## A Benchmark Approach to Quantitative Finance (Google eBook)SPRINGER BUSINESS SALE 50% OFF The benchmark approach provides a general framework for financial market modeling, which extends beyond the standard risk-neutral pricing theory. It permits a unified treatment of portfolio optimization, derivative pricing, integrated risk management and insurance risk modeling. The existence of an equivalent risk-neutral pricing measure is not required. Instead, it leads to pricing formulae with respect to the real-world probability measure. This yields important modeling freedom which turns out to be necessary for the derivation of realistic, parsimonious market models. The first part of the book describes the necessary tools from probability theory, statistics, stochastic calculus and the theory of stochastic differential equations with jumps. The second part is devoted to financial modeling by the benchmark approach. Various quantitative methods for the real-world pricing and hedging of derivatives are explained. The general framework is used to provide an understanding of the nature of stochastic volatility. The book is intended for a wide audience that includes quantitative analysts, postgraduate students and practitioners in finance, economics and insurance. It aims to be a self-contained, accessible but mathematically rigorous introduction to quantitative finance for readers that have a reasonable mathematical or quantitative background. Finally, the book should stimulate interest in the benchmark approach by describing some of its power and wide applicability. |

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worst book I've ever seen, misleading, easily getting distracted with too many useless notations, this is not a student book for sure.

### Contents

1 | |

11 | |

13 Moments of Random Variables | 22 |

14 Joint Distributions and Random Vectors | 39 |

15 Copulas | 50 |

16 Exercises for Chapter 1 | 53 |

Statistical Methods | 55 |

22 Confidence Intervals | 63 |

85 European Put Option | 295 |

86 Hedge Simulation | 298 |

87 Squared Bessel Processes | 304 |

88 Exercises for Chapter 8 | 317 |

Various Approaches to Asset Pricing | 319 |

92 Actuarial Pricing | 329 |

93 Capital Asset Pricing Model | 332 |

94 Risk Neutral Pricing | 337 |

23 Estimation Methods | 70 |

24 Maximum Likelihood Estimation | 78 |

25 Normal Variance Mixture Models | 81 |

26 Distribution of Index LogReturns Estimation of LogReturns | 84 |

27 Convergence of Random Sequences | 92 |

28 Exercises for Chapter 2 | 98 |

Modeling via Stochastic Processes | 99 |

32 Certain Classes of Stochastic Processes | 106 |

33 Discrete Time Markov Chains | 110 |

34 Continuous Time Markov Chains | 113 |

35 Poisson Processes | 120 |

36 Levy Processes | 126 |

37 Insurance Risk Modeling | 128 |

38 Exercises for Chapter 3 | 131 |

Diffusion Processes | 133 |

42 Examples for Continuous Markov Processes | 136 |

43 Diffusion Processes | 141 |

44 Kolmogorov Equations | 145 |

45 Diffusions with Stationary Densities | 154 |

46 MultiDimensional Diffusion Processes | 159 |

47 Exercises for Chapter 4 | 161 |

Martingales and Stochastic Integrals | 163 |

52 Quadratic Variation and Covariation | 174 |

53 Gains from Trade as Stochastic Integral | 187 |

54 Ito Integral for Wiener Processes | 193 |

55 Stochastic Integrals for Semimartingales | 197 |

56 Exercises for Chapter 5 | 203 |

The Ito Formula | 204 |

62 Multivariate Ito Formula | 209 |

63 Some Applications of the ItoFormula | 213 |

64 Extensions of the Ito Formula | 222 |

65 Levys Theorem | 227 |

66 A Proof of the Ito Formula | 230 |

67 Exercises for Chapter 6 | 234 |

Stochastic Differential Equations | 237 |

72 Linear SDE with Additive Noise | 241 |

73 Linear SDE with Multiplicative Noise | 243 |

74 Vector Stochastic Differential Equations | 246 |

75 Constructing Explicit Solutions of SDEs | 248 |

76 Jump Diffusions | 254 |

77 Existence and Uniqueness | 261 |

78 Markovian Solutions of SDEs | 272 |

79 Exercises for Chapter 7 | 275 |

Introduction to Option Pricing | 276 |

82 Options under the BlackScholes Model | 281 |

83 The BlackScholes Formula | 288 |

84 Sensitivities for European Call Option | 290 |

95 Girsanov Transformation and Bayes Rule | 345 |

96 Change of Numeraire | 350 |

97 FeynmanKac Formula | 356 |

98 Exercises for Chapter 9 | 364 |

Continuous Financial Markets | 367 |

102 Growth Optimal Portfolio | 372 |

103 Supermartingale Property | 375 |

104 Real World Pricing | 378 |

105 GOP as Best Performing Portfolio | 386 |

106 Diversified Portfolios in CFMs | 389 |

107 Exercises for Chapter 10 | 402 |

Portfolio Optimization | 403 |

111 Locally Optimal Portfolios | 404 |

112 Market Portfolio and GOP | 415 |

113 Expected Utility Maximization | 419 |

114 Pricing Nonreplicable Payoffs | 427 |

115 Hedging | 430 |

116 Exercises for Chapter 11 | 437 |

Modeling Stochastic Volatility | 438 |

122 Modified CEV Model | 444 |

123 Local Volatility Models | 461 |

124 Stochastic Volatility Models | 472 |

125 Exercises for Chapter 12 | 481 |

Minimal Market Model | 483 |

132 Stylized Minimal Market Model | 488 |

133 Derivatives under the MMM | 496 |

134 MMM with Random Scaling | 502 |

135 Exercises for Chapter 13 | 511 |

Markets with Event Risk | 512 |

142 Diversified Portfolios | 523 |

143 MeanVariance Portfolio Optimization | 532 |

144 Real World Pricing for Two Market Models | 536 |

145 Exercises for Chapter 14 | 549 |

Numerical Methods | 551 |

152 Scenario Simulation | 558 |

153 Classical Monte Carlo Method | 570 |

154 Monte Carlo Simulation for SDEs | 578 |

155 Variance Reduction of Functionals of SDEs | 587 |

156 Tree Methods | 591 |

157 Finite Difference Methods | 600 |

158 Exercises for Chapter 15 | 611 |

Solutions for Exercises | 614 |

667 | |

Author Index | 682 |

689 | |