Read e-book online The Basics of Financial Econometrics: Tools, Concepts, and PDF

By Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev, Bala G. Arshanapalli, Markus Hoechstoetter

An available consultant to the growing to be box of economic econometrics
As finance and fiscal items became extra advanced, monetary econometrics has emerged as a fast-growing box and invaluable beginning for someone interested in quantitative finance. The strategies of economic econometrics facilitate the improvement and administration of recent monetary tools via offering types for pricing and hazard overview. briefly, monetary econometrics is an integral portion of sleek finance.

The fundamentals of monetary Econometrics covers the widely used strategies within the box with out utilizing pointless mathematical/statistical research. It makes a speciality of foundational rules and the way they're utilized. subject matters coated contain: regression types, issue research, volatility estimations, and time sequence suggestions. furthermore, an linked web site encompasses a variety of real-world case reviews on the topic of very important concerns during this quarter.

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Extra resources for The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications (Frank J. Fabozzi Series)

Example text

This time span includes 96 observations. To illustrate the linear dependence between the index and individual stocks, we take the monthly stock returns of an individual stock, General Electric (GE), covering the same period. 1. 7125 using the formula shown in Appendix A. This indicates a fairly strong correlation in the same direction between the stock index and GE. So, we can expect with some certainty that GE’s stock moves in the same direction as the index. Typically, there is a positive correlation between stock price movement and a stock index.

Second, the distributions of all errors are the same. Consequently, for each pair (x,y), an error or residual term assumes some value independently of the other residuals in a fashion common to all the other errors, under equivalent circumstances. d. 1) are purely random. In other words, the residuals are statistical noise such that they cannot be predicted from other quantities. d. requirement, then something would appear to be wrong with the model. Moreover, in that case, a lot of estimation results would be faulty.

Thus, the linear regression makes little sense. If R2 = 1, the fit of the line is perfect. All of the variation in y is explained by the variation in x. 7 As an example, we use the monthly return data from the previous example. 2590. 5259). 3432. 5867 for daily. Relationship between Coefficient of Determination and Correlation Coefficient Further analysis of the R2 reveals that the coefficient of determination is just the squared correlation coefficient, rx,y, of x and y. The consequence of this equality is that the correlation between x and y is reflected by the goodnessof-fit of the linear regression.

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The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications (Frank J. Fabozzi Series) by Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev, Bala G. Arshanapalli, Markus Hoechstoetter


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