Handbook of Modeling High-Frequency Data in Finance (Wiley by Frederi G. Viens, Maria C. Mariani, Ionut Florescu

By Frederi G. Viens, Maria C. Mariani, Ionut Florescu


In contemporary years, the supply of high-frequency information and advances in computing have allowed monetary practitioners to layout platforms that may deal with and research this data. Handbook of Modeling High-Frequency information in Finance addresses the numerous theoretical and sensible questions raised through the character and intrinsic houses of this data.

A one-stop compilation of empirical and analytical examine, this instruction manual explores information sampled with high-frequency finance in monetary engineering, facts, and the trendy monetary enterprise enviornment. each bankruptcy makes use of real-world examples to give new, unique, and appropriate issues that relate to newly evolving discoveries in high-frequency finance, such as:

  • Designing new method to find elasticity and plasticity of rate evolution

  • Constructing microstructure simulation models

  • Calculation of choice costs within the presence of jumps and transaction costs

  • Using boosting for monetary research and trading

The instruction manual motivates practitioners to use high-frequency finance to real-world occasions via together with unique issues similar to probability size and administration, UHF information, microstructure, dynamic multi-period optimization, loan facts versions, hybrid Monte Carlo, retirement, buying and selling platforms and forecasting, pricing, and boosting. the varied issues and viewpoints provided in every one bankruptcy make sure that readers are provided with a large remedy of functional methods.

Handbook of Modeling High-Frequency information in Finance is an important reference for lecturers and practitioners in finance, enterprise, and econometrics who paintings with high-frequency info of their daily paintings. It additionally serves as a complement for chance administration and high-frequency finance classes on the upper-undergraduate and graduate levels.

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Additional info for Handbook of Modeling High-Frequency Data in Finance (Wiley Handbooks in Financial Engineering and Econometrics)

Example text

A. a. a. a. a. a. 5 Expected return surfaces for stock classes. 5 for each of the quantile levels considered. 5. The x axis is the proportion of after-event window size with respect to the before-event window size, and lines of the same color represent the three original window sizes chosen (dark grey for V0 = 3000, grey for V0 = 5000, and light grey for V0 = 10,000). (a) Small-cap stocks, (b) medium-cap stocks, (c) large-cap stocks, and (d) super equity. 5 for each of the quantile levels considered.

The skewness of the distributions decreases as V0 increases and it is more significant for small and mid-vol stocks. This is easy to explain since this type of equities are more rarely traded and it takes a longer time period to detect the changes in equity using a 10,000 shares window versus a 3000 shares window. Third, we remark the presence of a peak in the distribution of rare events during the day after about 30 min of trading across all classes. This does correspond to the previously observed peak in trading activity (Fig.

1a and no observation in Fig. 1b. 015 no matter how small will indeed select the observations corresponding to x = −4 for the distribution in Fig. 1a, however, for the distribution in Fig. 1b will select all the observations at x = −3 and x = 3. Therefore, using a traditional quantile rule would force us to analyze points from distributions which lack extreme observations. Note: Using rule (Eq. 1) with returns instead of change in price will be preferable in a trading environment. We use change in price ( p) for clarity of exposition.

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