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Post Graduate Diploma in Management (PGDM) in Quantitative Finance (QF)

Two-year, full-time, residential, AICTE-approved program.

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Program Overview & Highlights

The PGDM in Quantitative Finance (QF) at Jagdish Sheth School of Management (JAGSoM), Bangalore, is tailored for top-tier engineering, science, mathematics, or statistics graduates in India. It equips them with advanced skills in Financial Mathematics, Financial Engineering, and Financial Analytics. The program integrates mathematical models, stochastic processes, computational and machine learning techniques, and financial theories to prepare graduates for careers in finance, risk management, quantitative trading, and investment analysis.

Two-year, full-time, residential, AICTE-approved program.
The first cohort starts in August 2024.
Assured placement opportunity.
Limited 20 seats per academic year.
Exclusively for the recent graduates of IITs, IIITs, BITSs, NITs ISIs, Chennai Mathematical Institute and other similar top institutions in India.
Industry Internship.
Fully funded by JAGSoM – covering tuition fee, boarding and lodging. Graduates repay a part of the cost (INR 5,00,000) in installments to JAGSoM after they are employed.

Features of PGDM QF Program

Program Objectives

Comprehensive Understanding

To provide a thorough understanding of quantitative finance, integrating theory with practical applications.

Skill Development

To develop proficiency in stochastic modelling, risk management, algorithmic trading, and investment strategies.

Leadership Preparation

To prepare students for leadership roles in finance by augmenting their analytical, technical, and decision-making skills.

Global Perspective

To offer a global perspective on financial instruments used by investment banks and hedge funds.

FinTech in India

To prepare students for the flourishing and promising rise of FinTech in India.

Technological Advancements

  • AI and Machine Learning: Increasing integration of AI and machine learning into financial modelling and trading strategies.
  • Big Data Analytics: Leveraging large datasets for enhanced decision-making and predictive analysis.

Regulatory Changes

  • Compliance and Risk Management: Growing need for sophisticated risk management models to comply with evolving regulations.

Market Demand

  • Emerging Markets: Expanding opportunities in emerging markets requiring innovative financial products and strategies.

Sustainability and ESG

  • ESG Investing: Incorporating environmental, social, and governance factors into quantitative models for sustainable investment strategies.
  • Graduates of the PGDM – Quantitative Finance program can pursue a wide range of career paths in various sectors, including the following prominent ones:

Quantitative Analyst

  • Role: Develop and implement complex models for trading and investment decisions.
  • Skills: Advanced mathematics, programming (Python, C++), statistical analysis (R, Matlab).

Algorithmic Trader

  • Role: Design and execute automated trading strategies.
  • Skills: Programming, numerical methods, high-frequency trading knowledge.

Financial Engineer

  • Role: Develop new financial instruments and strategies using quantitative models.

Indicative Salaries (Mid-Career)

  • India: INR 35,00,000 to 50,00,000
  • Singapore/Hong Kong: INR 1,00,00,000
  • USA/UK: INR 1,25,00,000

Admission Selection Process

JAGSoM looks at innovation potential, solution orientation and value fit of the candidates, besides their consistent academic performance and work experience for admission into its PGDM in Quantitative Finance (QF).

Eligibility

  • A bachelor’s degree in mathematics, engineering, computer science, or a related field from IITs, BITSs, NITs ISIs, Chennai School of Mathematics or any other similar top institution in India.
  • Nationality: Indian.

Selection Process

  • Personal Interview with JAGSoM faculty.

Desired Skills

  • Strong analytical and quantitative skills; proficiency in programming languages such as Python, R, or C++ is preferred.

