Verification of Financial Models

Verification of Financial Models: Duplication of Development Efforts? Alyona Lamash, Boris Rabinovich EXTENT October 2011

Contents
Introduction
Model and Implementation
Application
Technical Analysis
Derivatives Pricing
Implied Liquidity
Value at Risk
Model Risk in Modern Markets
Summary
Q&A

1.Introduction
Financial modeling:
Applying appropriate mathematical models to financial concepts
Verification of financial models:
Correctness of model implementation
Internal consistency of the model
Its correspondence to real life
Calibration (fine tuning)

2.Model and Implementation
Verification of Model:
Selecting assumptions
The risk to make an assumption
The impact of the assumption on your model
Calibrating the model

2.Model and Implementation (cont.)
Verification of Implementation:
Algorithm
Hardware capacity
Market conditions

3.Application
Examples of application:
Technical analysis
Derivatives pricing
Implied liquidity
Risk measurement (VaR)
Trading algorithms (robots)
Accounting

4.Technical Analysis

4.Technical Analysis (cont.)
Testing of technical analysis applications
Excel: basic strategies and P&L calculations
Test on historical data
Manually include patterns to the data
Then test complex strategies, trends, etc.
on artificially created market data

4.Technical Analysis (cont.)
Testing of technical analysis strategies
Firstly test on historical data (back-testing)
No full freedom in data manipulation
Simulate specific market conditions
(extra-ordinary, but still realistic)
Take into account:
Delay after the signal
Bid-Ask spread
Market impact

5.Derivatives Pricing
Derivative – financial product depending on another asset (underlying)
Derivative pricing validation
Internal consistency:
Call - Put = Forward (call-put parity)
American option > European option
Knock In + Knock Out = Vanilla
Geometric mean < arithmetical mean
Dependencies on parameters
Simple is a particular case of complex

6.Implied Liquidity
Implied order – a combination of existing orders in the market.
Errors and limitations: rounding, dual liability, etc
Bid 2Y Offer 5Y Offer 2Yv5Y

7.Value at Risk
1 day 99% confidence level VaR –
A loss from a portfolio which you are 99% sure will not be exceeded in one day
Historical VaR vs Variance/covariance VaR vs Monte-Carlo simulation
Tail loss
Stress testing
VaR

8.Model Risk in Modern Markets
QA (verification) to prevent errors in model and its implementation
Financial disasters when models failed

9.Summary
Verification gives another point of view on the problem
Helps to find errors in the algorithm
Reveals caveats in model and implementation
Appropriate method should be selected in order not to duplicate efforts but give additional value

10.Questions & Answers
Thank you.

Verification of Financial Models

by Iosif Itkin on Nov 13, 2011

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Verification of Financial Models — Presentation Transcript