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Statistical Methods for Financial Engineering

Bruno Remillard
€61.50
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A01=Bruno Remillard
advanced econometrics techniques
Archimedean Copulas
asset price modeling
Author_Bruno Remillard
black
Brownian Motion
call
Category=KCH
Category=KF
Category=KFFM
Category=PBT
Category=PBW
Category=PBWL
Category=UXT
Change Point Test
Cir Model
Copula Family
credit risk modeling
Delta Hedging
dependence models in hedge fund replication
Distribution Function
dynamic hedging in discrete time
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
equivalent
Equivalent Martingale Measure
Esscher Transform
estimation of risk and performance measures
Feller Process
financial applications of filtering
financial applications of Levy processes
financial time series analysis
function
GARCH models
Hazard Rate Order
Hedging Errors
Independence Copula
Kalman Equations
limits of the Black-Scholes model
martingale
matlab
MATLAB financial engineering applications
MATLAB Function
measure
model
Non-central Chi Square Distribution
Normal Inverse Gaussian Process
Optimal Hedging
option
option pricing
Ornstein Uhlenbeck Process
Out-of Sample RMSE
portfolio optimization methods
quantitative risk assessment
Regime Switching Model
Saddlepoint Approximation
scholes
spot interest rate modeling
statistical aspects of stochastic models
Statistical Methods for Financial Engineering
stochastic differential equations
stochastic models in financial engineering
Student Copula
validation of stochastic models
Variance Gamma Process
Vasicek Model

Product details

  • ISBN 9781032477497
  • Weight: 740g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Jan 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases. Statistical Methods for Financial Engineering guides current and future practitioners on implementing the most useful stochastic models used in financial engineering.

After introducing properties of univariate and multivariate models for asset dynamics as well as estimation techniques, the book discusses limits of the Black-Scholes model, statistical tests to verify some of its assumptions, and the challenges of dynamic hedging in discrete time. It then covers the estimation of risk and performance measures, the foundations of spot interest rate modeling, Lévy processes and their financial applications, the properties and parameter estimation of GARCH models, and the importance of dependence models in hedge fund replication and other applications. It concludes with the topic of filtering and its financial applications.

This self-contained book offers a basic presentation of stochastic models and addresses issues related to their implementation in the financial industry. Each chapter introduces powerful and practical statistical tools necessary to implement the models. The author not only shows how to estimate parameters efficiently, but he also demonstrates, whenever possible, how to test the validity of the proposed models. Throughout the text, examples using MATLAB® illustrate the application of the techniques to solve real-world financial problems. MATLAB and R programs are available on the author’s website.

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