Download Financial Econometrics: From Basics to Advanced Modeling by Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, PDF

By Svetlozar T. Rachev, Stefan Mittnik, Frank J. Fabozzi, Sergio M. Focardi, Teo Ja?ić

Monetary econometrics is a quest for versions that describe monetary time sequence equivalent to costs, returns, rates of interest, and trade charges. In monetary Econometrics, readers could be brought to this growing to be self-discipline and the options and theories linked to it, together with historical past fabric on likelihood idea and facts. The skilled writer workforce makes use of real-world information the place attainable and brings within the result of released study supplied by way of funding banking organisations and journals. monetary Econometrics essentially explains the thoughts offered and gives illustrative examples for the themes mentioned.

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Extra info for Financial Econometrics: From Basics to Advanced Modeling Techniques

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Investors can employ various econometric tools discussed in this book to derive the future expected return of an asset. Approaches to Portfolio Construction Based on the expected return for a portfolio (which depends on the expected returns of all the asset returns in the portfolio) and some risk measure of the portfolio’s return (which depends on the covariance of c01-FinEconoScope Page 22 Thursday, October 26, 2006 1:57 PM 22 FINANCIAL ECONOMETRICS returns between all pairs of assets in the portfolio) an efficient portfolio can be constructed.

The simplicity of the logical structure of probability theory might be deceptive. In fact, the practical difficulty of probability theory consists in the description of events. For instance, derivative contracts link in possibly complex ways the events of the underlying with the events of the derivative contract. Though the probabilistic “dynamics” of the underlying phenomena can be simple, expressing the links between all possible contingencies renders the subject mathematically complex. Probability theory is based on the possibility of assigning a precise uncertainty index to each event.

Given the number of available sample data at high frequency, we could write much more precise laws than those established using longer time intervals. Note that the need to compute solutions over forecasting horizons much longer than the time spacing is a general problem which applies at any time interval. For example, as will be discussed in Chapter 5, in asset allocation we need to understand the behavior of financial quantities over long time horizons. The question we need to ask is if models estimated using daily intervals can correctly capture the process dynamics over longer periods, such as years.

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