Market risk analysis: 1 Quantitative methods in finance
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| Format: | Book |
| Language: | English |
| Published: |
Chichester [u.a.]
Wiley
2010
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| Edition: | Reprinted with corr. |
| Online Access: | Inhaltsverzeichnis Klappentext |
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| Physical Description: | XXVII, 290 S. graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 9780470998007 |
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| 020 | |a 9780470998007 |9 978-0-470-99800-7 | ||
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| 049 | |a DE-739 |a DE-29T |a DE-19 | ||
| 100 | 1 | |a Alexander, Carol |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Market risk analysis |n 1 |p Quantitative methods in finance |c Carol Alexander |
| 250 | |a Reprinted with corr. | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2010 | |
| 300 | |a XXVII, 290 S. |b graph. Darst. |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später ersch., unveränd. Nachdr. | ||
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| adam_text | Contents
List of Figures
xiii
List of Tables
xvi
List of Examples
xvii
Foreword
xix
Preface to Volume I xxiii
1.1
Basic Calculus for Finance
1
1.1.1
Introduction I
1.1.2
Functions and Graphs, Equations and Roots
3
1.1.2.1
Linear and Quadratic Functions
4
1.1.2.2
Continuous and Differentiable Real-Valued Functions
5
I
Л
.2.3
Inverse Functions
6
1.1.2.4
The Exponential Function
7
1.1.2.5
The Natural Logarithm
9
1.1.3
Differentiation and Integration
10
1.1.3.1
Definitions
10
1.1.3.2
Rules for Differentiation
И
1.1.3.3 Monotonie,
Concave and Convex Functions
13
1.1.3.4
Stationary Points and Optimization
14
1.1.3.5
Integration
15
1.1.4
Analysis of Financial Returns
16
1.
1.4.1
Discrete and Continuous Time Notation
16
1.1
.4.2
Portfolio Holdings and Portfolio Weights
17
1.1.4.3
Profit and Loss
19
1.1.4.4
Percentage and Log Returns
19
1.1.4.5
Geometric Brownian Motion
21
1.
1.4.6
Discrete and Continuous Compounding in Discrete Time
22
1.1.4.7
Period Log Returns in Discrete Time
23
1.1.4.8
Return on a Linear Portfolio
25
1.1.4.9
Sources of Returns
25
1.1.
5
Functions of Several Variables
26
1.1.5.1
Partial Derivatives: Function of Two Variables
27
1.1.5.2
Partial Derivatives: Function of Several Variables
27
Contents
1.1.5.3
Stationary
Points 28
1.1.5.4
Optimization
29
1.
1.5.5
Total Derivatives
31
1.1.6
Taylor Expansion
31
1.1.6.1
Definition and Examples
32
1.1.6.2
Risk Factors and their Sensitivities
33
1.1.6.3
Some Financial Applications of Taylor Expansion
33
1.1.6.4
MuMvariate Taylor Expansion
34
1.1.7
Summary and Conclusions
35
1.2
Essential Linear Algebra for Finance
37
1.2.1
Introduction
37
1.2.2
Matrix Algebra and its Mathematical Applications
38
1.2.2.1
Basic Terminology
38
1.2.2.2
Laws of Matrix Algebra
39
1.2.2.3
Singular Matrices
40
1.2.2.4
Determinants
41
1.2.2.5
Matrix Inversion
43
1.2.2.6
Solution of Simultaneous Linear Equations
44
1.2.2.7
Quadratic Forms
45
1.2.2.8
Definite Matrices
46
L2.3 Eigenvectors and Eigenvalues
48
1.2.3.1
Matrices as Linear Transformations
48
1.2.3.2
Fonnál
Definitions
50
1.2.3.3
The Characteristic Equation
51
1.2.3.4
Eigenvalues and Eigenvectors of a
2
χ
2
Correlation
Matrix
52
1.2.3.5
Properties of Eigenvalues and Eigenvectors
52
1.2.3.6
Using Excel to Find Eigenvalues and Eigenvectors
53
1.2.3.7
Eigenvalue Test for Defmiteness
54
1.2.4
Applications to Linear Portfolios
55
1.2.4.1
Covariance and Correlation Matrices
55
1.2.4.2
Portfolio Risk and Return in Matrix Notation
56
1.2.4.3
Positive Defmiteness of Covariance and Correlation
Matrices
58
1.2.4.4
Eigenvalues and Eigenvectors of Covariance and
Correlation Matrices
59
L2.5 Matrix Decomposition
61
1.2.5.1
Spectral Decomposition of a Symmetric Matrix
61
1.2.5.2
Similarity Transforms
62
1.2.5.3
Cholesky Decomposition
62
1.2.5.4
LU
Decomposition
63
1.2.6
Principal Component Analysis
64
1.2.6.
