Summary

For the sake of understanding, just imagine the example of the prices of stocks in the share market, they are highly volatile and keep on changing every day, hour, minutes and so on. So to have precise knowledge of these prices one needs to have large quantum of data, in tens of thousands or in millions… - Subject: Business
- Type: Essay
- Level: Masters
- Pages: 10 (2500 words)
- Downloads: 1
- Author: julianullrich

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Econometrics is the application of statistical methods for solving the financial issues. It has many applications like – the effect of the economic conditions on the financial markets, the asset price derivations, predicting the future financial variables and other financial decision-makings. In econometrics there is a lack of adequate test data for applying the particular methodology, this is termed as the small samples problem. There are further constraints in Econometrics with respect to data revisions and the measurement error. These problems are generally faced due to the subsequent revisions in the reference data and the incorrect data estimation or incorrect measurement of data. The frequency of observation of the financial data has far-reaching implications. For the sake of understanding, just imagine the example of the prices of stocks in the share market, they are highly volatile and keep on changing every day, hour, minutes and so on. So to have precise knowledge of these prices one needs to have large quantum of data, in tens of thousands or in millions. Financial data are very noisy in the sense that it is highly difficult to draw a certain pattern or trend from the available data. In other sense the data doesn’t have a specific distribution. But approximations are applied for modeling of the market and for analyzing the future trends, values of financial variables....

sections, e.g. the weekly prices of mid cap shares over the period of five years.

Cointergration: The macroeconomics and financial economics has empirical research

based on time series. The macroeconomic time series has a nonstationarity property,

which means that the variable doesn't return to a constant value or a linear trend. The

stationary processes has a basic tendency of moving around a linear value i.e. the mean

value and its fluctuation from this value is termed as the deviation. The variables such as

employment, asset prices, gross domestic product follow a nonstationarity property and

possess stochastic trends.

Consider the trend in the financial return series like the rate of change of daily exchange

rate. The figure shows the volatility of returns.

Fig.1

Earlier it was a general practice to estimate nonstationary process equations in

macroeconomic models by the simple linear regression.

Clive Granger (1981) proposed a solution to the time series by a simple regression

equation:

(1)

where,

= dependent variable

= single exogenous regressor

= white noise

To stress the solution, Granger defined the degree of integaration of the variable. Suppose

a variable can be made nearly stationary by differencing it d times, then it can be

termed as integrated of order d or I(d). Stationary random variables are I(0).

In equation (1), if I(1) and I(1), then I(1). But there exists an

important exception, if I(0) then I(0). The linear combination,

holds same statistical properties as an I(0) variable. This ...Download file to see next pagesRead More

sections, e.g. the weekly prices of mid cap shares over the period of five years.

Cointergration: The macroeconomics and financial economics has empirical research

based on time series. The macroeconomic time series has a nonstationarity property,

which means that the variable doesn't return to a constant value or a linear trend. The

stationary processes has a basic tendency of moving around a linear value i.e. the mean

value and its fluctuation from this value is termed as the deviation. The variables such as

employment, asset prices, gross domestic product follow a nonstationarity property and

possess stochastic trends.

Consider the trend in the financial return series like the rate of change of daily exchange

rate. The figure shows the volatility of returns.

Fig.1

Earlier it was a general practice to estimate nonstationary process equations in

macroeconomic models by the simple linear regression.

Clive Granger (1981) proposed a solution to the time series by a simple regression

equation:

(1)

where,

= dependent variable

= single exogenous regressor

= white noise

To stress the solution, Granger defined the degree of integaration of the variable. Suppose

a variable can be made nearly stationary by differencing it d times, then it can be

termed as integrated of order d or I(d). Stationary random variables are I(0).

In equation (1), if I(1) and I(1), then I(1). But there exists an

important exception, if I(0) then I(0). The linear combination,

holds same statistical properties as an I(0) variable. This ...Download file to see next pagesRead More

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