RISK MANAGEMENT - Management
Required textbook: Hull, J. (2018) “Risk Management and Financial Institutions” (5th edition).
FIN 562 Winter 2021 Final Project
The questions relate to chapters and respective lectures of 23, 24, 25, 27, 28, & 29 of the Hull book. If you have any calculations, please show them in your answers.
1)
Consider the following three events:
1) An insurance company loses $5 billion due to an earthquake in California.
2) An auto manufacturer loses $5 billion from a lawsuit relating to safety flaws of a new car
design.
3) A food processing firm loses $5 billion from a hedging trader speculating in the coffee and
sugar markets.
You have equal investments in the three firms. Which loss or losses are you more concerned about? Why or why not are you concerned? Are there any risk management issues you can point to in these firms?
2)
A hedge fund wants to purchase 100 shares of company X. The bid = $70, offer = $80. They also want to purchase 200 shares of company Y. The bid = $15, offer = $25.
1) What is the proportional bid–offer spread of each company?
2) What is the midmarket total value of each position?
3) What is the cost to the hedge fund to unwind the portfolio?
4) If the bid–offer spreads are normally distributed with mean $10 and standard deviation $3,
what is the 99\% worst-case cost of unwinding the position in the future?
3)
You are consulting to a financial institution that uses an imprecise model for pricing and hedging a particular type of structured product.
1) Discuss how, if at all, it is likely to realize its mistake.
2) As the risk management consultant, what procedures would you recommend mitigating the
institution’s model risk?
4)
A funds risk appetite wants to be 90\% certain it will not lose more than 20\% in any one year. Based on the following variables, calculate the beta the fund should have.
R = -0.20, RF = 0.03, RM = 0.082, M = 0.15, and p = 0.9 5)
When thinking about the cycle of innovation, discuss the following items:
1) What causes disintermediation?
2) Name three fintech items and what is their impact on traditional intermediaries
3) What is a common reason for financial institutions to reject fintech products and what can a
fintech firm do to reduce rejection risk?
5)
You are a risk consultant to a financial institution. Your client asks you to respond to the following two questions:
1) What are the two common factors for firms to have large losses?
2) List and discuss at least four actions you recommend the client to take to avoid risk
management mistakes.FIN 562
Risk
Management
Professor Mark Shore
DePaul University
Week 7 Lecture Notes
[email protected]
Twitter @shorecap
mailto:[email protected]
Due Diligence
• The examples are related to money managers
• However, it can be applied to varying industries
• What can go wrong?
Dictionary Definition: Due Diligence
1) “law : the care that a reasonable person exercises to avoid
harm to other persons or their property failed to exercise due
diligence in trying to prevent the accident”
2) “business : research and analysis of a company or
organization done in preparation for a business transaction
(such as a corporate merger or purchase of securities)”
“the care that a reasonable person takes to avoid harm to
other persons or their property”
The phrase can be traced back to the 1500s
Can you relate this to your firm or industry?
https://www.merriam-webster.com/dictionary/due\%20diligence#note-1
https://www.merriam-webster.com/dictionary/due\%20diligence#note-1
Due Diligence of a Fund Manager
Front Office
• Research – understand their research process
• Strategy – understand their strategy & risk mgmt. (manage
expectations)
• Capacity Issues – estimate of limits to their AUM based on their
strategy & markets traded
• Track Record/ Metrics
Back office
• Operational / back office
• Know the firm
• Backup plan / disaster recovery
• Key Personnel Risk
• Background of senior management
• Regulatory issues of firm & individuals –from NFA BASIC
https://www.nfa.futures.org/BasicNet/basic-search-
results.aspx?rnd=5618.661775151415
https://www.nfa.futures.org/BasicNet/basic-search-results.aspx?rnd=5618.661775151415
Due Diligence Process
• Due diligence of a manager is analogous to analysis of a
company or stock
• Analysis of a company
• Product / Service – (Portfolio/ Front office)
• Understand the product (portfolio)
• Examine portfolio metrics
• How does it compare to their competitors (Peers)
(Back Office / Operations)
• Management – who are they? Backgrounds of mgmt?
• Revenue – how is it generated? From AUM
• Will the revenue support their operations?
• Size of the firm/ Infrastructure / info on firm
• Add up the pros and cons and decide to allocate or not
(quantitative and qualitative information)
Importance of Operational Due Diligence
• After 9/11 (2001)
- Does the firm have a disaster recovery plan?