1 Definition of Principal Components
65
1.2.6.2
Principal Component Representation
66
1.2.6.3
Case Study: PCA of European Equity Indices
67
L2.7 Summary and Conclusions
70
Contents
1.3 Probabffity
and Statistics
71
1.3.1
Introduction
71
1.3.2
Basic Concepts
72
1.3.2.1
Classical versus Bayesian Approaches
72
1.3.2.2
Laws of Probability
73
1.3.2.3
Density and Distribution Functions
75
1.3.2.4
Samples and Histograms
76
1.3.2.5
Expected Value and Sample Mean
78
1.3.2.6
Variance
79
1.3.2.7
Skewness and Kurtosis
81
1.3.2.8
Quantises, Quartiles and Percentiles
83
1.3.3
Univariate Distributions
85
1.3.3.1
Binomial Distribution
85
1.3.3.2
Poisson
and Exponential Distributions
87
1.3.3.3
Uniform Distribution
89
1.3.3.4
Normal Distribution
90
1.3.3.5 Lognormal
Distribution
93
1.3.3.6
Normal Mixture Distributions
94
1.3.3.7
Student
t
Distributions
97
1.3.3.8
Sampling Distributions
100
1.3.3.9
Generalized Extreme Value Distributions
101
1.3.3.10
Generalized Pareto Distribution
103
1.3.3.11
Stable Distributions
105
1.3.3.12
Kernels
106
1.3.4
Multivariate Distributions
107
1.3.4.1
Divariate
Distributions
108
1.3.4.2
Independent Random Variables
109
1.3.4.3
Covariance
110
1.3.4.4
Correlation
111
1.3.4.5
Multivariate Continuous Distributions
114
1.3.4.6
Multivariate Normal Distributions
115
1.3.4.7
Bivariate Normal Mixture Distributions
116
1.3.4.8
Multivariate Student
t
Distributions
117
1.3.5
Introduction to Statistical Inference
118
1.3.5.1
Quantiles, Critical Values and Confidence Intervals
118
1.3.5.2
Central Limit Theorem
120
1.3.5.3
Confidence Intervals Based on Student
í
Distribution
122
1.3.5.4
Confidence Intervals for Variance
123
1.3.5.5
Hypothesis Tests
124
1.3.5.6
Tests on Means
125
1.3.5.7
Tests on Variances
126
1.3.5.8
Non-Parametric Tests on Distributions
127
1.3.6
Maximum Likelihood Estimation
130
1.3.6.1
The Likelihood Function
130
1.3.6.2
Finding me Maximum Likelihood Estimates
131
1.3.6.3
Standard Errors on Mean and Variance
Estimates
133
Contents
1.3.7
Stochastic Processes in Discrete and Continuous Time
134
1.3.7.1
Stationary and Integrated Processes in Discrete Time
134
1.3.7.2
Mean Reverting Processes and Random Walks in
Continuous Time
136
1.3.7.3
Stochastic Models for Asset Prices and Returns
137
1.3.7.4
Jumps and the
Poisson
Process
139
1.3.8
Summary and Conclusions
140
1.4
Introduction to Linear Regression
143
1.4.1
Introduction
143
1.4.2
Simple Linear Regression
144
1.4.2.1
Simple Linear Model
144
1.4.2.2
Ordinary Least Squares
146
1.4.2.3
Properties of the Error Process
148
1.4.2.4
ANOVA and Goodness of Fit
149
1.4.2.5
Hypothesis Tests on Coefficients
151
1.4.2.6
Reporting the Estimated Regression Model
152
1.4.2.7
Excel Estimation of the Simple Linear Model
153
1.4.3
Properties of OLS Estimators
155
1.4.3.1
Estimates and Estimators
155
1.4.3.2
Unbiasedness and Efficiency
156
1.4.3.3
Gauss-Markov Theorem
157
1.4.3.4
Consistency and Normality of OLS Estimators
157
1.4.3.5
Testing for Normality
158
L4.4 Multivariate Linear Regression
158
1.4.4.1
Simple Linear Mode! and OLS in Matrix Notation
159
1.4.4.2
General Linear Model
161
1.4.4.3
Case Study: A Multiple Regression
162
1.4.4.4
Multiple Regression in Excel
163
1.4.4.5
Hypothesis Testing in Multiple Regression
