Quantitative Finance and Risk Management (QFRM)

Module ID Module Title Credit Description Content
MBA901 Foundations of Economics and Finance 5 This module will provide the foundational understanding of economic terms and their application in equity and derivatives markets investment. At the same time, to introduce the students to the basics of financial concepts and develop a firm theoretical understanding.
  • ● Introduction to macroeconomic indicators and financial instruments.
  • ● Monetary and Fiscal policies - models and dynamics
  • ● Exchange rate determination, forex risk management practices, trade: current and capital accounts, trade policy.
  • ● Future and Present Values, annuities, perpetuities, compounding and measuring returns.
  • ● Basics of portfolio construction, mean-variance framework, Optimal portfolio analysis with riskless asset, capital allocation framework, optional portfolios with multiple assets.
  • ● Bond and its types, valuations, yield curve and duration
MBA902 Introduction to Derivatives 5 This module introduces the students to the pricing and valuation of derivative contracts, primarily focusing on contracts traded in the market. It also elaborates on the various theoretical frameworks linked to different types of commodities and financial instruments.
  • ● Basics of Derivatives Markets and Derivatives Markets in India.
  • ● Mechanics of Futures Markets - Forwards Contracts, Valuation.
  • ● Margining and Mark-to-market in Futures markets.
  • ● Hedging and Risk Management with Futures Contracts - Minimum Variance Hedging Strategy.
  • ● Futures Markets in India: Instruments and Specifics (Demonstration).
  • ● Options: Payoff structure, Basic trading strategies using Options.
MBA903 Quantitative Methods in R and Python 5 The primary objective of this module is to equip the students with various tools and techniques and their applications for better understanding and investment decisions. Through this module, the students will develop an ability to analyze the data by applying appropriate quantitative methods.
  • ● Overview of financial econometrics, statistical foundations: data, Visualizing and describing the data, descriptive statistics and data summary.
  • ● Role of linear regression in financial data modelling, assumptions, violations, diagnostics, and two-stage procedures.
  • ● Introduction to time series, autocorrelation and forecasting techniques.
  • ● Fixed effects and random effects and instrumentation process.
  • ● Logit, Probit, Tobit and other variants and their applications.
  • ● Monte Carlo simulations, Variance reduction techniques, bootstrapping and random number generation.
MBA904 Security Analysis and Portfolio Management 5 The module offers comprehensive learnings about security analysis and portfolio and exposes the practical side of security analysis and portfolio management.
  • ● Introduction to financial markets, investment alternatives, risk and return.
  • ● Optimal portfolio analysis with the riskless asset, capital allocation framework, optional portfolios with multiple assets, single index formulations.
  • ● CAPM, APT models, Factor models.
  • ● Return anomalies and market efficiency.
  • ● Security Analysis and Valuation.
  • ● Fundamental analysis, investment strategies.
MBA905 Treasury and Credit Risk Management 5 This module trains the students with different types of risks faced by firms. The module will focus on the advanced treatments of different risk management practices and provide exposure to regulatory norms.
  • ● Introduction to treasury risk management, its underlying usefulness in risk management: role and scope.
  • ● Cash forecasts, short-term finance, cash budgets, working capital management.
  • ● Long-term finance, cost of capital, capital investment appraisal, capital rationing.
  • ● Financial and non-financial risk measures: volatility, VaR; credit and counterparty risk management.
  • ● Credit risk in swaps, FRAs, and options.
  • ● Settlement risk, netting requirements, capital treatment, and margin and collateral requirements.
MBA906 ML in Financial Modeling 5 This module aims to understand the big data problems in finance. This module focuses on the various models for applying machine learning in quantitative finance, such as quantitative risk modeling with kernel learning and derivatives markets and risk management.
  • ● Basics of Machine Learning and difference between ML and Statistical Modelling: USE Case of ML in finance. Why ML Proliferation i.e. Use of Data, Computation Power; Use Case of SPAM Filtering.
  • ● Generalization and Regularization and Basics of Python Libraries.
  • ● Understanding Model Fit: Variance and BIAS, Use of Decision Trees, K Means Clustering.
  • ● Ensemble Methods: Boosting and Bagging Techniques, LSTM and Karas Modelling.
  • ● ML in Active Management.
  • ● ML in Risk Management.
MBA907 Advanced Derivative Contracts and Pricing 5 The module provides an in-depth understanding of derivatives contracts with a balanced exposure to futures and options initially. It then introduces pricing and valuations across a wide range of derivative types including commodity, weather, carbon, freight, property, and payroll derivatives. It also covers critical risk management strategies, hedging techniques, and speculation frameworks that are essential in modern financial markets.
