HSBC Quant Academy for AGH WMS

WMS

Last Updated: 2025-05-18

Content

Goal

We provide an introduction to quantitative risk management in banking. We start with a general introduction about the history of banks, the types of banks, balance sheets, etc. Then we dive into the different main risk categories such as credit risk, financial market's risk, and counterparty credit risk.

Calendar


Note that the version in the introduction to the program is outdated.

Lectures and Content

# Lecture Description Downloads Other Resources
1 Introduction to the program An introduction to the program to set rules and agreementsSee references in the slides
2 Introduction to banking History, specialisation and risk in banksSee references in the slides
3 Risk in banking Types of risk, taxonomy, and risk management.
4 Introduction to Companies This course explores the fundamentals of wealth creation in private enterprises, linking financial statements (balance sheets, profit/loss statements) to company valuation. It introduces core concepts of valuing businesses and connects these principles to financial markets, covering instruments like equities, bonds, options, and futures. Focused on practical insights, it bridges corporate finance theory with real-world market applications.This material corresponds to part VI of the book "The big R-book: from data science to learning machines and big data." The homepage of the book is here.
5 Loss Distributions Dive into financial risk management and lean about credit, market, liquidity, and operational risks. Get familiar with tools like Value at Risk (VaR) and Expected Shortfall; and model uncertainty using statistical frameworks (Bernoulli, Binomial). Analyze credit portfolios with exposure at default (EAD) and loss given default (LGD). Explore real-world cases (subprime crisis, LIBOR manipulation) and Basel regulations.
6 Coherent Risk Measures Approaching risk measures axiomatically introduces the concept of coherent risk measures and we explore the important consequences.
  • Proposal for a Practical Implementation of Maslowian Portfolio Theory, 2016, 'Problemy Zarządzania' 2016, vol. 14, nr 4(63), t. 1, pp. 39 - 56, draft
  • The Importance of Thinking Coherently for Strategic Asset Allocation, 2016-02-03, Journal of Advances in Management Sciences & Information Systems, Vol 2. 2016, DOI: from doi.org, author's copy
  • Target Oriented Investment Advice, 2011-03-19, Journal of Asset Management, 30 June 2011, DOI: from doi.org, author's copy
  • Maslowian Portfolio Theory, 2010-05-19, Journal of Asset Management, 9 (6), pp. 359–365. DOI: from doi.org, author's copy
7 Fundamental Review of the Trading Book This lecture examines the FRTB framework’s shift from Value-at-Risk (VaR) to Expected Shortfall (ES) for market risk, addressing Non-Modellable Risk Factors (NMRF) and systemic risks from complex portfolios. Includes simulations comparing ES and VaR conservatism.
8 Credit Risk Modelling This course examines credit risk management, focusing on core components: definitions, modeling frameworks, and quantitative techniques for Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). It addresses regulatory requirements (Basel III capital rules, IFRS 9 provisioning) and practical applications, including stress testing under adverse scenarios and model validation protocols.
Teacher: Piotr KOBUS
9 Risk Based Credit Risk Modelling Credit risk-based pricing and quantitative modeling for financial decision-making. Through case studies and practical frameworks, you will learn to assess credit risk using probability of default (PD), loss given default (LGD), and exposure at default (EAD) models, evaluate calibration accuracy (e.g., Hosmer-Lemeshow tests, Brier scores), and design credit rating systems compliant with Basel III regulations. Topics include risk-adjusted return on capital (RAROC), economic capital allocation, and the impact of credit grading on loan pricing. Hands-on exercises use Python/R.
10 Credit Modelling in practice This course uses R to demonstrate credit scoring model development using logistic regression with Weight of Evidence (WoE), variable selection via Information Value, model validation (ROC/KS), and scorecard generation for risk assessment.Refer to the documentation of the scorecard package and make sure it is installed before the class.
11 Prudent Value Adjustments This document outlines Fair and Prudent Valuation (FVA/PVA) frameworks in banking, distinguishing Fair Value (exit price under orderly transactions) from Prudent Value (conservative, 90% confidence-level adjustments for uncertainties). Post-2008 regulations (Basel II, EBA RTS) mandate Additional Valuation Adjustments (AVAs)—covering market uncertainty, model risk, concentration, and administrative costs—to ensure capital adequacy. Key adjustments include Bid-Offer reserves, CVA/DVA, and liquidity risk tools. Prudent Value impacts Tier 1 capital, reflecting conservative exit assumptions and regulatory compliance, while industry benchmarks highlight variability in PVA implementation.
12 Value Adjustments This course introduces X-Valuation Adjustments (xVA) and Counterparty Credit Risk (CCR) in derivatives trading. It covers adjustments like CVA (counterparty risk), DVA (own default), FVA (funding costs), and ColVA (collateral impacts), driven by post-2008 regulations. Students learn to model exposures using tools like the Black-Derman-Toy interest rate model, analyze real-world impacts (e.g., Covid-19, bank defaults), and apply risk mitigants (netting, collateral). Topics include pricing, capital management, and regulatory compliance for derivatives.
13 End Projects The exam: select one project, solve the question and present during the last sessionSee project brief for each project

Exam

Students form groups of 3 to 5 people and present a groupwork. The groupworks consists of

  1. choose one of the provided problems
  2. solve the task as proposed
  3. prepare a report about the work
  4. present the work in a short presentation