163
1.4.4.6
Testing Multiple Restrictions
166
1.4.4.7
Confidence Intervals
167
1.4.4.8
Multicoffinearity
170
1.4.4.9
Case Study: Determinants of Credit Spreads
171
1.4.4.10
Orthogonal Regression
173
1.4.5
Autocorrelation and Heteroscedasticity
175
1.4.5.1 Causes of Autocorrelation and Heteroscedasticity
175
1.4.5.2
Consequences of Autocorrelation and
Heteroscedasticity
176
1.4.5.3
Testing for Autocorrelation
176
1.4.5.4
Testing for Heteroscedasticity
177
1.4.5.5
Generalized Least Squares
178
1.4.6
Applications of Linear Regression in Finance
179
1.4.6.1
Testing a Theory
Î79
1.4.6.2
Analysing Empirical Market Behaviour
180
Ï.4.6.3
Optimal Portfolio Allocation
181
Contents
1.4.6.4
Regression-Based Hedge
Ratios
181
1.4.6.5
Trading on Regression Models
182
1.4.7
Summary and Conclusions
184
1.5
Numerical Methods in Finance
185
1.5.1
Introduction
185
1.5.2
Iteration
187
1.5.2.1
Method of Bisection
187
1.5.2.2
Newton-Raphson Iteration
188
1.5.2.3
Gradient Methods
191
1.5.3
Interpolation and Extrapolation
193
1.5.3.1
Linear and Bilinear Interpolation
193
1.5.3.2
Polynomial Interpolation: Application to Currency Options
195
1.5.3.3
Cubic Splines: Application to Yield Curves
197
1.5.4
Optimization
200
1.5.4.1
Least Squares Problems
201
1.5.4.2
Likelihood Methods
202
1.5.4.3
The EM Algorithm
203
1.5.4.4
Case Study: Applying the EM Algorithm to Nonnal Mixture
Densities
203
1.5.5
Finite Difference Approximations
206
1.5.5.1
First and Second Order Finite Differences
206
1.5.5.2
Finite Difference Approximations for the Greeks
207
1.5.5.3
Finite Difference Solutions to Partial Differential Equations
208
1.5.6
Binomial Lattices
210
1.5.6.1
Constructing the Lattice
211
1.5.6.2
Arbitrage Free Pricing and Risk Neutral Valuation
211
1.5.6.3
Pricing European Options
212
1.5.6.4 Lognormal
Asset Price Distributions
213
1.5.6.5
Pricing American Options
215
1.5.7
Monte Carlo Simulation
217
1.5.7.1
Random Numbers
217
1.5.7.2
Simulations from an Empirical or a Given Distribution
217
1.5.7.3
Case Study: Generating Time Series of
Lognormal
Asset
Prices
218
1.5.7.4
Simulations on a System of Two Correlated Normal Returns
220
1.5.7.5
Multivariate Normal and Student
t
Distributed
Simulations
220
1.5.8
Summary and Conclusions
223
1.6
Introduction to Portfolio Theory
225
1.6.1
Introduction
225
1.6.2
Utility Theory
226
1.6.2.1
Properties of Utility Functions
226
1.6.2.2
Risk Preference
229
1.6.2.3
How to Determine the Risk Tolerance of an Investor
230
1.6.2.4
Coefficients of Risk Aversion
231
xii Contents
1.6.2.5
Some Standard Utility Functions
232
1.6.2.6
Mean-Variance Criterion
234
1.6.2.7
Extension of the Mean—Variance Criterion to
Higher Moments
235
1.6.3
Portfolk
»
Allocation
237
1.6.3.1
Portfolio Diversification
238
1.6.3.2
Minimum Variance Portfolios
240
1.6.3.3
The
Markowitz
Problem
244
1.6.3.4
Minimum Variance Portfolios with Many Constraints
245
1.6.3.5
Efficient Frontier
246
1.6.3.6
Optimal Allocations
247
1.6.4
Theory
ι
of Asset Pricing
250
1.6.4.1
Capital Market Line
250
1.6.4.2
Capital Asset Pricing Model
252
L6.4.3
Security Market Line
253
1.6.4.4
Testing the CAPM
254
1.6.4.5
Extensions to CAPM
255
1.6.5
Risk Adjusted Performance Measures
256
1.6.5.1
CAPM RAPMs
