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Statistics for finance / Erik Lindstrom, Henrik Madsen and Jan Nygaard Nielsen.

By: Contributor(s): Material type: TextTextSeries: Texts in statistical sciencePublication details: Boca Raton : CRC Press, ©2015.Description: xvii, 365 p. : illustrations : 23 cmISBN:
  • 9781482228991
Subject(s): DDC classification:
  • 000SB:332 23 L753
Contents:
1. Introduction -- 2. Fundamentals -- 3. Discrete time finance -- 4. Linear time series models -- 5. Nonlinear time series models -- 6. Kernel estimators in time series analysis -- 7. Stochastic calculus -- 8. Stochastic differential equations -- 9. Continuous-time security markets -- 10. Stochastic interest rate models -- 11. Term structure of interest rates -- 12. Discrete time approximations -- 13. Parameter estimation in discretely observed SDEs -- 14. Inference in partially observed processes.
Summary: The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, It s formula, the Black Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students financial reasoning skills.
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Includes bibliographical references and index.

1. Introduction --
2. Fundamentals --
3. Discrete time finance --
4. Linear time series models --
5. Nonlinear time series models --
6. Kernel estimators in time series analysis --
7. Stochastic calculus --
8. Stochastic differential equations --
9. Continuous-time security markets --
10. Stochastic interest rate models --
11. Term structure of interest rates --
12. Discrete time approximations --
13. Parameter estimation in discretely observed SDEs --
14. Inference in partially observed processes.

The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, It s formula, the Black Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students financial reasoning skills.

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