There isn't a standard way of forecasting inflation in the industry. In the risk-modeling world, the models have to be submitted to a regulatory body to receive the approval for using an advanced modeling approach for credit risk. This has somehow ensured that, across the world, analysts and modelers are using a similar suite of statistical techniques to manage risk. However, in the case of inflation, there isn't much exchange of ideas or a platform available from where we can choose the best modeling technique. Usually, the central bank publishes some inflation figures and these can be used as a benchmark for inflation-forecasting accuracy checks. Most of the models built by organizations are proprietary in nature. Moreover, these organizations are chiefly concerned about benchmarking a model to the central bank inflation...
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Table Of Contents
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.
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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