Advanced Quantitative Finance with C++ by Alonso Peña

By Alonso Peña

Create and enforce mathematical types in C++ utilizing Quantitative Finance

About This Book

  • Describes the main mathematical types used for expense fairness, forex, rates of interest, and credits derivatives
  • The complicated versions are defined step by step in addition to a circulation chart of each implementation
  • Illustrates each one asset classification with absolutely solved C++ examples, either simple and complex, that help and supplement the text

Who This booklet Is For

If you're a quantitative analyst, possibility supervisor, actuary, or a qualified operating within the box of quantitative finance and need a brief hands-on creation to the pricing of economic derivatives, this e-book is perfect for you. try to be accustomed to the elemental programming ideas and C++ programming language. you need to even be conversant in calculus of undergraduate level.

What you'll Learn

  • Solve advanced pricing difficulties in monetary derivatives utilizing a based technique with the Bento field template
  • Explore a few key numerical tools together with binomial bushes, finite alterations, and Monte Carlo simulation
  • Develop your knowing of fairness, currency, rate of interest, and credits derivatives via concrete examples
  • Implement easy and intricate by-product tools in C++
  • Discover crucial mathematical versions utilized in quantitative finance this day to cost by-product instruments
  • Effectively include item orientated programming (OOP) rules into the code

In Detail

This booklet will introduce you to the main mathematical versions used to cost monetary derivatives, in addition to the implementation of major numerical versions used to resolve them. particularly, fairness, forex, rates of interest, and credits derivatives are mentioned. within the first a part of the ebook, the most mathematical types utilized in the realm of economic derivatives are mentioned. subsequent, the numerical tools used to resolve the mathematical types are provided. ultimately, either the mathematical types and the numerical equipment are used to unravel a few concrete difficulties in fairness, currency, rate of interest, and credits derivatives.

The types used contain the Black-Scholes and Garman-Kohlhagen versions, the LIBOR marketplace version, structural and depth credits types. The numerical equipment defined are Monte Carlo simulation (for unmarried and a number of assets), Binomial bushes, and Finite distinction equipment. you can find implementation of concrete difficulties together with ecu name, fairness Basket, forex eu name, FX Barrier choice, rate of interest switch, financial disaster, and credits Default switch in C++.

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Extra resources for Advanced Quantitative Finance with C++

Example text

Following Merton, we further assume that the dynamics of the firm follow a GBM as follows: G9 U9GW  V 9G: 4 Equation 11 And by doing this, we establish a bridge between credit risk and the pricing of equity derivatives. In fact, we can now use all the results from pricing equity derivatives to price structural models of credit risk. Note that the volatility σ is the firm's assets volatility and not the equity volatility. 5σ 2 )T σ T and d 2 = d1 − σ T In equation 12, N() is the cumulative standard normal distribution.

27 ] Mathematical Models For example, imagine that we have a firm that has a total value of its assets of 100 million USD. The risk-free interest rate is 5 percent. The volatility of the firm's assets is assumed to be 20 percent. And the face value of its debt stands at 70 million USD payable as a zero coupon bond in four years. What should be the fair value of its equity at time t=0? We use the framework of structural models of credit risk and proceed as follows. The parameters are 9    , '    , 7 , V  , and U  .

We will now explain the BT method in the simplified context of a two-step BT. This can be easily generalized to an N step tree. [ 39 ] Numerical Methods To start with, we assume that the underlying can only go up or down in the next time step. So we specify the up factor u to describe how much the value today changes to a higher value and the down factor d to describe how much the value today changes to a lower value, such that the up value is S(T)= u S(0), and the down value is S(T)= d S(0). Furthermore, refer to "Option pricing: A simplified approach".

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