Time Varying Volatility Models for Stochastic Finance | Weather Derivatives

Now that we have a defined the parameters of our modified mean-reverting Ornstein-Uhlenbeck process which defines our Temperature dynamics, in this tutorial we will now be looking to implement different models for our time varying volatility patterns.
We have a number of options to model temperature volatility across seasons.
- Piece-wise Constant Functions (volatility for each season)
- Parametric Regression - Polynomial
- Local and Nonparametric Regression - Splines
- Fourier Series
- Stochastic Differential Equations
Online written tutorial: quantpy.com.au/weather-deriva...
In this series we take a deep dive into a type of exotic financial products weather derivatives. Weather derivatives are financial instruments that can be used to reduce risk associated with adverse weather conditions like temperature, rainfall, frost, snow, and wind speeds.
Historical Data, Weather Observations for Sydney, Australia - Observatory Hill: www.bom.gov.au/climate/data/st...
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[@guilhermebrito4837]

Great video! Already waiting for the next episode!

[@onlyyt6093]

Very interesting and well made video!

[@kazamaki6586]

Hello, Please do you know how to determine the error of Euler-Mayurama's or Milstein's scheme (for SDE) without exactlly solution (for example in the stochastic volatility model) ?

[@patryknextdoor]

Thank you for great video!
Can you make video on time varying coefficient model which can deal with not normal distribution?

[@QuantPy]

Thanks, no worries . Will try and put this on the list

[@suryareddy1872]

Hello Jon, great video. When are you courses on quant coming out ?

[@QuantPy]

Hi Surya, will be very soon, stay tuned!

[@bikinibottom2100]

Are we even sure there is an actual pattern in the volatility? Id be surprised… the out of sample fit would be more interesting

[@lawrenceneufville1331]

🙂 ƤＲＯ𝓂Ｏ𝕤ᗰ

[@teddyregrets8384]

Confused about why you take this approach at 10:18. Why not just use np.fft.fft , keep the important frequencies (threshold out to 0 the ones that correspond to noise) and then np.fft.ifft