune LLMs with constraints for tailored applications.
RHMC improves sampling polytopes defined by inequalities with barriers.
New diffusion models handle constrained domains, improving generative tasks.
New study reveals a polynomial penalty for adapting to unknown margin parameters in batched nonparametric bandits.
Proximal policy optimization(PPO) has been proposed as a first-order optimization method for reinforcement learning. We should notice that an exterior penalty method is used in it. Often, the minimizers of the exterior penalty functions approach feasibility only in the limits as the penalty parameter grows increasingly…
A firm with heterogeneous shareholders optimizes dividends under ambiguity aggregation.
We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for generating samples from the uniform distribution over a polytope. Both random walks are sampling algorithms derived from interior point methods. The former is based on volumetric-logarithmic barrier introduced by Vaidya wher…
Statistical-computational gap found in aligning multiple Gaussian graphs.
New algorithm reduces regret from sqrt(T) to polylog(T) in stochastic contextual linear bandits.
New algorithm tackles heterogeneous curvature in online convex optimization.
Paper generalizes VB-FTRL for online learning of quantum states with logarithmic loss.
Let be an open subset of real affine space. We consider functions with non-degenerate Hessian such that the first or the third derivative of is parallel with respect to the Levi-Civita connection defined by the Hessian metric . In the former case the solutions are gi…
We analyze the problem of sequential probability assignment for binary outcomes with side information and logarithmic loss, where regret---or, redundancy---is measured with respect to a (possibly infinite) class of experts. We provide upper and lower bounds for minimax regret in terms of sequential complexities of the …
In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the…
Improved sampling from high-dimensional Gaussians using smoothed scores.
The paper calculates prices for multi-step barrier options under the Black-Scholes model.
Boosting improves accuracy by combining weak learners into a voting classifier.
New algorithm for online portfolio selection with reduced runtime.
We demonstrate effectiveness of the first-order algorithm from [Milstein, Tretyakov. Theory Prob. Appl. 47 (2002), 53-68] in application to barrier option pricing. The algorithm uses the weak Euler approximation far from barriers and a special construction motivated by linear interpolation of the price near barriers. I…
A new method uses deep learning to price barrier options.
We determine the price of digital double barrier options with an arbitrary number of barrier periods in the Black-Scholes model. This means that the barriers are active during some time intervals, but are switched off in between. As an application, we calculate the value of a structure floor for structured notes whose …
A time-dependent double-barrier option is a derivative security that delivers the terminal value at expiry if neither of the continuous time-dependent barriers $b_\pm:[0,T]\to \RR_+$ have been hit during the time interval . Using a probabilistic approach we obtain a decomposition of the barrier opti…
New approach for online learning with adaptive adversaries, simpler and more effective.
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…
Quantum RL algorithm achieves logarithmic regret for exploration.
Efficient semi-analytic methods for pricing double barrier options with time-dependent parameters.
We provided an analytical representation of the price of a barrier option with one type of special moving barrier. We consider the case that risk free rate, dividend rate and stock volatility are time dependent. We get a pricing formula and put call parity for barrier option when the moving barrier has a special relati…
New algorithm reduces prediction errors across various loss functions.
Hamiltonian method applied to floating barrier options pricing.
Deep learning solves barrier options with stochastic volatility.
The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.
Unified pricing method for FX options with barriers.
Root's barrier is continuous and finite under certain conditions.
Path integral method calculates barrier option prices.
This paper deals with a high-order accurate implicit finite-difference approach to the pricing of barrier options. In this way various types of barrier options are priced, including barrier options paying rebates, and options on dividend-paying-stocks. Moreover, the barriers may be monitored either continuously or disc…
Hydro storage system optimization is becoming one of the most challenging tasks in Energy Finance. While currently the state-of-the-art of the commercial software in the industry implements mainly linear models, we would like to introduce risk aversion and a generic utility function. At the same time, we aim to develop…
Research provides explicit NPV expressions for double barrier strategies.
New symplectic barriers found in ball embeddings.
Paper applies subdiffusive dynamics to American and barrier options pricing.
Barrier options are one of the most widely traded exotic options on stock exchanges. In this paper, we develop a new stochastic simulation method for pricing barrier options and estimating the corresponding execution probabilities. We show that the proposed method always outperforms the standard Monte Carlo approach an…
We consider the mean curvature flow of compact convex surfaces in Euclidean -space with free boundary lying on an arbitrary convex barrier surface with bounded geometry. When the initial surface is sufficiently convex, depending only on the geometry of the barrier, the flow contracts the surface to a point in finite…
We use Lie symmetry methods to price certain types of barrier options. Usually Lie symmetry methods cannot be used to solve the Black-Scholes equation for options because the function defining the maturity condition for an option is not smooth. However, for barrier options, this restriction can be accommodated and a sy…
In this paper we analyse financial implications of exchangeability and similar properties of finite dimensional random vectors. We show how these properties are reflected in prices of some basket options in view of the well-known put-call symmetry property and the duality principle in option pricing. A particular atten…
We say that a topologically embedded 3-sphere in a smoothing of Euclidean 4-space is a barrier provided, roughly, no diffeomorphism of the 4-manifold moves the 3-sphere off itself. In this paper we construct infinitely many one parameter families of distinct smoothings of 4-space with barrier 3-spheres. \par The existe…
We derive a forward equation for arbitrage-free barrier option prices, in terms of Markovian projections of the stochastic volatility process, in continuous semi-martingale models. This provides a Dupire-type formula for the coefficient derived by Brunick and Shreve for their mimicking diffusion and can be interpreted …