Computing Value at Risk Using Descriptive Statistics

Computing Value-at-Risk using

Statistical tools

And Descriptive statistics

Presented by:

• Rupal Mishra

Table of contents:

OBJECTIVE:

In the current project, we are computing value at risk for two securities. Both the securities are chosen from different industries and we are trying to understand there correlation. Value at Risk has been calculated using Normal Distribution curve drawn using one year data (From 1st April 2013 to 29th March 2014) of both securities.

We are also using descriptive statistic techniques to figure out the variability and average returns of both the stocks. The riskiness of stock will help us identify, which stock is better to invest for a risky investor and which one is better for risk averse investor.

At the end, we will be taking our decision based on value at risk computed and descriptive statistics done to find out, which stock has performed better and what does correlation between two suggests.

INTRODUCTION:

The project will be covered in two parts:

1. In the first part, we will use descriptive statistics techniques to find out the to analyze the stocks selected.

1. In the second part, we will do regression analysis of the stock with respect to indices and will use normal distribution probability curve to find out the value-at-risk  

For our reference, we have chosen one automobile stock- TATA MOTORS and one oil stock-(ONGC), to analyze. As a general perception, we should think that as both the stocks are from opposing sectors, their correlation should be negative and adding an oil sector stock in automobile sector portfolio, should reduce the risk.At the end of project, we will prove through out statistical analysis of stocks over the year that they have not behaved in the similar fashion as we expected. This could be due to various other market factors.

The price data for those stocks have been taken from here:

https://in.finance.yahoo.com/q/hp?s=TATAMOTORS.BO

DESCRIPTIVE STATISTICS:

Following descriptive statistics data has been computed in Excel for Tata Motors stock. All data is with respect to returns for each stock.

Here is the descriptive statistics analysis for ONGC stock:

INFERENCES

1. The mean returns of TATA MOTORS stock is more than ONGC, hence we can assume that returns from TATA Motors are more than ONGC for an investor.

1. The Standard deviation of TATA MOTORS stock is more than that of ONGC, though comparable, from which we can predict that both stocks are almost same in riskiness.

PROBABILITY DISTRIBUTION

TATA Motors

[pic 1]

Data from table in page 9

Frequency Distribution Table of TATA Motors: