Certificate in Quant Finance & Risk Engineering

Certificate in Quant Finance & Risk Management

AI is revolutionizing Quantitative Finance (QF) by enabling advanced trading strategies, sentiment analysis, and sophisticated risk modelling. The Certificate in Quant Finance & Risk Engineering from School of Quant Finance (SQF) integrates AI to equip participants with cutting-edge skills in this evolving field. It’s a novel blend of mathematics, statistics, and artificial intelligence that that takes you deep into the realm of risk management.

Program Details

Ideal for working professionals as well as students
Seven months (320 hours)
INR 1,79,500 (All inclusive) for participants in India
USD 2,450 for those registering from outside India
Super Early-bird discounts, Early-bird discounts, group discounts and special prices for students are available. Please contact us for more information
Fee payable in 3 installments
Classes are held on weekends, i.e., Saturdays and Sundays in the afternoon 3–8 pm IST (9:30 am–2:30 pm GMT).
Lectures will be live i.e. led by the instructor in real-time
Recording of each class will be available to all students

Eligibility

Who should attend?

This program is ideal for those passionate about leveraging quantitative methods in finance and aspiring to be part of the elite world of risk and investment professionals.

Regardless of the industry sector you are in now, if you possess a strong background in mathematics at the undergraduate level, this is for you, even if you have no experience in finance or programming.

Specifically, this program is highly beneficial for:

Students of engineering, physics, mathematics, statistics, economics, and operations research: Those with a strong foundation in calculus will find the curriculum directly applicable and enriching.

Software professionals: If you are looking to transition into quant role or enhance understanding of financial modelling.

Current finance professionals: Risk analysts, quantitative analysts, traders, and risk managers seeking to upskill themselves in advanced quantitative finance and risk management techniques. Especially those specializing in OTC valuations will find the program highly relevant.

Aspiring traders and finance professionals: If you are looking to make an impact in alternative investment or aiming for roles in quantitative asset management, trading, financial engineering, and risk management, you will gain the necessary foundation and practical skills.

Individuals in banking and hedge funds: Professionals working in boutique trading and risk analytics firms, or captive research and knowledge centers of global banks, who wish to deepen their quantitative skills will benefit significantly.

Graduating final year students: If you have a degree related to engineering, physice, mathematics, or operations research and you are seeking roles in quantitative finance, you should consider this program to jumpstart your career.

Anyone seeking to develop sophisticated technical skills: The program is designed for those who want to understand how financial instruments are designed, priced, traded, and deployed in a competitive market environment.

Curriculum: A bird’s eye view

1. Foundation in Maths

(Asynchronous) Mathematics for Quant Finance

Matrices, Ordinary and partial differential equations, Sequences and series, Taylor series.

Probability and Statistics

Basics of probability, Distributions, Expectation, Functions of a random variable, Moment generating function, Central Limit Theorem.

2. Explore the World of Finance

Foundations of Finance and Bond Mathematics

Forward rates, Yield-to-maturity, Duration, Convexity, Hedging with bonds, Floating rate notes, Interest Rate Swaps.

Options

Arbitrage Relationships for Option Prices, Put-Call parity, Trading strategies involving options, Two-step and multi-step Binomial Tree for option pricing.

3. Essential Tools

Python Programming for Finance

This course builds Python programming skills with emphasis on data types, functions, modules, OOP, and file handling. It also introduces NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization.

Time Series Modelling and Forecasting

Covers AR(p) and MA(q) models (stationarity, invertibility, GE log returns examples), ARIMA framework (differencing, model selection via AIC/BIC), and forecasting with ARCH/GARCH for volatility clustering in financial data.

4. Heart & Soul of Quant Finance

Stochastic Processes for Quant Finance

Definition and classification of stochastic processes, discrete-time and continuous-time processes, martingales, Brownian motion.

Stochastic Calculus for Derivative Pricing

A rigorous introduction to stochastic calculus and its applications in finance. Arbitrage-free valuation of contingent claims derived by martingale methods. Black-Scholes option pricing theory. Applications to complex contingent claims.

5. Numerical Methods

Monte Carlo Simulation for Financial Engineering

Generation of random numbers, Inverse Transform, Acceptance-Rejection Method, Pricing Path-Dependent Options, Variation Reduction Techniques, Control Variates, Antithetic Variates, Envelop Rejection method, Box-Muller method, Quasi Monte-Carlo Simulation

6. Structured Products

Fixed-Income Securities

Repo, Interest-rate futures, Black’s formula for swaptions, Bond futures, Securitization, Mortgage-backed security (MBS)

7. Credit Derivatives

Modelling and Valuation of Credit Derivatives

Structural Models, Reduced Form Models, The Hazard Rate Model, Valuation of a CDS, Calibrating the CDS Survival Curve, CDS Risk Management.

8. Risk Analytics

Value at Risk

This course starts with the basics about VaR and builds a thorough knowledge base including advanced concepts necessary for risk analysis. Applications of VaR and various methods for calculating VaR, including numerical methods, are discussed.

Market Risk

This course discusses risks that arise from uncertainty and volatility of interest rates, and ways to quantify them and manage them. The second part of the course discusses risks inherent in the use of derivatives, especially options and ways to hedge them.

Credit Risk

This course dives deep into the Credit at Risk (CaR), ways of measuring credit risk, and managing them. The second part of the course discusses counterparty risks including CVA and DVA

9. AI Applications in Quant Finance

Machine Learning: Algorithms, Models, and Performance Analysis

This course introduces core machine learning concepts, including supervised, unsupervised, and reinforcement learning. It covers regression, classification, decision trees, probabilistic models, and evaluation metrics for practical applications.

Transforming Finance with AI: Trading, Sentiment Analysis, and Risk Modelling

Explores AI and ML application in Finance, including algo trading, feature engineering, time series application and regulatory standards.

What Distinguishes Certificate in Quant Finance & Risk Engineering of SQF?

World-class faculty
Curriculum at par with the world’s best
Thorough coverage of risk analytics
Unmatched rigor in theoretical underpinnings
Hands-on implementation of the important models
Specialized support for interview preparation to secure quant roles at leading financial firms.

Career Opportunities

Graduates can pursue various roles in the quantitative finance sector:

Front Office or Desk Quant: Implements models required by traders
Data Engineer: Works in Risk/Finance/Compliance
Machine Learning Model Validation Expert
Risk Quants Modeler
Risk AI Model Validator
Risk Products AI Researcher
Model Validation Quant: Independently implements pricing models to check that front office models are correct
Research Quant: Conducts research to find new pricing approaches
Quant Developer: Implements given models in a major programming language either for testing the given model or for product development; good for techies!
Statistical Arbitrage Quant: Works on finding patterns in data to suggest automated trades

Still have questions?

Email: info@sqf.org.in

Teach at SQF

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