Financial institutions lend to various organizations. At times, financial institutions also end up borrowing. The lending creates a risk of default. In the event of a default, the borrower might not fully pay back the principal and the interest on the borrowed sum. This scenario is termed credit risk. There are various actions that can be taken to mitigate this risk. These actions collectively form the credit risk management discipline. The probability of the borrower defaulting needs to be taken into account when forming a credit risk management policy for a bank. Credit risk management is necessary in order to meet regulatory requirements and also to manage the lender's capital adequacy and monitor the quality of the loan book. Lenders also have an obligation to ensure that there are no systematic failures in their credit risk management policies...
SAS for Finance
By :
SAS for Finance
By:
Overview of this book
SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.
SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.
This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.
By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.
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Current Title:
SAS for Finance
Table of Contents (9 chapters)
Preface
Free Chapter
Time Series Modeling in the Financial Industry
Forecasting Stock Prices and Portfolio Decisions using Time Series
Credit Risk Management
Budget and Demand Forecasting
Inflation Forecasting for Financial Planning
Managing Customer Loyalty Using Time Series Data
Transforming Time Series – Market Basket and Clustering
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