Important Dates

Curriculum and Program Structure

The PGDM – Quantitative Finance program at JAGSoM offers a comprehensive curriculum designed to equip students with the necessary skills and knowledge to excel in the competitive field of quantitative finance. Through a combination of rigorous coursework, practical experience, and industry engagement, the program prepares graduates for successful careers in different domains of Quantitative Finance. The curriculum includes:

  • Financial Accounting: Introduction to Financial Accounting, The Accounting Equation and Financial Statements, Accrual Accounting Concepts, Revenue Recognition and Matching Principles, Cash and Receivables, Inventory and Cost of Goods Sold, Long-Term Assets: Property, Plant, and Equipment, Liabilities and Equity, Financial Statement Analysis, IFRS vs. GAAP.
  • Corporate Finance: Time Value of Money (TVM), Mergers and Acquisitions (M&A), Risk & Return, Capital Asset Pricing Model (CAPM), Multi-factor Model, Risk Decomposition, Project Appraisal, Capital Budgeting: Average Economic Value (AEV), Internal Rate of Return (IRR), Modified Internal Rate of Return (MIRR), Extended Internal Rate of Return (XIRR), Decision Trees, Net Present Value (NPV), Expected NPV (ENPV), Real Options, Capital Structure Theories, Dividend Decision Theories.
  • Microeconomics: Introduction to Microeconomics, Supply and Demand, Consumer Choice Theory, Production and Costs, Market Structures: Perfect Competition, Monopoly, Oligopoly, Monopolistic Competition, Market Failures and Externalities, Income Distribution and Poverty, Labor Markets and Wage Determination, Government Intervention in Markets, Game Theory and Strategic Behaviour.
  • Macroeconomics: Introduction to Macroeconomics, Measurement of Economic Performance (GDP, CPI, Unemployment Rate), Aggregate Demand and Aggregate Supply, Keynesian Economics, Fiscal Policy, Monetary Policy, Inflation and Deflation, Economic Growth, Exchange Rates and Balance of Payments, International Trade, The Phillips Curve, Business Cycles and Stabilization Policies, Macroeconomic Models, Economic Development and Growth Theory.
  • Fundamentals of Derivatives: Introduction to Derivative Instruments, Option Pricing Models (Black-Scholes Model, Binomial Option Pricing Model, CRR), Put-Call Parity and Option Strategies, Properties of Options (Delta, Gamma, Theta, Vega), Implied Volatility and Volatility Smile, Risk Management and Hedging with Derivatives, Case Studies and Use Cases in Financial Markets.
  • Machine Learning for Finance: Mathematics of Ordinary Least Squares (OLS), Lasso, Ridge, and Elastic-Net Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), Ensemble Learning, Imbalanced Classification, Principal Component Analysis (PCA), Self-Organizing Map, Clustering, Feature Engineering, Hyperparameter Tuning and Model Optimization, Cross Validation, Pipeline Development, Application in Finance such as Forecasting, Trading, and Fraud & Anomaly Detection, and Implementation in Python.
  • Quantitative Fixed Income Securities: Bond Valuation, Bond Price Sensitivities, Bond Immunization, Hedging, AssetLiabilities Management, Fixed Income Instruments: Yield Curve Strategies, Swaps, Forward Rate Agreements (FRAs), Securitization and their Valuation.
  • Time Series Analysis with Python: Ordinary Least Squares (OLS) Regression and Assumptions, Autoregressive (AR), Moving Average (MA), and Autoregressive Integrated Moving Average (ARIMA) Processes, Cointegrated Processes, Vector Autoregression (VAR), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Practical Implementation in Python.
  • Stochastic Calculus for Finance: Introduction to Stochastic Processes, Brownian Motion and Wiener Processes, Ito's Lemma and its Applications, Stochastic Differential Equations (SDEs), Martingale Theory and Martingale Representation Theorem, Girsanov's Theorem and Change of Measure, Stochastic Integration, Feynman-Kac Formula, BlackScholes Equation and its Derivation, Risk-Neutral Pricing and the Fundamental Theorem of Asset Pricing, Applications of Stochastic Calculus in Option Pricing, as well as other area within Finance.
  • Deep Learning for Finance: Introduction to Deep Learning Concepts, Multilayer Perceptron (MLP) Architecture, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) Networks, Autoencoders and Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Application of Deep Learning in Financial Forecasting, Time Series Prediction, Algorithmic Trading, Sentiment Analysis, Fraud Detection, Implementation in Keras and TensorFlow.
  • Advanced Derivatives: Local Volatility Models, Stochastic Volatility Models, Stochastic Integration, Credit Derivatives, Gaussian Copula, Variance Swaps, Currency Derivatives.
  • Portfolio Optimization: Introduction to Portfolio Theory, Markowitz Mean-Variance Optimization, Efficient Frontier Analysis, Capital Allocation Line (CAL), Sharpe Ratio Maximization, Modern Portfolio Theory (MPT), Black-Litterman Model, Factor Models and Smart Beta Strategies, Risk Budgeting Portfolio, Risk Parity Portfolios, Minimum Variance Portfolios, Mean-Semivariance Optimization, Mean-Downside Risk Optimization, Mean-Variance-Skewness Optimization, Mean-Variance-Skewness-Kurtosis Optimization, Mean-VaR Optimization, Mean-Conditional VaR Optimization, Mean-Marginal VaR Optimization, Mean-Max Drawdown Optimization, Mean-Avg Drawdown Optimization, Multi-Period Portfolio Optimization, Dynamic Asset Allocation Strategies, Performance Measurement and Attribution, Monte Carlo Simulation for Portfolio Optimization, and other Optimization Techniques.
