Theories of Interest Rates

Yeild Curve-   It Shows relationship between Return and Term structure.

THEORIES OF INTEREST RATES: vEXPECTATION THEORY- As per Expectation theory, if we are actually expecting the interest rates to fall they will actually fall leading to a downward sloping yield curve and if we are expecting the interest rates to rise they will increase leading to upward sloping yield curve. So what might be the driving forces behind these expectations? ·Political and Global Factors like Government policies (Demonetization, Introduction to GST) or BREXIT. ·Increase in level expenses i.e. Inflation 
Hence, Yield curve can be upward or downward sloping depends on Expectation
vLIQUIDITY PREFERNCE THEORY- Long term bonds are less liquid compared to short term bonds , So in order to compensate investors forthe higher liquidity risk involved in long term bonds, this should be awarded with higher returns
So, Long term bonds have higher return compared to short term bonds leading to upward sloping yield curve

Statistics Phase 6-: Skewness

SKEWNESS Skewness means lack of symmetry. Data can be skewed meaning that it tends to have a long tail on one side or the other. POSITIVE SKEWNESS Let’s take an example of the number of accident claims by an insurance customer in a sample of 50 people. NUMBER OF CLAIMS FREQUENCY 0 30 1 15 2 5 3 3 4 2 5 or more 1 TOTAL 50
Mean=8.33 Mode=0 Median=4 Here we can see that MEAN > MEDIAN After plotting, this looks like this

Statistics Phase 5 : Correlation Vs Causation

COVARIANCE Covariance is a measure of the relationship between 2 or more random variables or how 2 random variables vary together. Its similar to variance, but where variance tells you how a single variable varies, covariance tells you how 2 variables vary together. Positive covariance means as one variable increases the other one also increases. For example: lets’ take 2 variables height and weight. As heights increases weight also increases. Negative covariance means as one decreases, the other one also decreases. For example, as salary decreases, the expenditure also decreases. Measuring something in inches would be say 12 and converting the same into centimetres would be different only because of the unit change. So its hard to tell how strong the relationship is based on the actual magnitude of the covariance.
CORRELATION On one hand, covariance indicates the direction of the linear relationship between the variables whereas correlation on the other hand, indicates both the strength an…

Statistics Phase 4 -: Dilemma of Variation and Coefficient of Variation

VARIANCE Variance measures the dispersion/distance of a set of data points around their mean. It is the difference between the expected and the actual results such as that between budget and actual expenditure.

First lets talk about the numerator. It’s the square of the sum of difference between the observations and the mean. Closer the number to the mean, the lower the result will be and vice versa. The reasons why we do squaring are so that we do not obtain a negative variance because distance is never negative and to amplify the effect of large differences. The only difference in the formula is because of the denominator as we subtract 1 from the sample size in sample variance but not in population variance. Due to this, sample variance is always bigger than the population variance justifying the fact that there is more uncertainty about sample variance because we have randomly drawn a sample from a population. Let’s take an example of a stock market or other investm…

Statistics Phase 3 : Measures of Central Tendency

MEASURES OF CENTRAL TENDENCY When we get a lot of data then it becomes necessary to understand and analyse the data but it is very difficult to understand anything by looking at the huge amount of data. So there is a need to find a figure which is a representative of the whole data. Here we will look at 3 measures of central tendency which act as a representative of the data 1.Mean 2.Median 3.Mode Mean: It is the arithmetic average of the data. It is calculated by adding all observations and dividing by total no. of observations. It is the most widely used method of measurement of central tendency as it takes into account all the observations of data and is affected by all of them. But the biggest disadvantage of mean is that it is affected by extreme values. For eg if our data is 1,2,3,4,55,7,8,10. Then the mean of this data is 11.25. This is greater than 7 observations out of 8. So we can see that it is affected by extreme value 55 and is not measuring the correct central value. Median…

Statistics Phase 2 : Types of Data used in Statistics

Now as we have obtained our data so the next step is to determine the type of data so that appropriate analysis can be performed based on the type of data Data can be divided into 2 categories NUMERICAL DATA CATEGORICAL DATA
Numerical Data is a type of data which is available to us in the form of numbers and is quantitative.It can further be divided into 2 categories Discrete Data : It is a type of data which can only take a set of particular values. The set of values which the data can take are finite. For eg No. of claims arising in a given financial year. There is no restriction on the discrete data to take whole numbers it can have a set of values of negative integers and fractions
Continuous Data: it is a type of data in which the set of values which the data can take cannot be counted and it is infinite. Data can take any value within a given range. For eg exact weight of a person will lie in a range of (0kg,150kg) and exact age of a person will lie within (0yrs,130yrs) . Weight and a…

Statistics Phase 1 : Population Data vs Sample Data

We are starting with Descriptive Statistics . Discriptive Statistics is a branch of statistics that deals in determining the characteristics and featuresof the data provided.
The first question that needs to be answered is whether the data provided is the Population data or the Sample data.
Now what is Population data? Population is the collection of all items of interest of our study. For eg the collection of data of all residents of a country is the population data in the National Census Survey study.Now the size of this data will be very large and it will be very costly and time consuming to obtain this data and to analyse it. So to overcome these problems we make use of Sample data.

Sample data is a subset or a part of the Population data. We generally make use of the Sample data in our analysis because it’s size is less than the Population data and it’s collection and analysis is time and cost effective. For eg.  In our study of studying defects in …