257
1.6.5.2
Making Decisions Using the
Sharpe
Ratio
258
1.6.5.3
Adjusting the
Sharpe
Ratio for Autocorrelation
259
1.6.5.4
Adjusting the
Sharpe
Ratio for ffigher Moments
260
1.6.5.5
Generalized
Sharpe
Ratio
262
L6.5.6
Kappa Indices, Omega and Sortino Ratio
263
1.6.6
Summary and Conclusions
266
References
269
Statistical Tables
273
Index
279
List of Figures
1.1.1
A linear function
4
but different standard
1.1.2
The quadratic function
deviations
80
f(x)=4x2- + 3x + 2
5
13.6
(a) A normal density and a
1.13
The reciprocal function
6
leptokurtic density;
(b) a
1.1.4
The inverse of a function
7
positively skewed density
83
1.1.5
The exponential function
8
13.7
The
0.1
quantile of a
1.1.6
The natural logarithmic
continuous random variable
84
function
9
13.8
Some binomial density
1.1.7
Definition of the first
functions
86
derivative
10
13.9
A binomial tree for a stock
1.1.8
Two functions
12
price evolution
87
1.1.9
1.1.10
The definite integral
The fe-period log return is
15
13.10
13.11
The standard uniform
distribution
Two normal densities
89
90
the sum of
h
consecutive
one-period log returns
24
13.12
Lognormal
density
associated with the standard
1.1.11
Graph of the function in
normal distribution
93
Example
1.1.8
27
13.13
A variance mixture of two
1.2.1
A matrix is a linear
normal densities
95
transformation
48
13.14
A skewed, leptokurtic
1.2.2
A vector that is not an
normal mixture density
97
eigenvector
49
13.15
Comparison of Student
t
1.23
An eigenvector
50
densities and standard
1.2.4
Six European equity indices
67
normal
98
1.2.5
The first principal
13.1«
Comparison of Student
f
component
69
density and normal with
13.1
Venn diagram
75
same variance
99
1.3.2
Density and distribution
13.17
Comparison of standardized
functions: (a) discrete
empirical density with
random variable; (b)
standardized Student
f
continuous variable
77
density and standard
133
Building a histogram in
normal density
99
Excel
78
13.18
The Excel
t
distribution
13.4
The effect of cell width on
function
100
the histogram shape
78
13.19
Filtering data to derive the
13.5
Two densities with
GEV distribution
102
the same expectation
L3JU
A Fréchet
density
103
List of Figures
13.21
Filtering data in the
1.5.4
Convergence of
peaks-over-threshold
Newton-Raphson
model
104
scheme
190
Ł3J2
Kernel estimates of S&P
1.5.5
Solver options
191
500
returns
107
1.5.6
Extrapolation of a yield
curve
193
13.23
Scatter plots from a
1.5.7
Linear interpolation on
paired sample of returns:
percentiles
195
(a) correlation
+0.75;
1.5.8
Fitting a currency smile
197
(b) correlation
0;
1.5.9
A cubic spline interpolated
(c) correlation
—0.75
113
yield curve
200
13.24
Critical regions for
1.5.10
FTSE
100
and S&P
500
13.25
hypothesis tests
The dependence of the
125
1.5.11
index prices,
1996-2007
US
Dollar-Sterling
exchange rate,
1996-2007
204
204
likelihood on parameters
130
1.5.12
Slope of chord about a
13.26
The likelihood and the log
point
206
likelihood functions
131
Ł5.13
Discretization of space for
13.27
FTSE
100
index
133
the finite difference scheme
209
13.28
Daily prices and log prices
1.5.14
A simple finite difference
of DJIA index
137
scheme
210
13.29
Daily log returns on DJIA
L5.15
A binomial lattice
210
. .
