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This course covers the financial markets unprecedented changes with the advent of information technology. The markets are not the same as a few decades ago. Viewing markets today from the same old lenses will more be a myopic vision. Unlike the classical framework of fundamental and technical analysis, today’s market framework also demands a high level of expertise in quantitative techniques to understand the functioning of the market, the asset price formation, and the intuitive behavior of assets receiving signals from external sources.

The advent of Algorithmic and High-frequency trading brought all-new challenges. To handle this transition, which encompasses significant big data handling, requires the finesse of understanding the concepts of probability theory, financial econometrics, and stochastic calculus. And with the introduction of Artificial Intelligence has brought dynamic changes in the world of quantitative finance

This course is suitable for those students who want to pursue an advanced finance course dealing with mathematical aspects and keen to understand the intuition about how markets work? What is the inherent mechanism to deal with different asset classes? Besides, learning the application of AI techniques in finance.

2. Student learning outcomes

· Be able to gain intuition and understand market microstructure behavior.
· Be able to understand the mathematical framework of the pricing mechanism.
· Be able to handle big data with R programing language.
· Be able to revisit the various application of quant financé to different asset classes.
3. Text Books and Reading materials

Text Books

· Stochastic Calculus for Finance 1 – Steven Shreve Springer
· Stochastic Calculus for Finance 2 – Steven Shreve Springer
· An introduction to the mathematics of financial derivatives – Salih N. Neftci.
· Introduction to Time Series Analysis and Forecasting – Montgomery, Jennings, & Kulahci – Wiley

Reading Materials and Cases

4. Tentative Session Plan

Session Number	Topics/Activities	Reading/case list etc.
1.	This session will discuss the quant world and Probability Primer	Shreve Part 1
2 & 3.	Discussion on review of Probability – Expectations, moments, distribution functions including multivariate Gaussian, Change of Measure	do
4.	This session will discuss on Information, Filtrations, Independence, Conditioning, and introduction to Martingales	do
5.	This session will cover an introduction to time series models: A brief discussion and handling of high-frequency data in R	Montgomery et al.
6.	This session will introduce Nonlinear Models – Neural Networks and Markov Switching Models with R	do
7 & 8	This session will cover artificial intelligence and Machine learning in finance – Discussion will be on Genetic Algorithm, Hill Climbing, Simulated Annealing, and particle swarm optimization	Materials and articles will be provided
9.	Portfolio building using Genetic Algorithm – Revisiting the Markowitz World Using R	do
10 & 11	This session will cover Risk-Neutral Pricing & Hedging using Binomial and trinomial option pricing model; What is Needed to Generalize It – Case Study	Shreve part 1 & 2
12 & 13.	These sessions cover the discussion on Random walk and Brownian motion, Properties of Brownian Motion and particular discussion on Geometric Brownian Motion and workings with R	Shreve part 2
1 4 & 15.	These sessions will cover Stochastic Calculus: Itô’s Integral, Itô’s Lemma and general Ito’s lemma – Black – Scholes (BS) Model workings with R	do
16.	The discussion will be on a brief introduction to Stochastic Partial Differential Equations and Feynman-Kac Theorem	do
17.	This session will cover the application of the theme to Derivative Segment and discussion on beyond BS model	Materials and articles will be provided
18 & 19.	This session will cover a basic introduction to Interest rate, term structure, and term structure models with a brief discussion on the Ornstein-Uhlenbeck process and other pricing models using R	do
20.	Look back session

5. Evaluation

· Midterm Quiz 1 20%
· Midterm Quiz 2 20%
· Assignment/Project 10%
· Final term 40%
· Class participation 10%

6. Academic Integrity

· Students need to demonstrate a high order of academic integrity and discipline in the classroom/Via Zoom.
· Students are required to regularly read the reading materials inclusive of case studies and come prepared to class so that they contribute to the overall development of self and academia.
· Students should avoid using unfair practices during the evaluation process (Check details in the manual of policies).

Created By: Alora Kar on 09/01/2020 at 11:01 AM
Category: BM 19-21 T-V Doctype: Document