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Hedge Fund Modelling and Analysis: An Object Oriented Approach Using Cplusplus [Kõva köide]

(EDHEC Business School),
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 252x175x20 mm, kaal: 635 g
  • Sari: The Wiley Finance Series
  • Ilmumisaeg: 18-Nov-2016
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118879570
  • ISBN-13: 9781118879573
Teised raamatud teemal:
Hedge Fund Modelling and Analysis: An Object Oriented Approach Using CplusplusSuurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 252x175x20 mm, kaal: 635 g
  • Sari: The Wiley Finance Series
  • Ilmumisaeg: 18-Nov-2016
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118879570
  • ISBN-13: 9781118879573
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118879573.html
Märksõnad:
Use powerful C++ algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Low interest rates, overcrowded markets and greater regulatory oversight are just some of the many reasons it is close to impossible for hedge funds to draw competitive returns. The solution for many hedge fund managers, quantitative investment analysts and risk managers is to adopt new technologies, platforms and programming languages to better manage their risks and maximise the benefits of their return profiles.

Hedge Fund Modelling and Analysis is a full course in the latest analytic strategies for hedge fund investing, complete with a one-of-a-kind primer on both C++ and object oriented programming (OOP). Covering both basic and risk-adjusted performance measures, this practitioner's guide enables you to manage risk easily and make the most of key statistics with simple and advanced analysis techniques. This highly anticipated third book in the widely used Hedge Fund Modelling and Analysis series is the only guide available for applying the powerful C++ language to revolutionise hedge fund trading. Even if you've never worked with code before, the focused overview of C++ gives you everything you need to navigate the technical aspects of object oriented programming, which enables you to build sophisticated analysis programs from small units of reusable code. This book is your breakthrough introduction to winning with hedge funds in the new reality of trading.

Jumpstart your new approach to beating the markets with:





All the guidance and hands-on support you need to use quantitative strategies to optimise hedge fund decision-making. Illustrative modelling exercises and worked-out problems demonstrating what to expect when assessing risk and return factors in the real world. A companion website offering additional C++ programs, algorithms and data to download.

