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.
Analyzes impermanent loss in decentralized exchanges and provides a replication formula.
The article provides formulas to hedge impermanent loss in decentralized markets.
In this note we show how to replicate a stylized CDS with a repurchase agreement and an asset swap. The latter must be designed in such a way that, on default of the issuer, it is terminated with a zero close-out amount. This break clause can be priced using the well known unilateral credit/debit valuation adjustment f…
In this expository paper we illustrate the generality of game theoretic probability protocols of Shafer and Vovk (2001) in finite-horizon discrete games. By restricting ourselves to finite-horizon discrete games, we can explicitly describe how discrete distributions with finite support and the discrete pricing formulas…
We introduce a new model for pricing corporate bonds, which is a modification of the classical model of Merton. In this new model, we drop the liquidity assumption of the firm's asset value process, and assume that there is a liquidly traded asset in the market whose value is correlated with the firm's asset value, and…
We discuss and clarify the XVA modelling framework specified in the paper "MVA by replication and regression" (Risk Magazine, May 2015) for including bilateral credit risk and funding costs in derivative pricing, and in doing so we rectify two key errors in the valuation adjustments accounting for costs of capital and …
The paper prices and replicates various financial contracts on a risky asset with stochastic volatility and jumps.
We study the set of marginal utility-based prices of a financial derivative in the case where the investor has a non-replicable random endowment. We provide an example showing that even in the simplest of settings - such as Samuelson's geometric Brownian motion model - the interval of marginal utility-based prices can …
Paper shows how to replicate payoffs without oracles in CFMMs.
This work analyzes impermanent loss in decentralized markets and provides a hedging strategy.
We examine in this article the pricing of target volatility options in the lognormal fractional SABR model. A decomposition formula by Ito's calculus yields a theoretical replicating strategy for the target volatility option, assuming the accessibilities of all variance swaps and swaptions. The same formula also sugges…
Study of gamma-hedging using rough paths for European and exotic options.
We consider a model of linear market impact, and address the problem of replicating a contingent claim in this framework. We derive a non-linear Black-Scholes Equation that provides an exact replication strategy. This equation is fully non-linear and singular, but we show that it is well posed, and we prove existence o…
New algorithm ensures consistent results in constrained MAB problems.
Unified framework for fixed-income pricing and liability replication.
The paper examines fair pricing and hedging stability under small numéraire perturbations.
Characterizes super-replication prices in a financial market model.
New study on replicability and stability in machine learning algorithms.
Study on computational aspects of replicable learning, bridging statistical and algorithmic perspectives.
The paper classifies self-replicating 3D shapes using algebraic models.
We propose a model to study the effects of delayed information on option pricing. We first talk about the absence of arbitrage in our model, and then discuss super replication with delayed information in a binomial model, notably, we present a closed form formula for the price of convex contingent claims. Also, we addr…
We present an approach to market-consistent multi-period valuation of insurance liability cash flows based on a two-stage valuation procedure. First, a portfolio of traded financial instrument aimed at replicating the liability cash flow is fixed. Then the residual cash flow is managed by repeated one-period replicatio…
New algorithm prevents strategic replication in multi-armed bandit problems.
We consider the problem of option hedging in a market with proportional transaction costs. Since super-replication is very costly in such markets, we replace perfect hedging with an expected loss constraint. Asymptotic analysis for small transactions is used to obtain a tractable model. A general expansion theory is de…
Extends super-replication theorem with dynamic strategies and transaction costs.
Study reveals statistical bias in dataset replication, reducing accuracy drop from 11-14% to 3.6%.
Study replicability in high-dimensional statistics, resolving open problems.
New uniformity tester ensures consistent results across different samples.
Derives pricing formulas for perpetual futures contracts.
This paper investigates market-consistent valuation of insurance liabilities in the context of, for instance, Solvency II and to some extent IFRS 4. We propose an explicit and consistent framework for the valuation of insurance liabilities which incorporates the Solvency II approach as a special case. The proposed fram…
ERICA assesses replicability of cluster analysis results.
In this work we introduce the notion of fully incomplete markets. We prove that for these markets the super-replication price coincide with the model free super-replication price. Namely, the knowledge of the model does not reduce the super-replication price. We provide two families of fully incomplete models: stochast…
By the classical Martingale Representation Theorem, replication of random vectors can be achieved via stochastic integrals or solutions of stochastic differential equations. We introduce a new approach to replication of random vectors via adapted differentiable processes generated by a controlled ordinary differential …
We study super--replication of contingent claims in markets with fixed transaction costs. This can be viewed as a stochastic impulse control problem with a terminal state constraint. The first result in this paper reveals that in reasonable continuous time financial market models the super--replication price is prohibi…
Optimizing expensive black-box systems with limited data is an extremely challenging problem. As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output. We first …
Adaptive replication improves stochastic function optimization.
Replicable clustering algorithms for k-medians, k-means, and k-centers are proposed.
Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as lo…
In this paper we propose a new robust algorithm to find the optimal static replicating portfolios for general nonlinear payoff functions and give the estimate of the rate of convergence that is absent in the literature. We choose the static replication by minimizing the error bound between the nonlinear payoff function…
We present a method to obtain the average and the typical value of the number of critical points of the empirical risk landscape for generalized linear estimation problems and variants. This represents a substantial extension of previous applications of the Kac-Rice method since it allows to analyze the critical points…
The paper prices long-term options with a reflecting barrier model.
Researchers find a timing error in Black-Scholes-Merton option pricing model.
New RL algorithm ensures stable, replicable policies.
Study on replicability in reinforcement learning algorithms.
Replicates and improves Uniswap V3 model using DDQN and Mamba.
Efficient algorithms improve learning of large-margin halfspaces.
This paper studies robust payoff allocation in submodular games, especially against replication.
We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same models can be effortlessly deployed to different cluster architectures (i…