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Primer Mathematics Financial Engineering Pdf

Financial Engineering Advanced Background Series

Published or forthcoming

1.

A

Primer

for

the

Mathematics

of

Financial

Engineering,

by

Dan

Stefanica

2.

Numerical Linear Algebra

Methods

for Financial Engineering Applica-

tions,

by

Dan

Stefanica

3. A

Probability

Primer

for

Mathematical

Finance,

by.Elena

Kosygina

4. Differential

Equations

with

Numerical

Methods

for Financial Engineering,

by

Dan

Stefanica

A PRIMER

for

the

MATHEMATICS

of

FINANCIAL ENGINEERING

DAN STEFANICA

Baruch College

City University of New York

FE PRESS

New York

FE

PRESS

New York

www.fepress.org

Information

on

this title: www.fepress.org/mathematicaLprimer

©Dan

Stefanica 2008

All

rights reserved. No

part

of

this

publication

may

be

reproduced, stored

in

a retrieval system, or

transmitted,

in

any form or

by

any means, electronic, mechanical,

photocopying, recording,

or

otherwise,

without

the

prior

written

permission

of

the

publisher.

First

published 2008

Printed

in

the

United

States

of America

ISBN-13 978-0-9797576-0-0

ISBN-IO 0-9797576-0-6

To Miriam

and

to

Rianna

Contents

List

of

Tables

Preface

Acknowledgments

How

to

Use

This

Book

O.

Mathematical

preliminaries

0.1

Even

and

odd

functions

0.2 Useful

sums

with

interesting proofs .

0.3 Sequences satisfying linear recursions

0.4

The

"Big

0"

and

"little

0"

notations

0.5 Exercises

........

.

1

Calculus

review.

Options.

1.1 Brief review

of

differentiation

1.2

Brief

review

of

integration

. .

1.3 Differentiating definite integrals

1.4 Limits

........

.

1.5

L'Hopital's

rule

........

.

1.6 Multivariable functions

....

.

1.6.1 Functions

of

two variables

1.

7

Plain

vanilla

European

Call

and

Put

options

1.8

Arbitrage-free

pricing

..........

.

1.9

The

Put-Call

parity

for

European

options

1.10 Forward

and

Futures

contracts.

1.11 References

1.12 Exercises

.........

.

Vll

xi

xiii

xv

xvii

1

1

4

8

12

15

19

19

21

24

26

28

29

32

34

35

37

38

40

41

viii

CONTENTS

2

Numerical

integration.

Interest

Rates.

Bonds.

45

2.1 Double

integrals.

. . . . . . . . .

45

2.2

Improper

integrals

..............

48

2.3 Differentiating improper integrals . . . . . .

51

2.4 Midpoint, Trapezoidal,

and

Simpson's

rules.

52

2.5 Convergence of Numerical

Integration

Methods

56

2.5.1 Implementation of numerical integration

methods

58

2.5.2 A concrete

example.

.

62

2.6

Interest

Rate

Curves . . . . . 64

2.6.1

Constant

interest

rates

66

2.6.2 Forward

Rates.

. . . .

66

2.6.3 Discretely compounded interest 67

2.7 Bonds. Yield, Duration, Convexity . .

69

2.7.1 Zero

Coupon

Bonds.

. . . . . .

72

2.8 Numerical implementation of

bond

mathematics

73

2.9 References 77

2.10 Exercises .

78

3

Probability

concepts.

Black-Scholes

formula.

Greeks

and

Hedging.

81

3.1 Discrete probability

concepts.

. . . . . . . .

81

3.2 Continuous probability

concepts.

. . . . . .

83

3.2.1 Variance, covariance,

and

correlation

85

3.3

The

standard

normal variable 89

3.4 Normal

random

variables . . .

91

3.5

The

Black-Scholes

formula.

. 94

3.6

The

Greeks of

European

options.

97

3.6.1 Explaining

the

magic

of

Greeks

computations

99

3.6.2 Implied volatility . . . . . . . . . . . . 103

3.7

The

concept of hedging.

