This document discusses using the Python library DX for modeling complex derivatives and portfolios. It describes simulation classes for modeling relevant risk factors like geometric Brownian motion, jump diffusion, and square root diffusion. It also mentions calibration and hedging strategies like dynamic delta hedging to reduce risk from changes in the underlying asset's price.

1. PYTHON FOR DERIVATIVE
ANALYTICS
Alicia Guerra

2. WHAT IS A DERIVATIVE?
• In finance, a derivative is a contract that derives its value
from the performance of an underlying entity (i.e. an option,
future, etc.)

3. DX ANALYTICS
• Python library which allows for the modeling of rather
complex derivatives instruments and portfolios.

4. SIMULATION
Simulation classes in DX where relevant risk factors are
modeled:
• geometric_brownian_motion
• jump_diffusion
• square_root_diffusion

5. GEOMETRIC BROWNIAN MOTION
• Used to model option prices in
the Black-Scholes model

6. JUMP DIFFUSION
• Model for price behavior that
incorporates small, day-to-
day “diffusive” movements
together with larger,
randomly occurring jumps.
• Inclusion of jumps allows for
more realistic “crash”
scenarios.

7. SQUARE ROOT DIFFUSION
• Models mean-reverting
quantities like interest rates
and volatility.
• Mean-reverting means it is
assumed that a price will
move to the average
price over time.

8. CALIBRATION

9. HEDGING
• Dynamic delta hedging is
a derivative trading
strategy that attempts to
reduce – or eliminate – the
risk caused by price
changes in the underlying
asset.
• A derivative’s delta is the
relation between the
changes in the
derivative’s price and the
changes in the price of
the underlying asset.

10. CONTACT ME
Email: alicia.developer@aliciaguerra.com
LinkedIn: linkedin.com/in/aliciaisabelguerra
Twitter: @skepchick92
Phone: (619) 519-3805