  • Computational Methods in Finance: Generating Random Numbers: Inverse Transform Approach, Acceptance Rejection Approach, Polar Method, Box-Muller Approach, Rational Approximation Method; Variance Reduction Method, Monte-Carlo Simulations for Option Pricing, Finite Difference Method: Explicit, Implicit, Crank-Nicolson for Option Pricing, Euler Discretization, Cholesky Decomposition, LU Decomposition, Simulation of Financial Processes, Implementation in Python.
  • Market Risk Modelling: Historical VaR, Parametric VaR, Monte Carlo VaR, VaR Mapping, Backtesting VaR, Expected Shortfall, Coherent Risk Measure, Stress Testing, Scenario Analysis, Sensitivity Analysis, Risk Aggregation, Liquidity Risk, Tail Risk, Volatility Modelling, Term Structure Models, Regulatory Frameworks.
  • Credit Risk Modelling: Credit Analysis, Default Risk, Credit Scoring Models, Expected and Unexpected Loss, Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), Counterparty Risk, Credit Valuation Adjustment (CVA), Wrong Way Risk, Right Way Risk, Credit Risk Mitigation Techniques, Credit Derivatives, Stress Testing, Credit Portfolio Management, Loan Loss Provisions, Rating Agencies.
  • Financial Risk Management: Portfolio Risk, Risk Budgeting, Component VaR, Incremental VaR, Marginal VaR, Currency Risk, Interest Rate Risk, Liquidity Risk, Operational Risk, Risk Identification and Assessment, Risk Measurement and Metrics, Risk Reporting and Monitoring, Basel Accords (Basel I, II, III), Enterprise Risk Management (ERM), Capital Adequacy, Hedging Strategies, Internal Controls and Audits, Economic Capital, Risk Transfer Mechanisms, Risk Mitigation Techniques, Emerging Risks.
  • Quantitative Trading Techniques: Development and Implementation of Trading Strategies, Statistical Arbitrage, Pairs Trading, Momentum Trading, Mean Reversion Strategies, High-Frequency Trading (HFT), Algorithmic Trading, Machine Learning Techniques for Trading, Backtesting and Simulation of Trading Strategies, Risk Management in Quantitative Trading, Execution Strategies and Market Microstructure, Signal Processing, Optimization Techniques for Strategy Enhancement, Sentiment Analysis, News Analytics, Technical Analysis Integration, Transaction Cost Analysis (TCA), Performance Measurement and Attribution, Regulatory Considerations and Compliance in Algorithmic Trading, Strategy Evaluation and Improvement, Market Impact Models, Liquidity Provision and Market Making, Implementation in Python.
  • Stochastic Modelling: Maximum Likelihood Estimation, Monte Carlo Simulation, Cox-Ingersoll-Ross Model (CIR), Vasicek Model, Ho-Lee Model, Hull-White Model, Heston Model, SABR Model, Simulation-Based Pricing and Valuation, Stochastic Differential Jagdish Sheth School of Management (JAGSoM) Electronic City Phase I, Bangalore, India 8 Equations (SDEs), Calibration Techniques, Volatility Surface Modelling, Interest Rate Models, Application in Derivatives Pricing, Risk Management, and Hedging Strategies.
  • Interest Rate Derivatives: Swaps, Forward Rate Agreements (FRAs), Interest Rate Futures, Interest Rate Options, Cross Currency Swaps, Cross Currency Basis Swaps, Swaptions, Swaption Collar, Interest Rate Caps and Floors, Interest Rate Exotics, Credit Default Swaps (CDS), Credit-Linked Note.
  • Reinforcement Learning in Finance: Introduction to Reinforcement Learning (RL) Concepts, Markov Decision Processes (MDPs) and Dynamic Programming, Exploration-Exploitation Tradeoffs, Value Function Approximation Techniques, Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods, Actor-Critic Architectures, Model-based RL Approaches, Reinforcement Learning for Portfolio Management, Risk Optimization, Algorithmic Trading Strategies, Market Making and Liquidity Provision, Optimal Execution Strategies, Portfolio Rebalancing, Derivative Pricing and Hedging, Automated Trading Systems, Risk Management Applications, Real World Challenges and Considerations, Ethical and Regulatory Implications, Case Studies and Applications in Financial Markets.
  • Optimization Methods in Finance: Linear Programming, Asset/Liability Modelling, Nonlinear Programming, Quadratic Programming, Conic Optimization, Integer Programming, Dynamic Programming, Stochastic Programming, Monte Carlo Simulation Optimization, Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Evolutionary Strategies, Metaheuristic Algorithms, Application in Finance.
  • Internships: Opportunities to work with leading financial institutions, gaining hands-on experience in quantitative finance.
  • Capstone Projects: Real-world projects involving the development and testing of quantitative models under the guidance of faculty and industry experts. Development and implementation of trading strategies on actual capital funded by JAGSoM.
  • Research Seminars: Exposure to the latest research and developments in quantitative finance through seminars and workshops.

