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Derivatives

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Derivatives
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Derivatives



Lecture 1:

Introduction to Derivatives &

Related Mathematical Concepts



Otto Khatamov

2010/2011

Course Rules

 You must read the module handbook that

contains important course information. It is

your responsibility to look at the information.



 I will post by e-mail the lectures notes two

days before the actual day. You are expected

to read them.



 I would like to encourage you to ask questions

during the course.

Course Rules

 Your success in this course will depend on

you achieving the learning goals

(and it‟s hard work).



 If you want to contact me, please send me an

e-mail to make an appointment.



 You do not need to buy a lot of books, if you

want to buy a book get „Hull‟. You can consult

the other books at the library.

Today’s Learning Outcomes

1. What is this course about?



2. Basics of derivative markets:

a) Who are the actors?

b) What products are traded?



3. What mathematical tools are needed

to understand derivatives?

Agenda

 What is a Derivative?





 Who are the Actors?





 What are the Market Structures?





 Introduction to Mathematics of

Derivatives.

Reading for today’s lecture

REQUIRED READING:

 Hull (2008), chapter 1





RECOMMENDED READING

 Jarrow, R. and S. Turnbull (2000), chapter 1

 Neftci, N., (2000), chapters 3 and 5

 Watsham and Parramore (1997), chapters 3

and 4

 McDonald, R. (2006), chapter 1 and appendix

B

What are Derivatives?

 A derivative is an instrument whose value

depends on, or is derived from, the value of

another asset.

 Literally a derivative instrument is a byproduct

of another (underlying) financial instrument.

 Derivatives fulfill different needs for the

economy.

 They are at the core of “financial engineering”.

 Examples: futures, forwards, swaps, options,

exotics…

Why Using Derivatives?

 To hedge risks.

 To speculate by using leverage.

 To lock in an arbitrage profit.

 To change the nature of a liability.

 To change the nature of an investment

without incurring the costs of selling one

portfolio and buying another.

Basic Types

 Forward

 For example interest rate, foreign exchange…

 Future

 For example oil, bonds…

 Swap

 For example interest rate, foreign exchange…

 Option

 For example stocks, bonds…

 Securitization

 For example mortgages, corporate debt…

Example

 On May 24, 2010 the treasurer of a

corporation enters into a long forward

contract to buy £1 million in six months

at an exchange rate of 1.4422

 This obligates the corporation to pay

$1,442,200 for £1 million on November

24, 2010

 What are the possible outcomes?

Derivative Markets

 Exchange-traded markets

 Standardized contracts

 Electronic trading or open outcry (a trading

floor)

 Limited credit risks



 Over-the-counter (OTC) markets

 Network of dealers

 Provide quotes for contracts tailored to specific

needs of clients

 Non-standardized contracts

 Trades subject to credit risks

Size of OTC & Exchange-Traded

Markets









Source: Bank for International Settlements. Chart shows total principal

amounts for OTC market and value of underlying assets for exchange market

Who Trades Derivatives

Companies Hedging



Banks Hedging/Speculation/Issue



Mutual Funds Speculation



Hedge Funds Arbitrage/Speculation

Are Derivatives Dangerous?

 ”In my view, derivatives are financial

weapons of mass destruction, carrying

dangers that, while now latent, are

potentially lethal.” Warren Buffett 2002



 Leverage Effect

 profit or losses can be very high.



 Complex

 Non-linear

 Sometimes the structure is opaque

 The pricing involves elaborate mathematics

Leverage

 If you invest 100$ in a stock, the

maximum that you can loose on this

investment is 100$.



 If you sell a 100$ future you can

loose much more than the initial

investment.

Lehman Brother’s

 Lehman Brothers‟ leverage before

their bankruptcy was 25 times.



 It means that the company could in

theory loose 25 times their capital.



 You know the end of the story!

Complexity

 Without entering into details an Asset

Backed Security (ABS) is a packaged

of debt assets.



