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Basic statistics for risk management in banks and financial institutions

Basic statistics for risk management in banks and financial institutions

  • ISBN: 9780192849014
  • Editorial: Oxford University Press
  • Lugar de la edición: Oxford. Reino Unido
  • Encuadernación: Cartoné
  • Medidas: 23 cm
  • Nº Pág.: 292
  • Idiomas: Inglés

Papel: Cartoné
106,94 € 96,25 €
Stock en librería. Envío en 24/48 horas

Resumen

The book provides an engaging account of theoretical, empirical, and practical aspects of various statistical methods in measuring risks of financial institutions, especially banks. In this book, the author demonstrates how banks can apply many simple but effective statistical techniques to analyze risks they face in business and safeguard themselves from potential vulnerability. It covers three primary areas of banking; risks-credit, market, and operational risk and
in a uniquely intuitive, step-by-step manner the author provides hands-on details on the primary statistical tools that can be applied for financial risk measurement and management.

The book lucidly introduces concepts of various well-known statistical methods such as correlations, regression, matrix approach, probability and distribution theorem, hypothesis testing, value at risk, and Monte Carlo simulation techniques and provides a hands-on estimation and interpretation of these tests in measuring risks of the financial institutions. The book strikes a fine balance between concepts and mathematics to tell a rich story of thoughtful use of statistical

Introduction to risk management: basics of statistics
Description of data and summary statistics for measurement of risk
Probability and distribution theorems and their applications in risk management
Hypotheses testing in baking risk analysis
Matrix algebra and their application in risk prediction and risk monitoring
Correlation theorem and portfolio management techniques
Multivariate analysis to understand functional relationship and scenario building
Monte Carlo simulation techniques and value at risk
Statistical tools for model validation and back testing
Time-series forecasting techniques for banking variables
Appendix: statistical tables

Resumen

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