пя
Ł5.16
Computing the price of
1П0ЄХ
European and American
1.4.1
Scatter plot of Amex and
puts
216
S&P
500
daily log returns
145
Ł5.17
Simulating from a standard
1.4.2
Dialog box for Excel
normal distribution
218
regression
153
1.5.18
Possible paths for an asset
1.43
Unbiasedness and
price following geometric
efficiency
156
Brownian motion
220
1.4.4
Distribution of a consistent
13.19
A set of three independent
standard normal
estimator
157
simulations
221
IA5
Billiton share price, Amex
13.20
A set of three correlated
Oil index and
СВОЕ
Gold
normal simulations
222
index
162
1.6.1
Convex, concave and linear
1.4.6
Dialog box for multiple
utility functions
229
regression in Excel
164
1.6.2
The effect of correlation on
1.4.7
The iTraxx Europe index
portfolio volatility
239
and its determinants
172
1.63
Portfolio volatility as a
1.4.8
Residuals from the Billiton
function of portfolio weight
241
1.6.4
Portfolio risk and return as
regression
178
a function of portfolio
IJ.l
Method of bisection
187
weight
242
Ł5J
Setting Excei s Goal Seek
189
L6J
Minimum variance
из
Newton-Raphson iteration
189
portfolio
243
1.6.6
Solver settings
for
Example
1.6.9 246
1.6.7
The opportunity set and the
efficient frontier
247
1.6.8
Indifference curves of risk
averse investor
248
List of Figures
XV
1.6.9
Indifference curves of risk
loving investor
249
1.6.10
Market portfolio
251
1.6.11
Capital market line
251
1.6.12
Security market line
253
List of Tables
1.1.1
Asset prices
18
LŁ2
Portfolio weights and
portfolio value
18
1.13
Portfolio returns
26
1.2.1
Volatilities aad correlations
56
LZ2
The correlation matrix of
weekly returns
68
1.23
Eigenvectors and
eigenvalues of the
correlation matrix
68
Ł3.1
Example of the density of a
discrete random variable
75
13.2
Distribution function for
Table
І.З.І
75
133
Biased and unbiased sample
moments
82
13,4
The B(3,
1/6)
distribution
86
135
A Poisson
density function
88
13.6
A simple
Divariate
density
110
13.7
Distribution of the product
110
13.8
Calculating a covariance
111
13.9
Sample statistics
127
L4.1
Calculation of OLS
estimates
147
1.4.2
Estimating the residual sum
of sqaures and the standard
error of the regression
149
1.43
Estimating the total sum of
squares
150
1.4.4
Critical values of f3
152
I.4J
Some of the Excel output for
the Amex and S&P
500
model
1*54.
