stochastic-volatility-models

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Generate realizations of stochastic processes in python.

Quant Option Pricing - Exotic/Vanilla: Barrier, Asian, European, American, Parisian, Lookback, Cliquet, Variance Swap, Swing, Forward Starting, Step, Fader

Option pricing function for the Heston model based on the implementation by Christian Kahl, Peter Jäckel and Roger Lord. Includes Black-Scholes-Merton option pricing and implied volatility estimation. No Financial Toolbox required.

DRIP Fixed Income is a collection of Java libraries for Instrument/Trading Conventions, Treasury Futures/Options, Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV Metrics, Stochastic Evolution and Option Pricing, Interest Rate Dynamics and Option Pricing, LMM Extensions/Calibrations/Greeks, Algorithmic Differentiation, and Asset Backed Models and Analytics.

Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"

Monte Carlo option pricing algorithms for vanilla and exotic options

Quantitative finance and derivative pricing

Numerical experiments with stochastic differential equations

A list (quite disorganized for now) of papers tackling the Bayesian estimation of Ito processes (and their discrete time version)

R Code to accompany "A Note on Efficient Fitting of Stochastic Volatility Models"

Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods

Stochastic volatility models and their application to Deribit crypro-options exchange

Bayer, Friz, Gassiat, Martin, Stemper (2017). A regularity structure for finance.

This is a collection of Stochastic indicators. It's developed in PineScript for the technical analysis platform of TradingView.

Demonstrates how to price derivatives in a Heston framework, using successive approximations of the invariant distribution of a Markov ergodic diffusion with decreasing time discretization steps. The framework is that of G. Pagès & F. Panloup.

Comparison of different implementations of the same stochastic volatility model (stochvol, JAGS, Stan)

R package pmhtutorial available from CRAN.