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E-book: Basic Statistics for Risk Management in Banks and Financial Institutions

(Associate Professor (Finance), National Institute of Bank Management (NIBM), Pune)
  • Format: 320 pages
  • Pub. Date: 08-Mar-2022
  • Publisher: Oxford University Press
  • Language: eng
  • ISBN-13: 9780192665492
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Basic Statistics for Risk Management in Banks and Financial InstitutionsLarger Image
  • Format: 320 pages
  • Pub. Date: 08-Mar-2022
  • Publisher: Oxford University Press
  • Language: eng
  • ISBN-13: 9780192665492
Other books in subject:

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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 methods.
Book Summary xix
1 Introduction to Risk Management: Basics of Statistics
1(28)
What is Risk?
2(1)
Essence of Financial Risk Management
3(1)
Evolution of Basel Regulation
4(3)
What is Risk Management?
7(2)
Benefits of Risk Management
9(1)
Types of Risks in a Financial Institution/Organization
10(6)
Measurement of Operational Risk
16(4)
Need for Liquidity Risk Management
20(5)
Difference in Nature of Bank Risks
25(1)
Integration of Risks
26(1)
What is the Role of Statistical Approach to Manage Risk?
26(1)
Summary
27(1)
Review Questions
28(1)
References
28(1)
2 Description of Data and Summary Statistics for Measurement of Risk
29(24)
Data Description and Presentation
30(2)
Summary Statistics
32(1)
Coefficient of Variation (CV) = SD/Mean
33(4)
Quartiles and Percentiles
37(3)
Gini and Lorenz Curve
40(7)
Other Statistical Indices of Loan Inequality/Concentration
47(1)
Summary
48(1)
Review Questions
49(3)
References
52(1)
3 Probability and Distribution Theorems and Their Applications in Risk Management
53(32)
Probability Theorems
54(1)
Probability Properties
55(1)
Probability Rules
56(1)
Conditional Probability
57(2)
Joint Dependence
59(2)
Mutually Exclusive vs. Non-Exclusive Events
61(3)
Independent Events
64(3)
Bayes' Probability Theorem
67(1)
Repeated Trials---Draws with Replacement
68(1)
Probability and Expectations
69(1)
Probability Distribution
70(1)
Discrete Distributions
70(1)
Binomial Distribution
70(2)
Poisson Distribution
72(3)
Continuous Distribution
75(3)
Standard Normal Distribution
78(2)
Non-Normal Distributions
80(1)
Concept of Confidence Interval
81(1)
Summary
82(1)
Review Questions
83(1)
References
84(1)
4 Hypotheses Testing in Banking Risk Analysis
85(34)
Hypothesis Testing Procedure
86(1)
Statistical Concept behind Hypothesis Testing
86(1)
Power of Test
87(2)
One-Tailed vs. Two-Tailed Test
89(1)
Illustration of the Concept with Examples
89(4)
Statistical Significance through r-Statistic
93(2)
Example of One-Tailed Test
95(1)
Solution
96(1)
Analyse the Sample Data
96(1)
Statistical Test Results Interpretation
96(1)
Mean Comparispn Test (t-Test)
97(4)
Non-Parametric Wilcoxon Rank-Sum Test
101(1)
Test Procedure
101(5)
Analysis of Variance (ANO VA)
106(6)
Summary
112(1)
Review Questions
113(4)
References
117(2)
5 Matrix Algebra and their Application in Risk Prediction and Risk Monitoring
119(22)
Transition Matrix Analysis---Computation of Probability of Default
119(10)
Matrix Multiplication and Estimation of PD for Different Time Horizons
129(6)
Statistical Test on Significant Increase in Credit Risk (SICR)
135(1)
Inverse of Matrix and Solution of Equations
136(2)
Summary
138(1)
Review Questions
138(1)
References
139(2)