1.4.6
ANOVA for the Amex and
S&P
500
model
154
1.4.7
Coefficient estimates for the
Amex and S&P
500
model
154
1.4.8
ANOVA for BiUiton
regression
164
Ï.4.9
Wald,
LM
and
LR
statistics
167
Ł5,l
Mean and volatility of the
FTSE
100
and S&P
500
indices and the
£/$
FX rate
205
1SJ. Estimated parameters of
normal mixture distributions
205
Ł53
Analytic vs finite difference
Greeks
208
Ł5.4
Characteristics of asset
returns
221
1.6.1
Two investments (outcomes
as returns)
227
1.6.2
Two investments (utility of
outcomes)
228
1.63
Returns characteristics for
two portfolios
237
1.6.4
Two investments
258
1.6.5
Sharpe
ratio and weak
stochastic dominance
259
1.6.6
Returns on an actively
managed fund and its
benchmark
261
1.6.7
Statistics on excess returns
262
1.6.8
Sharpe
ratios and adjusted
Sharpe
ratios
262
1.6.9
Kappa indices
264
List of Examples
1.1.1
Roots of a quadratic
1.2.12
equation
5
1.1.2
Calculating derivatives
12
1.2.13
1.13
Identifying stationary points
14
1.1.4
A definite integral
16
Ł2.14
I.1J
Portfolio weights
18
1.1.6
Returns on a long-short
portfolio
20
1.2.15
1.1.7
Portfolio returns
25
1.1.8
Stationary points of a
1.2.16
function of two variables
28
1.1.9
Constrained optimization
30
1.2.17
1.1.10
Total derivative of a
function of three variables
31
1.2.18
1.1.11
Taylor approximation
32
1.2.1
Finding a matrix product
1.2.19
using Excel
40
1.2.2
Calculating a
4
χ
4
13.1
determinant
42
142
Ш
Finding the determinant
and the inverse matrix
using Excel
43
133
1.2.4
Solving a system of
Ili
simultaneous linear
Ж·*?** »
TIC
equations in Excel
45
Ili
1.2.5
A quadratic form in Excel
45
Ł2.6
Positive definiteness
46
Ł2.7
Determinant test for
Ł3.7
positive definiteness
47
Ł2.8
Finding eigenvalues and
Ł3.8
eigenvectors
51
1.2.9
Finding eigenvectors
53
L2.10
Using an Excel add-in to
13.9
find eigenvectors and
eigenvalues
54
иле
UL11
Covariance and correlation
13.11
matrices
56
13.12
Volatility of returns and
volatility of P&L
57
A non-positive definite
3x3
matrix
59
Eigenvectors and
eigenvalues of a
2
χ
2
covariance matrix
60
Spectral decomposition of a
correlation matrix
61
The Cholesky matrix of a
2x2
matrix
62
The Cholesky matrix of a
3x3
matrix
63
Finding the Cholesky
matrix in Excel
63
Finding the
LU
decomposition in Excel
64
Building a histogram
77
Calculating moments of a
distribution
81
Calculating moments of a
sample
82
Evolution of an asset price
87
Normal probabilities
90
Normal probabilities for
portfolio returns
91
Normal probabilities for
active returns
92
Variance and kurtosis of a
zero-expectation normal
mixture
95
Probabilities of normal
mixture variables
96
Calculating a covariance
110
Calculating a correlation
112
Normal confidence intervals
119
xviii
List of Examples
13.13
One- and two-sided
1.5.5
Fitting a 25-delta currency
confidence intervals
120
option smile
196
13.14
Confidence interval for a
L5.6
Interpolation with cubic
population mean
123
splines
198
L3.15
Testing for equality of
1.5.7
Finite difference
means and variances
127
approximation to delta,
13.16
Log likelihood of the
gamma and
vega
208
normal density
131
1.5.8
Pricing European call and
13.17
Fitting a Student
t
put options
212
distribution by maximum
1.5.9
Pricing an American option
likelihood
132
with a binomial lattice
215
1.4.1
Using the OLS formula
147
1.5.10
Simulations from correlated
1.4.2
Relationship between beta
Student
t
distributed
and correlation
147
variables
222
1.43
Estimating the OLS
1.6.1
Expected utility
227
standard error of the
Ł6.2
Certain equivalents
228
1.4.4
regression
ANOVA
148
150
L63
Portfolio allocations for an
exponential investor
235
1.4.5
Hypothesis tests in a simple
1.6.4
Higher moment criterion
linear model
151
for an exponential investor
236
1.4.6
Simple regression in matrix
form
160
Ł65
Minimum variance
portfolio: two assets
241
1.4.7
Goodness-of-fit test in
L6.6
Minimum variance
multiple regression
164
portfolio on S&P
100
and
FTSE
100
242
1.4.8
Testing a simple hypothesis
1.6.7
JŁ *Ł
Έι
J
*
uf
Ł
Vir^r
General formula for
1.4.9
1.4.10
in multiple regression
Testing a linear restriction
Confidence interval for
165
165
1.6.8
1.6.9
minimum variance portfolio
The
Markowitz
problem
Minimum variance
244
245
1.4.11
regression coefficient
Prediction in multivariate
168
portfolio with many
constraints
246
regression
169
1.6.10
The CML equation
252
1.4.12
Durbin—
Watson test
