Advanced Programme in Quantitative Finance and Risk Management (APQFRM - Batch 1)

Objectives

The main objective of this programme is bring together experienced and young quantitative analysts and managers in large financial institutions, hedge funds and boutique trading and risk analytics firms and provide them with

1. mathematical and economic foundations of derivatives pricing and risk management
2. an in-depth understanding of the famous Black-Scholes model, its applications and extensions
3. training in numerical methods used in computational finance (via Python and R)
4. an introduction to most commonly used machine learning methods in finance
5. knowledge of mathematical techniques used in quantitative risk management and
6. a peek into the state of the art in quantitative finance, machine learning applications and risk management

Contents & Pedagogy

Module 1: Building blocks

• Financial markets, products and institutions
• Principle of no-arbitrage and pricing of forwards, futures and options
• Essentials of calculus, linear algebra, probability theory and stochastic processes for quantitative finance
• Properties of Brownian motion and applications of Ito’s lemma

Module 2: The Black-Scholes model, its applications and extensions

• Different ways to get to the Black-Scholes model and their equivalence
• The idea of option Greeks, volatility smile and local and stochastic volatility models
• Early exercise feature, barriers and design of structured products
• The numeraire change toolkit and the Libor Market Model

Module 3: Machine learning applications in finance

• PAC learning framework for machine learning
• Support vector machines, Neural networks and LASSO
• Online learning and reinforcement learning
• Importance sampling and Markov Chain Monte Carlo methods

Module 4: Advanced topics in risk management

• Value at risk, conditional value at risk and other tail risk measures
• Probability of default, expected loss and credit risk
• The Merton and the Moody’s-KMV model of credit risk
• Backtesting, stress testing and model risk management

Counterparty risk: CVA, DVA and other adjustments

Pedagogy: The pedagogy will be highly interactive, consisting of a blend of lectures, real life case studies, in-class exercises, assignments and a supervised group project. In the final campus module, all groups would be required to present their project and findings to the entire class.

Eligibility and Admission Criteria

• Educational Qualification: Applicants must be working professionals with a graduate degree (10+2+3 or equivalent) in any discipline, securing at least 50% marks. On a strictly case by case basis, the program would also consider final year students of four year graduate degree programs in subjects like engineering, mathematics, physics and computer science.
• Work Experience: The programme is open to professionals at all levels – senior, middle, and junior management, as well as entrepreneurs.
• The eligible candidates will be selected based on their educational and professional background and motivation based on the application form submitted.
• Applications with Credentials & Corporate Nominations – Selections will be based on a detailed Profile of Candidate in their own words elaborating their Academic record, Profile, Designation, Salary, Roles, Responsibilities, Job Description along with a Statement of Purpose.

For Whom

This programme is designed for professionals at all levels – junior, middle, and senior management, with a good background in mathematics. It is particularly relevant for:

• Risk analysts, quantitative analysts, traders, and risk managers
• Professionals working in boutique trading and risk analytics firms and captive research and knowledge centers
• Individuals employed in banks and hedge funds
• Graduating final year students with a degree related to engineering or mathematics who have a placement offer (or are looking) to work in roles related to quantitative finance.