• After Madoff (2008) $65 billion stolen from investors & $18
billion in investor losses (Ponzi scheme)
-Who are the auditors and administrators of the
fund?
-Potential conflicts of interest?
- Are items handled internally or by third parties?
• After MF Global bankruptcy (2011/2012) about $1.6 billion in
customer losses
-Does the fund have backup brokerage firms?
• Operational due diligence has become as important or more
important than front office due diligence
Feffer & Kundro (2003):
-Studied over 100 fund liquidations over 20 years:
At least 50\% of hedge fund failures are due to operational FIN 562
Risk
Management
Professor Mark Shore
DePaul University
Week 8 Lecture Notes
[email protected]
Twitter @shorecap
mailto:[email protected]
Liquidity Risk
2 Types of Liquidity Risk
• Liquidity Trading Risk
➢Relates to ability to enter and exit the market
• Liquidity Funding Risk
➢Relates to ability to pay obiligations
Liquidity Trading Risk
• More liquid = smaller bid – ask spread
➢Less slippage = lower cost to enter or exit the position
➢Less costly to enter and exit a market position
Liquidity Trading Risk
• Factors for Price depends on:
• The mid market price
• Quantity = How much is to be sold
• Time = How quickly it is to be sold
• The economic environment
• Financial Crisis transparency is also a factor that affects
liquidity: Mortgages:
• More transparency = greater understanding of involved risk
• Unacceptable risks = reduced demand= less volume = wider bid-
ask spreads = costlier to enter and exit the market
• 2007 investors were using credit ratings in place of
understanding the risks
• When confidence declines; liquidity declines
• Sellers market
• Buyers market
• Predatory trading
Bid-Offer Spread As a Function of
Quantity (Figure 24.1, page 539)
Risk Management and Financial Institutions
5e, Chapter 24, Copyright © John C. Hull 2018
Offer Price
Bid Price
Quantity
100
units 500 or 1,000 units
Having many small positions instead of one large position, may reduce liquidity risk
Bid-Offer Spread (page 540-541)
Risk Management and Financial Institutions
5e, Chapter 24, Copyright © John C. Hull 2018
.instrumentth for the spreadoffer -bid
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p=20-15 = 5
S= (20-15)/17.5 = 29\%
Unwinding a Position Optimally
• Relative to a large position:
➢Unwound quickly = large bid-ask spreads
➢But the loss from a mid-market price is small
Or
➢Unwind the position over several days = smaller bid-ask spread
➢But the potential loss of mid-market price increases
Other Measures of Trading Liquidity
• Volume of trading per day = less liquidity: smaller volume
• Price impact of the size of a trade
• Absolute value of daily return divided by daily dollar
volume (suggested by Amihud in 2002)
Research shows that an asset’s expected return increases
as its liquidity decreases
Risk Management and Financial Institutions
5e, Chapter 24, Copyright © John C. Hull 2018
Liquidity Black Holes
Risk Management and Financial Institutions
5e, Chapter 24, Copyright © John C. Hull 2018
Liquidity Black Holes
• A liquidity black hole occurs when most market
participants want to take one side of the market
and liquidity dries up
• ExamplesFIN 562
Risk
Management
Professor Mark Shore
DePaul University
Week 9 Lecture Notes
[email protected]
Twitter @shorecap
mailto:[email protected]
Model Risk Management
Uses of Models
• Credit decisions and credit exposures
• Liquidity management
• Derivatives valuation
• Calculation of VaR, Expected shortfall and other risk
measures
• Asset management
• Fraud detection
• etc
Risk Management and Financial Institutions
5e, Chapter 25, Copyright © John C. Hull 2018
Models
• Models approximate reality
➢Ideally try to keep the models simple
➢Assumptions are built into the models
• Models are considered part of operational risk
• Models need to be:
• Documented
• Validated
• Used the way its intended
• Monitored
Regulatory Requirements
• April 2011 SR11-7 was published from the Fed to
provide guidance to banks on model risk management
• Model risk: “ the potential for adverse consequences
from decisions based on incorrect or misused model
outputs and reports”
• A systematic approach to managing model risk
• Model risk can arise from
➢fundamental errors involving data, calculations, numerical
procedures, and assumptions
➢Inappropriate use of the model
➢Not understanding the limitations of a model
SR 11-7 continued
• SR11-7 contains directives on
• Model development, testing, and documentation
• The role of model validation groups
• The use of vendor models
Risk Management and Financial Institutions
5e, Chapter 25, Copyright © John C. Hull 2018
Vendor Model
• Subject to the same validation as internal models
• Have a process to choose vendor models
• Does the vendor conduct ongoing monitoring of their
models?