177
1.6.11
Stochastic dominance and
1.4.13
White s heteroscedasticity
the
Sharpe
ratio
258
1.5.1
test
Excel s Goal Seek
177
188
1.6.12
Adjusting
a Sharpe
ratio for
autocorrelation
260
L5.2
Using Solver to find a bond
1.6.13
Adjusted
Sharpe
ratio
261
yield
191
1.6.14
Computing a generalized
из
Interpolating implied
Sharpe
ratio
263
volatility
194
1.6.15
Omega, Sortine
and kappa
IJ.4
Bilinear interpolation
194
indices
264
Written by leading market risk academic, Professor Carol Alexander, QUANTITATIVE METHODS IN
FINANCE forms part one of the MARKET
RISKANALYSIS
four volume set. Starting from the basics, this
book helps readers to take the first step towards becoming a properly qualified financial risk manager
and asset manager, roles that are currently in huge demand. Accessible to intelligent readers with a
firm grasp of mathematics at high school level, or to anyone with a university degree in mathematics,
physics or engineering, no prior knowledge of finance is necessary. Instead the emphasis is on
understanding ideas rather than on mathematical rigour, meaning that this book offers a fast-track
introduction to financial risk analysis for readers with some quantitative background, highlighting
those areas of mathematics that are particularly relevant to solving problems in financial risk
management and asset management. Unique to this book is a focus on both continuous and discrete
time finance so that QUANTITATIVE METHODS IN FINANCE is not only about the application of
mathematics to finance; it also explains, in very pedagogical terms, how the continuous
cime
and
discrete time finance disciplines meet, providing a comprehensive, highly accessible guide which will
equip readers with the tools to start applying their knowledge immediately.
All together, the MARKET
RISK ANALYSIS
four volume set illustrates virtually every concept or formula
with a practical, numerical example or a longer, empirical case study. Across all four volumes there are
approximately
300
numerical and empirical examples,
400
graphs and figures and
30
case studies
many of which are contained in interactive Excel spreadsheets available from the accompanying
CD-ROM. Empirical examples and case studies specific to this volume include:
|
| any_adam_object | 1 |
| author | Alexander, Carol |
| author_facet | Alexander, Carol |
| author_role | aut |
| author_sort | Alexander, Carol |
| author_variant | c a ca |
| building | Verbundindex |
| bvnumber | BV036695514 |
| ctrlnum | (OCoLC)820516007 (DE-599)BVBBV036695514 |
| edition | Reprinted with corr. |
| format | Book |
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| id | DE-604.BV036695514 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:45:59Z |
| institution | BVB |
| isbn | 9780470998007 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020614056 |
| oclc_num | 820516007 |
| open_access_boolean | |
| owner | DE-739 DE-29T DE-19 DE-BY-UBM |
| owner_facet | DE-739 DE-29T DE-19 DE-BY-UBM |
| physical | XXVII, 290 S. graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Wiley |
| record_format | marc |
| spelling | Alexander, Carol Verfasser aut Market risk analysis 1 Quantitative methods in finance Carol Alexander Reprinted with corr. Chichester [u.a.] Wiley 2010 XXVII, 290 S. graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Hier auch später ersch., unveränd. Nachdr. (DE-604)BV023295503 1 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020614056&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020614056&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Alexander, Carol Market risk analysis |
| title | Market risk analysis |
| title_auth | Market risk analysis |
| title_exact_search | Market risk analysis |
| title_full | Market risk analysis 1 Quantitative methods in finance Carol Alexander |
| title_fullStr | Market risk analysis 1 Quantitative methods in finance Carol Alexander |
| title_full_unstemmed | Market risk analysis 1 Quantitative methods in finance Carol Alexander |
| title_short | Market risk analysis |
| title_sort | market risk analysis quantitative methods in finance |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020614056&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020614056&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023295503 |
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