P1.T4.Valuation Allen, Chapters 2 & 3 Bionic Turtle FRM Video Tutorials By

52 Pages·2013·0.91 MB·English

P1.T4.Valuation Allen, Chapters 2 & 3
Bionic Turtle FRM Video Tutorials
By: David Harper CFA, FRM, CIPM
Note: This tutorial is for paid members only. You know who you are.
Anybody else is using an illegal copy and also violates GARP’s ethical
standards.
P1.T4.Valuation Allen, Chapters2 & 3
P1.T4.Valuation Allen, Chapters 2 & 3
Linda Allen, Jacob Boudoukh and Anthony Saunders, Understanding Market,
Credit and Operational Risk: The Value at Risk Approach
• Chapter 2: Quantifying Volatility in VaR Models
• Chapter 3, Putting VaR to Work
2
P1.T4.Valuation Allen, Chapters2 & 3
Related Learning Spreadsheets
Exam Relevance
Workbook (XLS not topic)
T4.Allen.2 VaR High Portfolio VaR
(incl Jorion’s 3-asset delta normal VaR)
Medium Taylor Series Approx.
(with BSM option value)
Low Structured Monte Carlo Sim
(With Cholesky decomposition)
3
Chapter 2: Quantifying Volatility in VaR Models
P1.T4. Allen, Chapter 2: QuantifiyingVolatility in VaRModels
Discuss how asset return distributions tend to deviate from the normal
distribution. …
Normal Actual returns
Symmetrical Skewed
“Normal” Tails Fat-tailed (leptokurtosis)
Stable Unstable (time-varying)
-4 -3 -2 -1 0 1 2 3 4
5
P1.T4. Allen, Chapter 2: QuantifiyingVolatility in VaRModels
Discuss how asset return distributions tend to deviate from the normal
distribution. …
AAccttuuaall rreettuurrnnss::
11.. SSkkeewweedd
22.. FFaatt--ttaaiilleedd ((kkuurrttoossiiss>>33))
4.5% 33rrdd MMoommeenntt == 33.. UUnnssttaabbllee
SSkkeeww •• 33
4.0%
3.5%
3.0%
2.5%
44tthh MMoommeenntt ==
2.0%
22nndd VVaarriiaannccee
1.5% kkuurrttoossiiss •• 44
““ssccaallee””
1.0%
0.5%
11sstt mmoommeenntt
0.0%
-3 -2 -1 MM0eeaann 1 2 3
““llooccaattiioonn””
6
P1.T4. Allen, Chapter 2: QuantifiyingVolatility in VaRModels
Explain potential reasons for the existence of fat tails in a return distribution …
1. Conditional mean is time-varying
– Unlikely given assumption of efficient markets (says L. Allen et al)
2. Conditional volatility is time-varying
– This is the likely explanation!
-4 -3 -2 -1 0 1 2 3 4
7
P1.T4. Allen, Chapter 2: QuantifiyingVolatility in VaRModels
… and discuss the implications fat tails have on analysis of return distributions.
NNoorrmmaall ddiissttrriibbuuttiioonn ssaayyss::
--1100%% @@ 9955tthh %%iillee
-3 -2 -1 0 1 2 3
BBuutt wwhhaatt iiff rreeaallllyy iitt’’ss mmoorree lliikkee --
1155%% ??!!
8
P1.T4. Allen, Chapter 2: QuantifiyingVolatility in VaRModels
Distinguish between conditional and unconditional distributions.
• A condition distribution depends on the information set; e.g., economy, market
shock
Same Distribution =Function [economic,
Regardless market conditions]
2 2
r ~ N(, ) r | I ~ N(, )
t t t1
9
P1.T4. Allen, Chapter 2: QuantifiyingVolatility in VaRModels
Discuss the implications regime switching has on quantifying volatility.
LLooww VVoollaattiilliittyy HHiigghh VVoollaattiilliittyy
RReeggiimmee RReeggiimmee
-4-3-2-10 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4
RReeggiimmee--sswwiittcchhiinngg:: llooww oorr hhiigghh vvoollaattiilliittyy tthhaatt iiss ccoonnddiittiioonnaall
((ee..gg..,, oonn eeccoonnoommiicc ssttaattee))
Model likely to lag/delay actual volatility!
(abrupt shift into new regime)
10