Department of Management Sciences (DoMS)

*Initially [ Industrial and Management Engineering (IME) ]

Programme Name: Quantitative Finance and Risk Management (QFRM)

Module

Module Title

Credit

Description

Content

MBA901

Foundations of

Economics and

Finance

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

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

● Basics of Derivatives Markets and

Derivatives Markets in India.

● Mechanics of Futures Markets -Forwards

Contracts, Valuation.

● Margining and Mark-to-market in Futures

to different types of commodities and

financial instruments.

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

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

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 port-folios 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

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

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

The module provides an in-depth

understanding of derivatives contracts

with a balanced exposure to futures and

options initially, and then it introduces to

● Pricing and valuations of commodity

futures

○ Pricing of forwards and futures,

spot and forward relationship under

the new dynamics of commodity

derivatives. The valuations of forwards

and futures are crucial from the

perspectives of price discovery and risk

management.

no-arbitrage, the market value of

futures positions, 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

○ 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, valuations of

temperature-based derivatives

contracts, contract size, wind speed

derivatives, rainfall futures and

options.

● Pricing and Valuations of Carbon

Derivatives

○ Rules and regulations, Emission

trading standards, CDM,

Mechanism of Carbon Credits,

Pricing of Carbon Units: Allowance

Units, CER and ERU, switching

price and Carbon Credits.

● Pricing and Valuations of Freight,

Property, and Payroll

○ Functioning of Freight exchanges,

freight indexes,

REITs, Payroll and Water

derivatives valuations,

Pricing and the Baltic Freight

market, forward freight agreements

and Options.

● Hedging and speculation with futures

○ Types of hedges, profit margin

hedging, inverse hedging,

enhancements, speculation and

investment process, cross-hedge,

tailing the hedge.

MBA908

Blockchain

Applications in

Finance

This module provides an overview of

blockchain technology and its

applications, supported by illustrations

and use cases for effective learning.

● Introduction

● Evolution and Genesis

● Blockchain in Finance

● Blockchain in Finance Application – 1

● Application – 2

● Wholesale P2P Trading

MBA909

Technical

Analysis in

Finance

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, Penko, Kagi

etc

● Chart

○ Chart and Candlestick patterns,

Bullish and Bearish pattern

● 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

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 the financial data

of different frequencies.

● Introduction to Financial Modelling for

equity and derivatives

○ Mathematical Foundation, Time -

Series properties, Introduction to

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 based application

in derivative markets.

● Modelling short-term and long-term

relationships

○ Non-stationarity and unit root

testing, Error correction models and

cointegration. Case study based

application in commodity Markets.

● Dynamic volatility models

○ Conditional Volatility Models,

Standard and Non-standard

GARCH model, BEKK GARCH,

DCC GARCH, Conditional Quantile

and Time-varying Spillover models,

Dynamic Conditional correlations,

Dynamic hedge ratios and portfolio

rebalancing

● Regime Switching Models

○ Seasonality and cycles in financial

markets, forecasting with regime

switching models, Markov Regime

Switching Models, model

estimation and residual diagnostics,

state-space models. Case study

applications in energy markets.

● Events Study Analysis

○ Econometrics of event study

approach, estimating normal and

abnormal returns, application to

carbon markets.

● Price discovery across equity and

derivative markets

○ Lead-lag relationships, information

share and component share

methods, structural breaks in

relationships, price discovery with

time-varying approaches,

Applications to equity-derivatives.

MBA911

Project-I

Capstone Project

MBA912

Project-II

Capstone Project