~-

and

r-hedging

. 105

3.8 Implementation of

the

Black-Scholes

formula.

108

3.9 References 110

3.10

Exercises.

. . . . . . . . . . . . . . . . . . .

111

4

Lognormal

variables.

Risk-neutral

pricing.

117

4.1 Change of probability density for functions of

random

variables 117

4.2 Lognormal

random

variables . 119

4.3

Independent

random

variables . . . . . . . . . . . . . . . . . .

121

IX

4.4 Approximating sums of lognormal variables . 126

4.5 Power series . . . . . . . . . . . . . 128

4.5.1 Stirling's formula . . . . . . . . . 131

4.6 A lognormal model for asset prices

...

132

4.7

Risk-neutral

derivation

of

Black-Scholes 133

4.8

Probability

that

options expire in-the--money 135

4.9

Financial

Interpretation

of

N(d

1

)

and

N(d

2

)

137

4.10 References 138

4.11 Exercises . . . 139

5

Taylor's

formula.

Taylor

series.

143

5.1 Taylor's Formula for functions

of

one variable 143

5.2 Taylor's formula for multivariable

functions.

. 147

5.2.1 Taylor's formula for functions

of

two variables 150

5.3 Taylor series expansions

..

. . . . . . . . . 152

5.3.1 Examples

of

Taylor series expansions . 155

5.4 Greeks

and

Taylor's formula . . . . . . . . . . 158

5.5 Black-Scholes formula: ATM

approximations.

160

5.5.1 Several ATM approximations formulas 160

5.5.2 Deriving

the

ATM approximations formulas

161

5.5.3

The

precision of

the

ATM

approximation

of

the

Black-

Scholes formula . . . . . . . . . . . . . 165

5.6 Connections between

duration

and

convexity . 170

5.7 References 172

5.8

Exercises..................

173

6

Finite

Differences.

Black-Scholes

PDE.

177

6.1 Forward, backward, central finite differences 177

6.2

Finite

difference solutions of

ODEs

. . . . . 180

6.3

Finite

difference approximations for

Greeks.

190

6.4

The

Black-Scholes

PDE

. . . . . . . . . . .

191

6.4.1 Financial

interpretation

of

the

Black-Scholes

PDE

. 193

6.4.2

The

Black-Scholes

PDE

and

the

Greeks 194

6.5 References

6.6

Exercises......................

7

Multivariable

calculus:

chain

rule,

integration

by

substitu-

195

196

tion,

and

extrema.

203

7.1

Chain

rule for functions

of

several

variables.

. . . . . . . . . . 203

x

CONTENTS

7.2 Change of variables for double integrals

.....

.

7.2.1 Change of Variables

to

Polar

Coordinates.

7.3 Relative

extrema

of multivariable functions .

7.4

The

Theta

of a derivative security . .

7.5

Integrating

the

density function of Z

...

.

7.6

The

Box-Muller

method

..........

.

7.7

The

Black-Scholes

PDE

and

the

heat

equation.

7.8 Barrier options . . . . . . .

7.9

Optimality

of early exercise

7.10 References

7.11

Exercises.

. . . . .

8

Lagrange

multipliers.

Newton's

method.

Implied

volatil-

ity.

Bootstrapping.

8.1 Lagrange multipliers

..............

.

8.2 Numerical

methods

for 1-D nonlinear

problems.

8.2.1 Bisection

Method

8.2.2 Newton's

Method

............

.

8.2.3 Secant

Method

. . . . . . . . . . . . . .

8.3 Numerical

methods

for

N-dimensional

problems

8.3.1

The

N-dimensional

Newton's

Method

8.3.2

The

Approximate

Newton's

Method.

8.4

Optimal

investment portfolios

8.5

Computing

bond

yields . . . . . . . . . . .

8.6 Implied volatility

.............

.