  • ● Pricing and valuations of commodity futures:
    • Pricing of forwards and futures, spot and forward relationship under the new dynamics of commodity derivatives. No-arbitrage, market value of futures, cost of carry model (seasonal and non-seasonal), convenience yield, pricing of precious metals: Gold and Silver, spread arbitrage, pricing of forwards for storable consumption commodities.
  • ● Pricing and valuations of non-storable commodities:
    • Electricity derivatives, DAM, DAC, TAM, Daily Term Ahead Market, RECs, Nord Pool, valuations of gas storage facility, swing options.
  • ● Pricing and valuations of weather derivatives:
    • Weather risk and weather derivatives, temperature-based contracts, wind speed derivatives, rainfall futures and options.
  • ● Pricing and valuations of carbon derivatives:
    • Emission trading standards, CDM, carbon credit mechanism, allowance units, CER and ERU pricing, switching price and carbon credits.
  • ● Pricing and valuations of freight, property, and payroll derivatives:
    • Freight exchanges, freight indexes, REITs, payroll and water derivatives, Baltic Freight market, forward freight agreements, options.
  • ● Hedging and speculation with futures:
    • Types of hedges, profit margin and inverse hedging, enhancements, speculation and investment process, cross-hedge, tailing the hedge.
MBA908 Blockchain Applications in Finance 5 This module provides an overview of blockchain technology and its applications in the financial domain. It is supported by real-world illustrations and use cases to facilitate effective learning. The module explores the evolution, current state, and future potential of blockchain in transforming traditional finance systems.
  • ● Introduction
  • ● Evolution and Genesis
  • ● Blockchain in Finance
  • ● Blockchain in Finance Application – 1
  • ● Application – 2
  • ● Wholesale P2P Trading
MBA909 Technical Analysis in Finance 5 This module helps to learn about various methods of detecting and identifying trends and develop trading strategies.
  • ● Introduction:
    • Introduction to Technical Analysis, importance, basic rules and terminology, philosophy, price, volume and time, pattern
  • ● Trend Recognition:
    • Real time chart, understanding various types of charts - moving averages, Bars, Candles, Hollow candles, Heikin Ashi, Renko, Kagi etc.
  • ● Chart:
    • Chart and Candlestick patterns, Bullish and Bearish patterns
  • ● Technical Indicators:
    • Introduction to technical indicators, leading indicators, lagging indicators, pivot point, Oscillators, Advantages and disadvantages
  • ● Technical Indicators - Oscillators:
    • Types of Oscillators (Momentum, Centered, Banded, Stochastics), Gap analysis
  • ● Commodity Trading Strategies:
    • Commodity market, products, Types of investors and indices
MBA910 Advanced Financial Modeling 5 This module helps students get an overview of financial modelling in the equity and derivatives markets and explore the tools and techniques required for analysing financial data of different frequencies.
  • ● Introduction to Financial Modelling for equity and derivatives:
    • Mathematical Foundation, Time-Series properties, Stationarity, ARIMA models, AIC/BIC Criteria, MLE; Recap of OLS, Panel data, Quantile, and Logistic regressions
  • ● Multivariate time-series models:
    • Simultaneous Equation approach, Vector Auto Regressions, Impulse response functions, Variance decomposition, Granger Causality test. Case study in derivative markets
  • ● Modelling short-term and long-term relationships:
    • Non-stationarity and unit root testing, Error correction models and cointegration. Case study in commodity Markets
  • ● Dynamic volatility models:
    • Conditional Volatility Models, GARCH variants, Conditional Quantile, Spillover models, Dynamic hedge ratios, portfolio rebalancing
  • ● Regime Switching Models:
    • Seasonality and cycles, Markov Regime Switching Models, estimation and residual diagnostics, state-space models. Case study in energy markets
  • ● Events Study Analysis:
    • Econometrics of event study, normal and abnormal returns, carbon market applications
  • ● Price discovery across equity and derivative markets:
    • Lead-lag relationships, information share methods, structural breaks, time-varying approaches, equity-derivatives applications
MBA911 Project-I 5 Project-I Capstone Project
  • ● Capstone Project: Application of concepts from previous modules in a practical project
MBA912 Project-II 5 Project-II Capstone Project
  • ● Capstone Project: Final phase of applied learning and research presentation