calibration of a stochastic volatility model

In Progress


I need to calibrate a stochastic volatility model on freight option market prices.

The Heston Model is a stochastic volatility model which is driven by two stochastic processes: the underlying and the volatilty. It has five parameters (mean reversion rate, long run average price variance, volatility of the volatility, instantaneous variance and correlation between the two processes) which need to be calibrated and is implemented via Monte-Carlo method. The code for a Monte-Carlo Heston Call is available.

The data on market prices consists of spot prices, strike price, risk-free interest rate, time in years and market prices of the freight options. All data is available.

The calibration method is differential evolution, a global optimizer that iteratively optimizes a problem. The code and an example for the optimization of the ackley function (two parameters) is available.

The code for the differential evolution needs to be applied on the Monte-Carlo Heston Model. It shouldn't take long to combine these codes into one.

I'm looking forward to your answer.


Skills: Algorithm, Finance, Matlab and Mathematica, Statistics

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Project ID: #4641938

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hi, this sounds like an interesting project. without having a look at the data or the code that you refer to, it is a little hard to estimate how much time this will take. however, i guess that it will be in the order More

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I have good foundation in MATHs and stochastic process, Can I help

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I have done stochastic methods before (Neural Networks and Monte Carlo applied in Civil Engineering) as my thesis so I can do this job for you.

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I have a very strong academic background in stochastics, financial mathematics and financial engineering as you can see from my transcripts. I've interned in Financial Engineering at a major German bank. I am new to More

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Let's start! Please check your PM.

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