8.7

Bootstrapping

for finding zero

rate

curves

8.8 References

8.9

Exercises.

Bibliography

Index

205

207

208

216

218

220

221

225

228

230

231

235

235

246

246

248

253

255

255

258

260

265

267

270

272

274

279

282

List

of

Tables

2.1

Pseudocode

for Midpoint

Rule.

. 59

2.2 Pseudocode for Trapezoidal Rule 59

2.3 Pseudocode for Simpson's Rule . 60

2.4 Pseudocode for computing

an

approximate

value of

an

integral

with

given tolerance

.........................

61

2.5 Pseudocode for computing

the

bond

price given

the

zero

rate

curve 74

2.6 Pseudocode for computing

the

bond

price given

the

instantaneous

interest

rate

curve

..........................

75

2.7 Pseudocode for computing

the

price,

duration

and

convexity of a

bond

given

the

yield

of

the

bond

...............

77

3.1 Pseudocode for

computing

the

cumulative

distribution

of Z 109

3.2 Pseudocode for Black-Scholes formula 109

8.1 Pseudocode for

the

Bisection

Method

247

8.2 Pseudocode for Newton's

Method.

. 250

8.3 Pseudocode for

the

Secant

Method

. 254

8.4 Pseudocode for

the

N-dimensional

Newton's

Method

257

8.5 Pseudocode for

the

N-dimensional

Approximate

Newton's

Method

259

8.6 Pseudocode for computing a

bond

yield.

. . 266

8.7 Pseudocode for

computing

implied volatility . . . . . . . . . . . . 269

Xl

Preface

The

use

of

quantitative

models

in

trading

has

grown

tremendously

in

recent

years,

and

seems likely

to

grow

at

similar speeds

in

the

future, due

to

the

availability

of

ever faster

and

cheaper

computing

power. Although

many

books

are

available for anyone

interested

in

learning

about

the

mathematical

models

used

in

the

financial industry,

most

of

these

books

target

either

the

finance

practitioner,

and

are

lighter

on

rigorous

mathematical

fundamentals,

or

the

academic scientist,

and

use high-level

mathematics

without

a clear

presentation

of

its

direct financial applications.

This

book

is

meant

to

build

the

solid

mathematical

foundation

required

to

understand

these

quantitative

models, while

presenting

a large

number

of

financial applications. Examples range from

Put-Call

parity,

bond

duration

and

convexity,

and

the

Black-Scholes model,

to

more

advanced topics, such as

the

numerical

estimation

of

the

Greeks, implied volatility,

and

bootstrapping

for finding

interest

rate

curves.

On

the

mathematical

side, useful

but

some-

times overlooked topics

are

presented

in

detail: differentiating integrals

with

respect

to

nonconstant

integral limits, numerical

approximation

of

definite

integrals, convergence

of

Taylor series, finite difference approximations, Stir-

ling's formula,

Lagrange

multipliers,

polar

coordinates,

and

Newton's

method

for multidimensional problems.

The

book

was designed so

that

someone

with

a solid knowledge

of

Calculus should

be

able

to

understand

all

the

topics pre-

sented.

Every

chapter

concludes

with

exercises

that

are

a

mix

of

mathematical

and

financial questions,

with

comments

regarding

their

relevance

to

practice

and

to

more

advanced topics.

Many

of

these

exercises are,

in

fact, questions

that

are frequently asked

in

interviews for

quantitative

jobs

in

financial in-

stitutions,

and

some

are

constructed

in

a sequential fashion, building

upon

each

other,

as is

often

the

case

at

interviews.

Complete

solutions

to

most

of

the

exercises

can

be

found

at

http://www.fepress.org/

This

book

can

be

used as a companion

to

any

more

advanced

quantitative

finance book.

It

also makes a good reference

book

for

mathematical

topics

that

are

frequently assumed

to

be

known

in

other

texts,

such as Taylor expan-

sions, Lagrange multipliers, finite difference approximations,

and

numerical

methods

for solving nonlinear equations.