Make reading Hedge Fund Modelling and Analysis your new routine and gain all the insight and relevant information you need to beat the markets.
Preface xi
Chapter 1 Essential C++
1(70)
1.1 A Brief History of C and C++
1(1)
1.2 A Basic C++ Program
2(2)
1.3 Variables
4(8)
1.3.1 Characters and Strings
5(3)
1.3.2 Variable Declarations
8(1)
1.3.3 Type Casting
9(1)
1.3.4 Variable Scope
10(1)
1.3.5 Constants
11(1)
1.4 Operators
12(6)
1.4.1 The Assignment Operator
12(2)
1.4.2 Arithmetic Operators
14(1)
1.4.3 Relational Operators
15(1)
1.4.4 Logical Operators
16(1)
1.4.5 Conditional Operator
17(1)
1.5 Input and Output
18(3)
1.6 Control Structures
21(9)
1.6.1 Branching
21(4)
1.6.2 Looping
25(1)
1.6.3 The for Loop
25(2)
1.6.4 The while Loop
27(2)
1.6.5 The do ... while Loop
29(1)
1.7 Arrays
30(1)
1.8 Vectors
31(2)
1.9 Functions
33(8)
1.9.1 Call-by-Value vs. Call-by-Reference
36(3)
1.9.2 Overloading Functions
39(2)
1.10 Object Oriented Programming
41(30)
1.10.1 Classes and Abstract Data Types
42(1)
1.10.2 Encapsulation and Interfaces
43(1)
1.10.3 Inheritance and Overriding Functions
44(1)
1.10.4 Polymorphism
45(1)
1.10.5 An Example of a Class
46(3)
1.10.6 Getter and Setter Methods
49(3)
1.10.7 Constructors and Destructors
52(3)
1.10.8 A More Detailed Class Example
55(6)
1.10.9 Implementing Inheritance
61(3)
1.10.10 Operator Overloading
64(7)
Chapter 2 The Hedge Fund Industry
71(20)
2.1 What are Hedge Funds?
71(3)
2.2 The Structure of a Hedge Fund
74(3)
2.2.1 Fund Administrators
74(1)
2.2.2 Prime Brokers
75(1)
2.2.3 Custodian, Auditors and Legal
76(1)
2.3 The Global Hedge Fund Industry
77(5)
2.3.1 North America
79(1)
2.3.2 Europe
80(1)
2.3.3 Asia
81(1)
2.4 Specialist Investment Techniques
82(3)
2.4.1 Short Selling
82(1)
2.4.2 Leverage
83(1)
2.4.3 Liquidity
84(1)
2.5 Recent Developments for Hedge Funds
85(6)
2.5.1 UCITS Hedge Funds
85(3)
2.5.2 The European Passport
88(1)
2.5.3 Restrictions on Short Selling
88(3)
Chapter 3 Hedge Fund Data Sources
91(28)
3.1 Hedge Fund Databases
91(1)
3.2 Major Hedge Fund Indices
92(21)
3.2.1 Non-Investable and Investable Indices
92(2)
3.2.2 Dow Jones Credit Suisse Hedge Fund Indices (www.hedgeindex.com)
94(6)
3.2.3 Hedge Fund Research (www.hedgefundresearch.com)
100(2)
3.2.4 FTSE Hedge (www.ftse.com)
102(2)
3.2.5 Greenwich Alternative Investments (www.greenwichai.com)
104(4)
3.2.6 Morningstar Alternative Investment Center (www.morningstar.com/advisor/alternative-investments.htm)
108(4)
3.2.7 EDHEC Risk and Asset Management Research Centre (www.edhec-risk.com)
112(1)
3.3 Database and Index Biases
113(2)
3.3.1 Survivorship Bias
113(2)
3.3.2 Instant History Bias
115(1)
3.4 Benchmarking
115(4)
3.4.1 Tracking Error
116(3)
Chapter 4 Statistical Analysis
119(54)
4.1 The Stats Class
119(1)
4.2 The Utils Class
120(3)
4.3 The Import Class
123(4)
4.4 Basic Performance Plots
127(4)
4.4.1 Value Added Index
127(3)
4.4.2 Histograms
130(1)
4.5 Probability Distributions
131(2)
4.5.1 Populations and Samples
132(1)
4.6 Probability Density Function
133(1)
4.7 Cumulative Distribution Function
134(1)
4.8 The Normal Distribution
134(2)
4.8.1 Standard Normal Distribution
136(1)
4.9 Visual Tests for Normality
136(2)
4.9.1 Inspection
136(1)
4.9.2 Normal Probability Plot
137(1)
4.10 Moments of a Distribution
138(8)
4.10.1 Mean and Standard Deviation
138(3)
4.10.2 Skew
141(1)
4.10.3 Kurtosis
142(4)
4.11 Covariance and Correlation
146(12)
4.12 Linear Regression
158(15)
4.12.1 Coefficient of Determination
163(4)
4.12.2 Residual Plots
167(6)
Chapter 5 Performance Measurement
173(40)
5.1 The PMetrics Class
173(1)
5.2 The Intuition Behind Risk-Adjusted Returns
174(10)
5.2.1 Risk-Adjusted Returns
182(2)
5.3 Absolute Risk-Adjusted Return Metrics
184(3)
5.4 The Sharpe Ratio
187(4)
5.5 Market Models
191(16)
5.5.1 The Information Ratio
192(5)
5.5.2 The Treynor Ratio
197(6)
5.5.3 Jensen's Alpha
203(2)
5.5.4 M-Squared
205(2)
5.6 The Minimum Acceptable Return
207(6)
5.6.1 The Sortino Ratio
207(4)
5.6.2 The Omega Ratio
211(2)
Chapter 6 Mean-Variance Optimisation
213(34)
6.1 The Optimise Class
213(1)
6.2 Mean-Variance Analysis
214(33)
6.2.1 Portfolio Return and Variance
214(15)
6.2.2 The Mean-Variance Optimisation Problem
229(15)
6.2.3 The Global Minimum Variance Portfolio
244(2)
6.2.4 Short Sale Constraints
246(1)
Chapter 7 Market Risk Management
247(30)
7.1 The RMetrics Class
247(1)
7.2 Value-at-Risk
248(3)
7.3 Traditional VaR Methods
251(12)
7.3.1 Historical Simulation
251(3)
7.3.2 Parametric Method
254(7)
7.3.3 Monte-Carlo Simulation
261(2)
7.4 Modified VaR
263(3)
7.5 Expected Shortfall
266(5)
7.6 Extreme Value Theory
271(6)
7.6.1 Block Maxima
272(1)
7.6.2 Peaks Over Threshold
272(5)
References 277(2)
Index 279
PAUL DARBYSHIRE gained his PhD in Theoretical Physics from Kings College London and then began his career working as a Quantitative Analyst and Trader at HSBC on the Exotic Derivatives and Structured Products desk. He has subsequently been involved in the development and implementation of a variety of trading and risk management platforms for a number of major investent banks around the globe. Since 2005, Paul has been responsible for the analysis and design of cutting-edge algorithms in the development of behavioural finance and decision-making models at the University of Oxford. Paul also provides many private equity firms, hedge funds and investment management companies with senior consultancy in areas such as dynamic portfolio optimisation, trading platform design, software engineering and risk management.

DAVID HAMPTON gained his PhD in Electrical Engineering from the Queens University of Belfast and an international MBA from Institut Superieur de Gestion in Paris, New York and Tokyo before joining Bank of America Capital Markets in London. David was previously an Adjunct Finance Professor at Skema Business School in Sophia Antipolis where he taught Financial Engineering and Excel/VBA Programming at the MSc level. At EDHEC Business School in Nice, he was responsible for managing their range of five MSc courses as Assistant Dean of the Financial Economics Track. An NFA registered CTA since 1996, David has been active as a consultant to the hedge fund community and as a Hedge Fund Manager with particular expertise in Global Macro Managed Futures and Long Short Equity investment styles.

This is the third book in the authorial teams popular Hedge Fund Modelling and Analysis series, which includes Hedge Fund Modelling and Analysis using MATLAB and Hedge Fund Modelling and Analysis Using Excel and VBA.