Certifications (Optional)

  • MSc in Financial Engineering from World Quant University, USA - Free of cost.
  • FRM from GARP, USA - Cost: USD 1600.
  • CQF from CQF Institute, Fitch Learning, London - Cost: USD 8,495.
Year 1Term 1Term 2Term 3
  1. Financial Accounting
  2. Human Capital Management
  3. Marketing Management
  4. Managerial Decision Making
  5. Organizational Behaviour
  6. Corporate Finance
  7. Business Economics
  8. Python Programming
  9. Probability and Statistics
1. Macroeconomics
2. Probability and Statistics
3. C/C++ Programming
4. Fundamental of Derivatives
6. Time Series Analysis with Python
  1. Econometrics and Financial Time-Series Analysis in Python
  2. Quantitative Fixed- Income Security
  3. Fundamentals of Derivatives
  4. Advanced Corporate Finance
  5. Machine Learning for Finance
  Year 2Term 4Term 5Term 6
  1. Stochastic Calculus for Finance
  2. Deep Learning for Finance
  3. Advanced Derivatives
  4. Portfolio Optimization
  5. Technical Analysis
  1. Reinforcement Learning
  2. Computational Methods in Finance
  3. Stochastic Modelling
  4. Financial Risk Modelling
  5. Quantitative Trading Techniques
  1. Signal Processing: Fourier, Laplace and Wavelet Transform
  2. C/C++ Programming
  3. Optimization Methods in Finance
Capstone Project

Key Program Faculty

The PGDM-QF program is taught by a distinguished faculty comprising academicians and industry professionals with extensive experience in quantitative finance. Guest lectures and workshops by industry experts further enrich the learning experience.

DR. AMIT RAM PUNIYANI

Alumnus of IIT Bombay & Stanford University, USA
Ex VP - Deutsche Bank, Credit Suisse

DR. AKHILESH PRASAD

Alumnus of IIT Kharagpur & SP Jain School of Global Management, Sydney
Ex IBM, Oracle

supriyo ghose

DR. SUPRIYO GHOSE

Alumnus of IIM Calcutta
Ex Infosys, PwC, TCS

tuhin chattopadhyay

DR. TUHIN CHATTOPADHYAY

Ex CTO - QuantFarm

DR. SRIJITH MOHANAN

Alumnus of IIT Kharagpur & IIM Bangalore
Ex COO - Deutsche Bank

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JAGSoM is amongst the select few Business Schools in India with an International Profile: AACSB Accredited & QS Ranked for Marketing, Finance and Analytics programs.

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