This

book

should

be

useful

to

a large audience:

Prospective

students

for financial engineering (or

mathematical

finance)

xiii

xiv

PREFACE

programs

will find

that

the

knowledge

contained

in

this

book

is

fundamental

for

their

understanding

of more advanced courses

on

numerical

methods

for

finance

and

stochastic calculus, while

some

of

the

exercises will give

them

a

flavor

of

what

interviewing for

jobs

upon

graduation

might

be

like.

For finance practitioners, while

parts

of

the

book

will

be

light reading,

the

book

will also provide new

mathematical

connections (or present

them

in

a

new

light) between financial

instruments

and

models used

in

practice,

and

will

do

so

in

a rigorous

and

concise

manner.

For

academics teaching financial

mathematics

courses,

and

for

their

stu-

dents,

this

is a rigorous reference

book

for

the

mathematical

topics required

in

these

courses.

For

professionals interested

in

a career

in

finance

with

emphasis

on

quan-

titative

skills,

the

book

can

be

used

as a

stepping

stone

toward

that

goal,

by

building a solid

mathematical

foundation

for

further

studies, as well as

providing a first insight

in

the

world

of

quantitative

finance.

The

material

in

this

book

has

been

used

for a

mathematics

refresher course

for

students

entering

the

Financial Engineering

Masters

Program

(MFE)

at

Baruch

College,

City

University

of

New York.

Studying

this

material

be-

fore

entering

the

program

provided

the

students

with

a solid background

and

played

an

important

role

in

making

them

successful graduates: over 90

per-

cent

of

the

graduates

of

the

Baruch

MFE

Program

are

currently

employed

in

the

financial industry.

The

author

has

been

the

Director

of

the

Baruch

College

MFE

Program

1

since

its

inception

in

2002.

This

position

gave

him

the

privilege

to

inter-

act

with

generations of

students,

who were exceptional

not

only

in

terms

of

knowledge

and

ability,

but

foremost as

very

special friends

and

colleagues.

The

connection

built

during

their

studies

has

continued over

the

years,

and

as

alumni

of

the

program

their

contribution

to

the

continued success

of

our

students

has

been

tremendous.

This

is

the

first

in

a series of

books

containing

mathematical

background

needed for financial engineering applications,

to

be

followed

by

books

in

N u-

merical

Linear

Algebra, Probability,

and

Differential Equations.

Dan

Stefanica

New York, 2008

IBaruch

MFE

Program

web page:

http://www.baruch.cuny.edu/math/masters.html

QuantNetwork student forum web page:

http://www.quantnet.org/forum/index.php

Acknow

ledgments

I have

spent

several wonderful years

at

Baruch

College, as Director

of

the

Financial Engineering Masters

Program.

Working

with

so

many

talented

students

was a privilege, as well as a learning experience

in

itself,

and

see-

ing a

strong

community

develop

around

the

MFE

program

was incredibly

rewarding.

This

book

is

by

all accounts a direct

result

of interacting

with

our

students

and

alumni,

and

I

am

truly

grateful

to

all

of

them

for this.

The

strong

commitment

of

the

administration

of

Baruch

College

to

sup-

port

the

MFE

program

and

provide

the

best

educational

environment

to

our

students

was essential

to

all

aspects

of

our

success,

and

permeated

to

creating

the

opportunity

for

this

book

to

be

written.

I

learned

a

lot

from working alongside

my

colleagues

in

the

mathematics

department

and

from

many

conversations

with

practitioners

from

the

finan-

cial

industry

..

Special

thanks

are

due

to

Elena

Kosygina

and

Sherman Wong,

as well as

to

my

good

friends

Peter

Carr

and

Salih Neftci.

The

title

of

the

book

was suggested

by

Emanuel

Derman,

and

is

more

euphonious

than

any

previously considered alternatives.

Many

students

have looked over

ever-changing

versions of

the

book,

and

their

help

and

encouragement were

greatly

appreciated.