• Have information on data used to develop the model
• Contingency plan if the vendor stops supporting the
model or goes out of business?
➢Vendor should make available proprietary data
Risk Management and Financial
Institutions 5e, Chapter 25,
Copyright © John C. Hull 2018
Finance vs Physics (page 572)
• The models of physics describe physical processes
and are highly accurate. Their parameters do not
change through time.
• The models of finance describe human behavior.
They are at best approximations. Parameters do
change through time
Developing & Testing Models
• Marking to Market
➢Valuation based on market prices
• Marking to Model
➢Valuation based on model pricing
➢Are assumptions built into the model?
• Stochastic Model
➢Model will have some randomness to the results; results can vary
• Deterministic Model
➢Model will have greater consistency (constant) of results
Marking the Prices of an
Instrument to Market
• Use prices quoted by market maker
• Use price at which financial institution has traded
product
• Use prices from SEFs (Swap Execution Facilities) or
interdealer brokers
• Use price indications
• Use model (marking to model)
Risk Management and Financial
Institutions 5e, Chapter 25,
Copyright © John C. Hull 2018
FIN 562
Risk
Management
Professor Mark Shore
DePaul University
Week 10 Lecture Notes
[email protected]
Twitter @shorecap
mailto:[email protected]
Financial Innovation
• Cash markets to forwards, futures, options, other
derivatives for commodities & financial, private equity
and public equity = innovation
• Choices of insurance policies = innovation
• Increased choices of credit and lending = innovation
• Information & data transfer from printed books, letters,
railroad, telegraph, telephone, radio, airplanes,
television to the internet and high-speed digital transfer
= innovation
➢Data & information is critical to financial services
• Fintech
The Cycle of Innovation
• Intermediaries = the function between two parties
➢“The middleman”
• Disintermediation = removal of the traditional
intermediary
➢Bank Teller
➢Stockbroker
• Reintermediation = new technology intermediaries
appear
➢ATM
➢Online trading
Fintech
Machine Learning (page 622-623)
• Branch of artificial intelligence that allows
computers to learn without being explicitly
programmed
➢Supervised Learning: builds rules to map inputs to
outputs
➢Unsupervised Learning: seeks patterns in the data
➢Reinforcement Learning: Has a goal/ task and learns the
task
• Uses tools to search for patterns. Some are
traditional statistical tools such as linear regression,
logistic regression and PCA
• An important tools that mirrors the way humans
recognize patterns are neural networks
Risk Management and Financial Institutions
5e, Chapter 28, Copyright © John C. Hull 2018
Examples of Applications
• Translation from one language to another
• Fraud recognition
• Lending decisions
• Driving a car
• Also used for:
➢Market trading patterns
➢Customer behavior
➢Algos for online behavior
Risk Management and Financial Institutions
5e, Chapter 28, Copyright © John C. Hull 2018
Blockchain
• A distributed ledger (spreadsheet)
• When updated a new block is added = a chain of
blocks
• A community of users can update it
• Hacking and fraud are extremely unlikely
• It is the technology behind bitcoin
➢Has other applications in finance, supply chain
management, law (smart contracts), regulation
• Permission = Private blockchains where people tend to
know and trust each other
• Permissionless = Non-restrictive on users and people
may not know or trust each other
Hashing 1
• Hashing converts text to a 64 string of numbers and letters
• Cannot be reversed
• Hashing is an important part of blockchain security
systems
• Blockchain records are made tamper proof with a hash
where one of the inputs is the hash of the immediately
preceding block
Risk Management and Financial Institutions
5e, Chapter 28, Copyright © John C. Hull 2018
Hashing 2
Hashing “Risk Management and Financial Institutions”
(without quotation marks) using SHA gives:
1dcc48387a27cd95378b08ab26261b161a97c51a7c9146f3d3f
f73710d656a3f
Adding the edition numbRisk Management and
Financial Institutions
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Risk Management and
Financial Institutions
Fifth Edition
John C. Hull
Cover images: © Roman Sigaev/Shutterstock;
© Rawpixel.com/Shutterstock; © Chalermsak/Shutterstock
Cover design: Wiley
Copyright © 2018 by John C. Hull. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
The Third Edition was published by John Wiley & Sons, Inc., in 2012. The first and second editions of this
book were published by Prentice Hall in 2006 and 2009, respectively.
Published simultaneously in Canada.
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