6 Correlation Theorem and Portfolio Management Techniques
141(22)
Portfolio Measure of Credit Risk
141(1)
Example
142(6)
Correlation Measures
148(4)
Steps for Computation of the Spearman Rank Correlation
152(2)
Measurement of Portfolio Market Risk
154(1)
Portfolio Optimization
154(3)
Integration of Risk and Estimation of Bank Capital
157(1)
Summary
158(1)
Review Questions
158(2)
References
160(3)
7 Multivariate Analysis to Understand Functional Relationship and Scenario Building
163(48)
Regression Basics
163(5)
Interpretation
168(3)
Applications of Multiple Regressions in Risk Analysis
171(1)
Multiple Discriminant Analysis (MDA)
172(6)
Diagnostic Checks
178(3)
Application of MDA Technique
181(2)
Non-Linear Probability Models-Logistic Regression
183(1)
Application of Logit Model in Risk Management
184(4)
Validation of Predictive Power of Logit Models
188(1)
Panel Regression Methods
189(3)
The Fixed Effect Model
192(1)
LSDV Model
192(1)
Limitations of Fixed Effect Approach
193(1)
Random Effect Model
194(1)
Fixed Effect vs. Random Effect Specification
194(2)
Example of Panel Regression in STATA: Factors Determine Refinancing by Housing Finance Companies (HFCs)
196(7)
Heteroskedasticity and Multicollinearity Tests
203(3)
Summary
206(1)
Review Questions
207(3)
References
210(1)
8 Monte Carlo Simulation Techniques and Value at Risk
211(22)
Types of VaR Techniques
212(1)
Steps in HS
212(1)
Steps in VCVaR
213(1)
Steps in MCS
214(1)
Value at Risk as a Measure of Market Risk
215(1)
VaR for Interest Rate Instruments
216(1)
Stressed VaR
216(1)
Credit VaR (C-VaR) for Loan Portfolio
217(3)
Operational Risk VaR: Loss Distribution Approach
220(1)
Methodology
221(1)
Kolmogorov--Smirnov Test (K--S)
221(1)
Anderson--Darling (A--D) Test
221(1)
P--P & Q--Q Plot
222(4)
Exercise-Operational Risk VaR Method
226(2)
VaR Back Testing
228(1)
Summary
229(1)
Review Questions
229(1)
References
230(3)
9 Statistical Tools for Model Validation and Back Testing
233(22)
Power Curve
234(3)
Kolmogorov-Sminrov (K-S) Test
237(2)
Information Value (IV)
239(5)
Hosmer--Lemeshow (HL) test
244(1)
Goodness-of-Fit Test
245(1)
Steps in HL Test
245(1)
STATA Example
246(2)
ROC Curve Generated from Retail Logit PD Model
248(2)
Akaike Information Criterion
250(1)
Bayesian Information Criterion (BIC) or Schwarz Criterion
251(1)
Summary
251(1)
Review Questions
252(1)
References
252(3)
10 Time-Series Forecasting Techniques for Banking Variables
255(28)
Analysis of Trend: Polynomial Trend
256(1)
Application of Trend Forecasting
257(2)
Time Series: AR and MA Process
259(1)
Stationarity
260(1)
Seasonality
260(1)
ARMA Model
260(1)
Autoregressive Model
261(1)
Stationarity Condition
262(1)
Autocorrelation Function and Partial Autocorrelation Function
263(1)
Unit Root Test
263(1)
Autoregressive Integrated Moving Average Model
264(2)
ARIMA Model Identification
266(1)
Detecting Trend and Seasonality in a Series
267(1)
Estimating the ARIMA Model-Box-Jenkins Approach
267(1)
Forecasting with ARIMA Model
268(1)
Key Steps in Building ARIMA Forecasting Model
268(1)
ARIMA Forecast Example
269(8)
Multivariate Time-Series Model
277(2)
Summary
279(1)
Review Questions
280(1)
References
281(2)
Appendix: Statistical Tables 283(6)
Index 289
Arindam Bandyopadhyay is Professor and Dean (Academic Program), National Institute of Bank Management. He is also the Editor of the journal PRAJNAN. He has a PhD and an M.Phil from Jawaharlal Nehru University.
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