The

knowledgeable

comments

and

suggestions

of

Robert

Spruill

are

reflected

in

the

final ver-

sion

of

the

book, as

are

exercises suggested

by

Sudhanshu

Pardasani.

Andy

Nguyen

continued

his

tremendous

support

both

on

QuantNet.org,

hosting

the

problems solutions,

and

on

the

fepress.org website.

The

art

for

the

book

cover is

due

to

Max

Rumyantsev.

The

final effort

of

proofreading

the

mate-

rial was

spareheaded

by

Vadim Nagaev,

Muting

Ren,

Rachit

Gupta,

Claudia

Li, Sunny Lu,

Andrey

Shvets, Vic Siqiao,

and

Frank

Zheng.

I would have never

gotten

past

the

lecture

notes

stage

without

tremen-

dous

support

and

understanding

from my family.

Their

smiling presence

and

unwavering

support

brightened

up

my

efforts

and

made

them

worthwhile.

This

book

is

dedicated

to

the

two ladies

in

my

life.

Dan

Stefanic a

New York, 2008

xv

How

to

Use

This

Book

While

we

expect

a large audience

to

find

this

book

useful,

the

approach

to

reading

the

book

will

be

different

depending

on

the

background

and

goals

of

the

reader.

Prospective

students

for financial engineering

or

mathematical

finance pro-

grams

should find

the

study

of

this

book

very

rewarding, as

it

will give

them

a

head

start

in

their

studies,

and

will provide a reference

book

throughout

their

course

of

study. Building a solid

base

for

further

study

is of

tremen-

dous

importance.

This

book

teaches core concepts

important

for a successful

learning experience

in

financial engineering

graduate

programs.

Instructors

of

quantitative

finance courses will find

the

mathematical

topics

and

their

treatment

to

be

of

greatest

value,

and

could use

the

book

as a

reference

text

for a

more

advanced

treatment

of

the

mathematical

content

of

the

course

they

are

teaching.

Instructors

of

financial

mathematics

courses will find

that

the

exercises

in

the

book

provide novel assignment ideas. Also, some topics might

be

non-

traditional

for

such

courses,

and

could

be

useful

to

include

or

mention

in

the

course.

Finance

practitioners

should enjoy

the

rigor

of

the

mathematical

presentation,

while finding

the

financial examples interesting,

and

the

exercises a

potential

source for interview questions.

The

book

was

written

with

the

aim

of

ensuring

that

anyone thoroughly

studying

it

will have a

strong

base for

further

study

and

full

understanding

of

the

mathematical

models used

in

finance.

A

point

of

caution:

there

is a significant difference between

studying

a

book

and

merely reading it. To benefit fully from

this

book, all exercises

should

be

attempted,

and

the

material

should

be

learned as if for

an

exam.

Many

of

the

exercises have

particular

relevance for people who will inter-

view for

quantitative

jobs,

as

they

have a flavor similar

to

questions

that

are

currently

asked

at

such interviews.

The

book

is sequential

in

its

presentation,

with

the

exception

of

Chapter

0, which

can

be

skipped

over

and

used as a collection

of

reference topics.

XVll

xviii

HOW

TO

USE

THIS

BOOK

Chapter

0

Mathematical

preliminaries

Even

and

odd

functions.

Useful

sums

with

interesting proofs.

Sequences satisfying linear recursions.

The

"Big

0"

and

"little

0"

notations.

This

chapter

is a collection

of

topics

that

are

needed

later

on

in

the

book,

and

may

be

skipped

over

in

a first reading.

It

is also

the

only

chapter

of

the

book

where no financial applications are presented.

Nonetheless, some

of

the

topics

in

this

chapter

are

rather

subtle

from a

mathematical

standpoint,

and

understanding

their

treatment

is instructive.

In

particular,

we include a discussion of

the

"Big

0"

and

"little

0"

notations,

i.e.,

0(·)

and

0('), which are often a source

of

confusion.

