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MA-323_Monte_Carlo_Simulation

Submissions made for IIT Guwahati Winter Semester (2020) MA 323 course - Monte Carlo Simulation

Monte Carlo Simulation

This repository contains all my submissions to assignments completed during study of the course MA 323: Monte Carlo Simulation, taken in Autumn 2020 at IIT Guwahati.

All the details can be find in the respective folders. Following is a brief summary of the assignments:

Lab - 1

This lab covered about general linear congruence generator, and Spectral Test for the random numbers.

Lab - 2

This lab was based on using special kind of random number generators, and introduction to the Sampling Method with Inverse Transform Method.

Lab - 3

This lab continued with the concepts of Sampling Method by using Acceptance Rejection Method, and using these methods to both discrete and continuous distributions.

Lab - 4

This lab focused on a special kind of distribution, Beta Distribution, and how to use Acceptance Rejection Method on such distributions.

Lab - 5

This lab was aimed at computational comparsion of 2 different methods to generate normal random numbers - Box-Muller method, and Marsaglia and Bray Method.

Lab - 6

This lab continued with the generation of normal random numbers, but this time the distribution was multivariate normal distribution.

Lab - 7

The aim of this lab was to find out the expected value of the stock prices using Geometric Brownian Motion (gBm) model.

Lab - 8

It was the continuation of lab 7, but this time the model used was Merton's Jump Diffusion model.

Lab - 9

This lab discussed about the idea of using Control Variates to determine the option prices based on BSM framework, to achieve variance reduction.

Lab - 10

This lab was continuation of previous lab, but dealt with the concept of Anithetic Variates for variance reduction.

Lab - 11

This lab was aimed at finding the Discrepancy of the random numbers generated using general LCG.

Lab - 12

The last lab focused on generating low discrepancy sequences for one dimension (Van der Corput sequences), and d-dimension (Halton Sequences). Further their sample distribution was compared with that of LCG used in previous labs.

LICENSE

This project is licensed under the MIT License.

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