Information Session - Quantitative Risk Management and Business Analytics

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Kerr Hall South, Room 239

350 Victoria Street

Toronto, ON M5B 0A1


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Course Objectives

This course is to demonstrate how quantitative analysis is used in financial analysis and risk management in the banking industry, with the emphasis on the application of how to develop, evaluate and analyze the financial and risk measures to comply with regulatory and internal management requirements.

The course consists of three core components are

1) The business concepts of the financial analysis and risk management;

2) The standard procedure of quantitative analysis and the industry common practice;

3) A hands-on project to build a predictive model with full lifecycle development to forecast the bank’s pre-provision net revenue for loan portfolio from macroeconomic drivers.

Potential Job Positions

Quantitative risk analyst

Quantitative risk manager

Business analyst

Credit risk modeler

Stress testing analyst


Jonathan Wang, CFA

Jonathan has over 15 years of experience in Canadian banking industry. He is a seasoned financial professional with expertise across quantitative analysis & modeling, risk management (credit, liquidity & market risk), capital market, collateral management, corporate treasury, and enterprise data warehouse.

Prior to his current senior consulting role in risk analytics and regulatory reporting at a major Canadian bank, he also served many senior management roles in Canadian banks, including the director of Treasury and Global Risk Management, and the senior manager in Financial Modeling.

Jonathan is a CFA charter holder. He is also a certified Personal Financial Planner and SAS modeller. He will best leverage his strong cross-business background to present you a good application of financial modeling in Financial Risk Management (FRM), especially in the context of the inter-relationship between regulatory, financial, risk and capital management issues in Financial Institutions.

Course Outline

1. The Object of Quantitative Analysis – Introducing the Business Concepts

1) Financial analysis:

  • Pricing & valuation
  • Performance evaluation (ROE, RAROC etc.)
  • Profitability analysis (NII, NIR, PCL, Cost etc.)
  • Financial reporting & analytics
  • Customer segmentation & marketing

2) Risk Management:

  • Risk appetite & framework
  • Risk category and its magnitude
  • Risk Management -limit & excess monitoring, credit scoring
  • Risk mitigation – securitization, collateral
  • Risk weighted asset and risk capital

3) Fundamental statistics:

  • Descriptive & inferential statistics
  • Hypothesis test and error types: significance level α; p-value; decision rules
  • Regression: explanatory & residual analysis
  • How to read and apply statistics test results in quantitative analysis

2. Quant Procedure Step 1: Data Analysis

1) Data collection & exploratory analysis

  • Data collection, cleaning and segmentation
  • Data distribution stats
  • Data categorization: time series, cross-sectional …

2) Data remediation

  • Outlier identification and smoothing techniques
  • Seasonality analysis

3) Data Transformation

  • Change vs growth
  • Log and its implication

4) Data normalization and standardization

3. Quant Procedure Step 2: Model Development

1) Model variable selection:

  • Business rationale and correlation direction
  • How to identify the critical data element (CDE)
  • Test the significance of correlation
  • Driver selection process

2) Model selection

  • Model framework and philosophy
  • Choice of models and their application (regression, ARIMA, simulation….)
  • Model specification

3) Variable transformation techniques (in case of insignificant correlation)

4) Build a OLS (Ordinary Least Square) Linear Regression Model

5) How to read the statistic test results

4. Quant Procedure Step 3: Performance Assessment and Maintenance

1) Model fit

  • Fitness assessment
  • Robustness assessment
  • Accuracy/convergence
  • Common tests & how to read the test results

2) Model Performance

  • In-sample vs out-of-sample tests
  • Sensitivity analysis
  • Discriminatory analysis

3) Model maintenance

  • Regime change identification
  • Model calibration

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Kerr Hall South, Room 239

350 Victoria Street

Toronto, ON M5B 0A1


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