R codes to implement two examples for the mode and importance sampling estimation methods.
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Updated
Aug 10, 2020 - R
R codes to implement two examples for the mode and importance sampling estimation methods.
Config files for my GitHub profile.
Introducing the data-driven concept through neural networks to price an option whose volatility is measured as a stochastic process.
Investigating Wiener Processes
R package pmhtutorial available from CRAN.
The workings for a very interesting exercise from the Econometrics of Financial Markets module of the MSc Quantitative Finance 2023/24 course at Bayes Business School (formerly Cass).
Code of numerical experiments in Master's thesis [TBD]
Estimated Bayesian Small Open Economics DSGE model with Stochastic Volatility in Structural Shock Processes
R implementation of the Heston option pricing function
Quantitative finance and derivative pricing
Bayer, Friz, Gassiat, Martin, Stemper (2017). A regularity structure for finance.
Stochastic volatility models and their application to Deribit crypro-options exchange
Comparison of different implementations of the same stochastic volatility model (stochvol, JAGS, Stan)
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.
Code files containing research done around monte carlo stimulations, bayesian interference and stochastic volatility
An implementation of the Heston model, a stochastic volatility model for options pricing. We compute prices of European call and put options via Monte Carlo simulation, for a variety of strike prices and maturities. We also show that the Heston model captures volatility smiles/smirks/skews.
A list (quite disorganized for now) of papers tackling the Bayesian estimation of Ito processes (and their discrete time version)
Numerical experiments with stochastic differential equations
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