Posts Tagged Quantitative Finance

Quantitative Finance Reading List – Theoretical Foundations



Not everybody wants to become a theoretical physicist. Some consider the academic environment too relaxed, others are not keen on the politics or the necessity to continually hunt for funding early in their career. A job in Quantitative Finance offers an attractive alternative.

Financial engineering has both strong theoretical and applied components, is immensely intellectually stimulating and fast-paced. A significant degree of background knowledge and an exceptional academic record are required even to achieve an interview. If you have recently decided that academia is not where your career path lies and you possess strong technical skills then the reading list outlined below will get you started towards becoming a quant.

This is the first part in a multi-part series on textbooks suitable for becoming a quantitative analyst. The remaining parts will focus on implementation, further mathematical excursions, interview skills and numerical methods. This article will concentrate on the theory of financial engineering for those who have not had an exposure to finance before.

Mathematical Finance

A great place to start learning about the world of derivatives is with the classic text Options, Futures and Other Derivatives by John Hull. It is light on the mathematics, but covers a lot of ground. Specifically, it is a good introduction to derivative markets for those who haven’t had prior exposure to finance.

Once you’re comfortable with the concepts used in the financial markets the next step is to begin learning about arbitrage and the Black-Scholes model in a more mathematical manner. Dan Stefanica’s A Primer for the Mathematics of Financial Engineering will provide all of the calculus (differentiation, integration, taylor expansion etc) needed to tackle the Black-Scholes equation. It will also cover “the Greeks” and basic risk neutral pricing. This is a great book for somebody who doesn’t have the required undergraduate mathematical background needed for later texts.

At this stage you will be ready to tackle the intermediate works such as Mark Joshi’s Concepts and Practice of Mathematical Finance (an excellent book, highly recommended), Paul Wilmott on Quantitative Finance (extremely comprehensive and humourous explanations!), Baxter and Rennie’s Financial Calculus and Salih Neftci’s Introduction to the Mathematics of Financial Derivatives. A good working knowledge of the contents of these books is sufficient theory for any front office desk quant interviews.

If you wish to delve deeper into the mathematical theory underpinning derivatives pricing then Bernt Oksendal’s Stochastic Differential Equations is a great start, as it has plenty of SDE exercises to work through.

A rather heavy going text for desk work, but an essential book for researching financial engineering, is the two volume masterpiece by Steven Shreve – Stochastic Calculus for Finance (Vol I and Vol II). Vol I concentrates on the discrete pricing models while Vol II focuses on continuous models. Be warned that for the Vol II, a strong background in undergraduate mathematics is required – particularly in Real Analysis, Probability Theory and Measure Theory.

Summary and Suggested Reading Chronology
Options, Futures and Other Derivatives – John Hull A Primer for the Mathematics of Financial Engineering – Dan Stefanica The Concepts and Practice of Mathematical Finance – Mark Joshi Financial Calculus: An Introduction to Derivative Pricing – Martin Baxter, Andrew Rennie Stochastic Calculus for Finance II: Continuous-Time Models – Steven Shreve In the next article, texts on implementation will be presented which will give you the knowledge you need to begin creating your own quant models.

By: Michael Halls-Moore

About the Author:
QuantStart is a leading quant finance resource with quant jobs, quant articles, financial engineering tutorials and the latest quant events.

Visit www.QuantStart.com now and sign up for the free newsletter!



Kansieo.com

Tags: , , , , , , , , , , , , , , , , , , ,

Quantitative Finance Reading List – Numerical Methods



In the previous article the core C++ books required for a good grounding in quantitative programming were outlined. Now it is time to discuss the books useful for learning numerical methods, in particular Finite Difference Methods (FDM) and Monte Carlo Methods (MCM).

Finite Difference Methods

Finite Difference Methods are a class of numerical methods used to provide an approximate, discrete solution to various partial differential equations, in particular the Black-Scholes PDE. Finite Difference Methods work by discretising the derivative terms in the PDE, such that they can be implemented algorithmically. An explicit FDM has the quantities at the next time step calculated in terms of the values at the previous step. An implicit FDM has the quantities at the next time step calculated in terms of both the values of the next time step and the previous time step. Stability of the scheme is an important concept.

The following are some of the more well known (and recommended!) text books on Finite Difference Methods:

Finite Difference Methods in Financial Engineering: A Partial Differential Equation Approach – Duffy Financial Instrument Pricing Using C++ – Duffy Numerical Solution of Partial Differential Equations: Finite Difference Methods – Smith Pricing Financial Instruments: The Finite Difference Method – Tavella and Randall Option Pricing: Mathematical Models and Computation -Wilmott et al.

Monte Carlo Methods

Monte Carlo Methods rely on the concept of risk neutral valuation in order to price derivatives. In essence, many underlying random asset price paths are calculated and the associated derivative payoff is calculated for each path. The mean of the payoffs are taken and then the price is discounted to today’s price. This will give an approximation of the the option price. Further accuracy can be obtained by increasing the number of random trials.

Here are some of the top financial modelling MCM books:

C++ Design Patterns and Derivatives Pricing – Joshi Monte Carlo Methods in Financial Engineering – Glasserman Monte Carlo Frameworks: Building Customisable High-performance C++ Applications – Duffy et al. Monte Carlo Methods in Finance – Jaeckel Monte Carlo Methodologies and Applications for Pricing and Risk Management – Dupire

Suggested Reading

The best books to start with from a C++/numerical point of view are Duffy’s “Financial Instrument Pricing Using C++” and Joshi’s “C++ Design Patterns and Derivatives Pricing” books. In fact, Joshi’s can be read in conjunction with his “Concepts and Practice of Mathematical Finance”. They will get you up to speed on intermediate usage of C++ as well as give you an insight into both FDM and MCM. Depending on which way you lean (FDM or MCM), you may wish to continue with Wilmott’s “Option Pricing” or with Glasserman’s “Monte Carlo Methods in Financial Engineering” and Duffy’s “Monte Carlo Frameworks.

By: Michael Halls-Moore

About the Author:
QuantStart is a leading quant finance resource with many finite difference method tutorials as well as the latest quant jobs, quant events and quant articles.

Visit www.QuantStart.com now and sign up for the free newsletter!



Caffeinated Content – Members-Only Content for WordPress

Tags: , , , , , , , , , , , , , , , , , , ,