0.1

Even

and

odd

functions

Even

and

odd

functions are special families

of

functions whose graphs exhibit

special symmetries. We present several simple

properties

of

these

functions

which will

be

used subsequently.

Definition

0.1.

The

function

f :

~

-7

~ is an even

function

if

and only

if

f(

-x)

=

f(x),

V x E

~.

(1)

The

graph

of

any

even function is

symmetric

with

respect

to

the

y-axis.

Example:

The

density

function f

(x)

of

the

standard

normal

variable, i.e.,

2

MATHEMATICAL

PRELIMINARIES

is

an

even function, since

1

_(_x)2

f(-x)

=

-e

2

V2ir

see section 3.3 for more properties

of

this

function.

= f(x);

o

Lemma

0.1.

Let

f(x)

be

an

integrable

even

function. Then,

1:

j(

x) dx =

1"

j (x) dx, V a E

R.,

and

therefore

I:

j (x) dx = 2

1"

j (x), V a E

R..

Moreover,

if

Jo

oo

f (x)

dx

exists,

then

I:

j(x)

dx =

f'

j(x)

dx,

and

I:

j(x)

dx = 2

f'

j(x).

(2)

(3)

(4)

(5)

Proof. Use

the

substitution

x =

-y

for

the

integral

on

the

left

hand

side

of (2).

The

end

points x =

-a

and

x = 0 change into y = a

and

y = 0,

respectively,

and

dx

=

-dy.

We conclude

that

1

0

f(x)

dx

= r

O

f(

-v)

(-dy)

= r

a

f(

-v)

dy

= r

a

f(y)

dy, (6)

-a

Ja Jo Jo

since

f(

-V)

=

f(y);

cf.

(1). Note

that

y is

just

an

integrating variable.

Therefore, we can replace

y by x

in

(6)

to

obtain

1:

j(x)

dx =

1"

j(x)

dx.

Then,

t

j(x)

dx =

1:

j(x)

dx +

1"

j(x)

dx =

21"

j(x).

The

results (4)

and

(5) follow from (2)

and

(3) using

the

definitions (2.5),

(2.6),

and

(2.7) of improper integrals.

0.1.

EVEN

AND

ODD

FUNCTIONS

3

For example,

the

proof of (4)

can

be

obtained

using (2) as follows:

I:

j(x)

dx =

1

0

l-t

lim

f(x)

dx

=

Hm

f(x)

dx

t-+-oo

t

t-+-oo

0

lim

(t

f(x)

dx

=

roo

f(x)

dx.

t-+oo

Jo

Jo

D

Definition

0.2.

The

function

f :

JR

---+

JR

is

an

odd

function

if

and only

if

f(-x)

= -

f(x),

V x E

JR.

(7)

If

we let x = 0

in

(7), we find

that

f(O) = 0 for

any

odd

function

f(x).

Also,

the

graph

of

any

odd

function is symmetric

with

respect

to

the

point (0,0).

Lemma

0.2.

Let

f(x)

be

an

integrable odd

function.

Then,

I:

j(x)

dx =

0,

V a E

R..

(8)

Moreover,

if

Jo

oo

f (

x)

dx

exists,

then

1:

j(x)

dx =

o.

(9)

Proof. Use

the

substitution

x =

-y

for

the

integral from (8).

The

end points

x =

-a

and

x = a change into y = a

and

y =

-a,

respectively,

and

dx

=

-dy.

Therefore,

since

f(

-V)

= -

f(y);

cf. (7). Since y is

just

an

integrating

variable, we can

replace

y

by

x in (10),

and

obtain

that

t

j(x)

dx = - t

j(x)

dx.

We conclude

that

I:

j(x)

dx = O

..

The

result of (9) follows from (8)

and

(2.10).

D

Primer Mathematics Financial Engineering Pdf

Source: https://www.studmed.ru/view/stefanica-d-a-primer-for-the-mathematics-of-financial-engineering_2480331da04.html

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