更新时间：2021-06-25 21:56:26
封面
版权信息
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Disclaimer
Time Series Modeling in the Financial Industry
Time series illustration
The importance of time series
Forecasting across industries
Characteristics of time series data
Seasonality
Trend
Outliers and rare events
Disruptions
Challenges in data
Influencer variables
Definition changes
Granularity required
Legacy issues
System differences
Source constraints
Vendor changes
Archiving policy
Good versus bad forecasts
Use of time series in the financial industry
Predicting stock prices and making portfolio decisions
Adhering to Basel norms
Demand planning
Inflation forecasting
Managing customer journeys and maintaining loyalty
Summary
References
Forecasting Stock Prices and Portfolio Decisions using Time Series
Portfolio forecasting
A portfolio demands decisions
Forecasting process
Visualization of time series data
Business case study
Data collection and transformation
Model selection and fitting
Part A – Fit statistics
Part B - Diagnostic plots
Part C - Residual plots
Dealing with multicollinearity
Role of autocorrelation
Scoring based on PROC REG
ARIMA
Validation of models
Model implementation
Recap of key terms
Credit Risk Management
Risk types
Basel norms
Credit risk key metrics
Exposure at default
Probability of default
Loss given default
Expected loss
Aspects of credit risk management
Basel and regulatory authority guidelines
Governance
Validation
Data
PD model build
Genmod procedure
Proc logistic
Proc Genmod probit
Budget and Demand Forecasting
The need for the Markov model
Business problem
Markovian model approach
ARIMA model approach
Markov method for imputation
Inflation Forecasting for Financial Planning
What is inflation?
Reasons for inflation
Inflation outcome and the Philips curve
Winners and losers
Business case for forecasting inflation
Data-gathering exercise
Modeling methodology
Multivariate regression model
Forward selection model
