Do

Ahead Replaces Run

Time:
A Neural Network Forecasts
Options Volatility
Mary
Malliaris
and Linda
Salchenberger
10
th
IEEE Conference on Artificial
Intelligence for Applications
Overview
•
We compare existing methods of estimating
the volatility of daily S&P 100 Index options
•
Implied volatility (calculated using the Black

Scholes model)
•
Historical volatility
•
A neural network is used to predict volatility
Volatility
•
A measure of price movement
•
Often used to ascertain risk
•
Ability to forecast volatility gives a
trader a significant advantage in
determining options premiums
Calculating Volatility
•
There are two main approaches to estimating
and predicting the non

constant volatility
•
The historical approach
–
However this assumes that future volatility will
not change and that history will repeat itself
•
The implied volatility approach
–
One solves the Black

Scholes model for the
volatility that yields the observed call price
Neural Networks
•
Layers of interconnected nodes
•
Constructed in three layers
•
Sigmoid function applied to sum of weighted
inputs at each node
•
Connection weights are learned by the
network through a training process by looking
at training set examples
Neural Network Architecture:
Nodes, Connections, & Weights
Each node in the
hidden
&
output
layers applies a
function to the sum of the
weighted inputs.
w1
w2
w3
w16
w17
w18
w19
w20
w21
F(sum inputs*weights)=node output
F(sum inputs*weights)=output
Data
•
S&P 100 (OEX)
•
Daily closing call and put prices
•
Associated exercise prices closest to at

the

money
•
S&P 100 Index prices
•
Call and put volume
•
Call and put open interest
•
250 observations for six series of volatilities
Comparison of Historical and
Implied Volatility Estimates
Neural Network and Implied
Volatility Estimates
Results
•
Historical volatility is only backward looking
•
Implied volatility provides estimates which are
only valid at that current time
•
Neural network volatility uses both short

term
historical knowledge and contemporaneous
variables in the estimate
•
NN predictions can be made for a full trading
cycle and are more accurate
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