Monte Carlo Simulation is a numerical method that is used in a wide range of applications in finance, space exploration, energy, engineering, etc. In finance it has been used to price complex derivatives that do not have ready closed form solutions; determine profitability distributions for various hedging strategies; simulate interest rate term structures; understand various problems being analyzed such as decoding option price sensitivities (Greeks) or the risk adjusted probabilities of the Black Scholes formula; quantify the risk of illiquid or complex financial products, etc.

How to – Step by Step Guides to Model Building

- How to build a fuel hedging model
- Simulation Models – Pricing Ladder Options using Monte Carlo Simulations
- Pricing Exotic Options using Monte Carlo Simulations
- Cox-Ingersoll-Ross (CIR) interest rate model – Parameter calibration, Short rates simulation and modeling of longer term interest rates – An example
- Calculating Value at Risk for Options and Futures
- Understanding delta hedging for options
- How to simulate prices using historical returns rather than the normal distribution

# Recent Posts

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- Conditional Value at Risk (CVaR)
- Monte Carlo Simulation – How to reference
- Monte Carlo simulation methods – Tweaking the distribution

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- Cox-Ingersoll-Ross (CIR) Interest Rate model – EXCEL example
- Calculating VaR for Futures and Options – EXCEL
- Exotic Option Pricing using Monte Carlo Simulation
- Monte Carlo Simulation – Models and Applications
- Monte Carlo Simulation – Commodity – Example
- Monte Carlo Simulation – Currency – Example
- Monte Carlo Simulation – Equity – Example
- Monte Carlo Simulator with Historical Returns
- Monte Carlo Simulation with Option Pricing – Package
- Monte Carlo Simulation – Package
- Monte Carlo Simulation – Variance Reduction Procedures – EXCEL Examples
- Pricing Ladder Options using a Monte Carlo Simulator
- Understanding N(d1) & N(d2) – EXCEL Example
- Value at Risk using the Monte Carlo simulation with Historical Returns approach