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 reduces Monte Carlo error in option pricing and Greeks estimation.
This study compares MC and QMC methods for derivative pricing, showing QMC's superior convergence rates.
Pricing options is an important problem in financial engineering. In many scenarios of practical interest, financial option prices associated to an underlying asset reduces to computing an expectation w.r.t.~a diffusion process. In general, these expectations cannot be calculated analytically, and one way to approximat…
Study compares MC and QMC methods for pricing and risk analysis in a hyperbolic local volatility model.
GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point …
Developed scalable Monte Carlo method for VIX option pricing.
The paper predicts cryptocurrency prices using a path-dependent Monte Carlo simulation.
Improved pricing method for illiquid assets using Lambert function.
Speeds up complex portfolio exposure calculations.
Sequential Monte Carlo (SMC) methods have successfully been used in many applications in engineering, statistics and physics. However, these are seldom used in financial option pricing literature and practice. This paper presents SMC method for pricing barrier options with continuous and discrete monitoring of the barr…
New method prices interest rate derivatives without Monte Carlo, achieving high accuracy and speed.
We introduce a stacking version of the Monte Carlo algorithm in the context of option pricing. Introduced recently for aeronautic computations, this simple technique, in the spirit of current machine learning ideas, learns control variates by approximating Monte Carlo draws with some specified function. We describe the…
In this paper, we discuss the application of quasi-Monte Carlo methods to the Heston model. We base our algorithms on the Broadie-Kaya algorithm, an exact simulation scheme for the Heston model. As the joint transition densities are not available in closed-form, the Linear Transformation method due to Imai and Tan, a p…
Quantum Monte Carlo speeds up option pricing for complex payoff functions.
A fast Monte Carlo method for additive processes and option pricing.
Adapts Monte Carlo method to price π-options related to maximum drawdown.
Weighted Monte Carlo prices exotic options calibrating the probabilities of previously generated paths by a regular Monte Carlo to fit a set of option premiums. When only vanilla call and put options and forward prices are considered, the Martingale condition might not be preserved. This paper shows that this is indeed…
The paper uses LSMC to price capped American options with time-dependent caps.
Utility based methods provide a very general theoretically consistent approach to pricing and hedging of securities in incomplete financial markets. Solving problems in the utility based framework typically involves dynamic programming, which in practise can be difficult to implement. This article presents a Monte Carl…
One of the main practical applications of quasi-Monte Carlo (QMC) methods is the valuation of financial derivatives. We aim to give a short introduction into option pricing and show how it is facilitated using QMC. We give some practical examples for illustration.
Quantum computing improves Monte Carlo option pricing for complex derivatives.
A new two-step LSMC method improves game option pricing accuracy.
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
Quantum algorithm speeds up financial option pricing.
The paper models natural gas futures prices and volatility, using Monte Carlo and reinforcement learning.
QMC and GSA improve option pricing and risk measures efficiency.
Monte Carlo Tree Search improves financial derivative hedging efficiency.
Improved Least-Squares Monte Carlo with finite-difference ansatz.
Enhances option pricing for American-style options using JDOI method.
A new method predicts future paths using a Monte-Carlo approach.
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst exponent , is exploited in order to predict future BTC/USD price. A Monte Carlo simulation with geometric fractional Brownian motion realisations is performed as extensions of historical data. The accuracy of statistical inferen…
The use of sequential Monte Carlo within simulation for path-dependent option pricing is proposed and evaluated. Recently, it was shown that explicit solutions and importance sampling are valuable for efficient simulation of spot price and volatility, especially for purposes of path-dependent option pricing. The result…
Typically options with a path dependent payoff, such as Target Accumulation Redemption Note (TARN), are evaluated by a Monte Carlo method. This paper describes a finite difference scheme for pricing a TARN option. Key steps in the proposed scheme involve tracking of multiple one-dimensional finite difference solutions,…
We explore the possibilities of importance sampling in the Monte Carlo pricing of a structured credit derivative referred to as Collateralized Debt Obligation (CDO). Modeling a CDO contract is challenging, since it depends on a pool of (typically about 100) assets, Monte Carlo simulations are often the only feasible ap…
We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic programming problem can be …
This paper explores alternative regression techniques in pricing American put options and compares to the least-squares method (LSM) in Monte Carlo implemented by Longstaff-Schwartz, 2001 which uses least squares to estimate the conditional expected payoff to the option holder from continuation. The pricing is done und…
New method for pricing discrete Asian and Lookback options under Heston model.
The rough Bergomi (rBergomi) model, introduced recently in [5], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet remarkably fits with empirical implied volatility surfaces. In the absence of analytical European option pricing methods f…
We review and apply Quasi Monte Carlo (QMC) and Global Sensitivity Analysis (GSA) techniques to pricing and risk management (greeks) of representative financial instruments of increasing complexity. We compare QMC vs standard Monte Carlo (MC) results in great detail, using high-dimensional Sobol' low discrepancy sequen…
This research improves option pricing models using Heston, GARCH, and jump diffusion models.
Efficient hybrid method for pricing barrier options with stochastic volatility.
Option valuation problems are often solved using standard Monte Carlo (MC) methods. These techniques can often be enhanced using several strategies especially when one discretizes the dynamics of the underlying asset, of which we assume follows a diffusion process. We consider the combination of two methodologies in th…
Quantum computing speeds up CDO pricing models.
Overlay framework simplifies exotic derivative pricing.
Framework for pricing data products in data-poor markets.
Machine learning improves American option pricing accuracy.
A hybrid framework prices options using neural networks and VAE latent space.
We describe general multilevel Monte Carlo methods that estimate the price of an Asian option monitored at fixed dates. Our approach yields unbiased estimators with standard deviation in expected time for a variety of processes including the Black-Scholes model, Merton's jump-diffusion mod…