 Each asset element is complex to

evaluate on its own.



 Some time one element of an ABS is

an other ABS.

Example: hedge risks

 An airline company need to buy fuel

for their jets. The company knows

how much it needs for the next

year.



 Buying forward contracts on fuel

can hedge them against the price of

oil.

Example:

Speculate by Using Leverage

 An investor believed that the price of oil is

going to go up in a month time, but do not

want to buy a lot of oil today and keep it for a

month.



 This investor can buy today a call option on oil

with maturity one month. The price of the

option is a small fraction of the underlying

price.



 What are the possibilities?

Example:

change the nature of a liability

 A company has a debt which is on

its balance sheet.



 This company can package this

debt with an asset, let say a factory

and sell it to an investor.

Example:

Portfolio Management

 A company has too much

exposure on the automobile

industry and wish to reduce it.



 Selling the portfolio and buying a

new one would be extremely costly

(broker fees, bid/offer).



 Selling an index on automobile

industry is a much cheaper option.

Mathematics

 Complexity of cash flows requires

elaborate mathematics.



 You cannot predict the future, you can

just calculate the probabilities of what

is likely to happen.



 Mathematics are at the core of modern

finance, whether in academia or in the

financial industry.

Basics

 I assume that you all know:

 Sets and Numbers.

 Functions theory

 Exponential logarithm functions

 Limit of functions

 Basic probability

If you do not have those basics

you should either spend a lot of time catching up,

or you should not take this course!

Differentiation

 Definition of a differentiation









It is useful to describe trend

Integration

 Definition of integration









Basis of probability theory

Differential Equations

 Finance is based on differential

equations of the form:









A solution is a function f that satisfies this

equation

Probability

 Bayesian probability is a measure of the

likelihood of an event compared with all

possible states of the world.



 For example the likelihood that a dice

rolls on the number 6 is:

Expectation

 Expectation is a way of deciding what is the

probability of gain.



 For example a coin toss were you win 1 if it is

head and -1 if it is tail is:







 So if the coin is fair then the expectation of gain is

0.

 If the coin is not fair and there is a probability of

head is 55% then the expectation of gain is 0,55

Normal Density Function

 Fundamental tool for derivatives







 The probability density function of a

continuous random variable X that is

normally distributed with mean μ and

variance σ2 is:

Markov Processes

 The past “path” followed by the variable is

independent of the probability distribution of the

value in the future.

 Only the current value of the variable is relevant

for predicting the future.

 That is why non-Markov processes are known as

path dependent.

 Modeling financial prices as Markov stochastic

processes implies that we cannot tell anything

about the asset's price tomorrow by using its

price history up to today.

 Markov stochastic processes are thus consistent

with the weak form of the market efficiency

hypothesis.

Wiener Processes

 A Wiener process (W) is a particular type of

Markov process that has the following

properties:





 is almost surely continuous

 has independent increments with

distribution

Stochastic Processes

 A stochastic process, also called random

process, with state space X is a collection of

X-valued random variables indexed by a set T

(time).



 Stochastic processes can also be categorized

as discrete-variable or continuous-variable:

 a continuous-variable process: the variable can take

any value within a certain range

 a discrete-variable stochastic process: only certain

values are possible

Martingale

 A discrete-time martingale is a discrete-

time stochastic process (i.e., a sequence of

random variables) X1, X2, X3, ... that

satisfies for all n:







 This can be interpreted as: The best

approximation of the future value of a

random variable is the current value.

Option Equation

 We note the value at maturity of

the underling and K the strike



 The cash flow at maturity for a call

option is:

Interest Calculation

You want to invest an amount A for T

years at an interest rate of r.

 Simple Interest Calculation







 Compound Interest Calculation

Modeling Asset Prices



 No one knows the future, but it

is possible to calculate the

expected probability of gain.

Modeling Asset Prices





 What is the difference between a

bookmaker and a derivatives

trader?


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