Handbook of Modeling High-Frequency Data in Finance 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 which could deal with and research this knowledge. Handbook of Modeling High-Frequency facts in Finance addresses the various theoretical and sensible questions raised by way of the character and intrinsic homes of this data.

A one-stop compilation of empirical and analytical examine, this guide explores info sampled with high-frequency finance in monetary engineering, records, and the trendy monetary company enviornment. each bankruptcy makes use of real-world examples to give new, unique, and correct subject matters that relate to newly evolving discoveries in high-frequency finance, such as:

  • Designing new method to find elasticity and plasticity of fee 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 particular themes equivalent to probability dimension and administration, UHF facts, microstructure, dynamic multi-period optimization, loan facts versions, hybrid Monte Carlo, retirement, buying and selling platforms and forecasting, pricing, and boosting. the varied subject matters and viewpoints provided in each one bankruptcy make sure that readers are provided with a large remedy of functional methods.

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

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Extra info for Handbook of Modeling High-Frequency Data in Finance

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2, we present the basic methodology for detecting and evaluating the rare events. 3 details results obtained applying the methodology to tick data collected over a period of five trading days in April, 2008. 4 presents the distribution of the trades and the rare events during the trading day. 5 presents conclusions drawn using our methodology. 2 Methodology In this analysis, we use tick-by-tick data of 5369 equities traded on NYSE, NASDAQ, and AMEX for a five-day period. We need the most detailed possible dataset; however, since our discovery is limited to past trades we do not require the use of a more detailed level 2 order data.

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Furthermore, by using a simple translation in α and Vae , we may be able to map each surface into another. This translation is very important because once we decide on a optimal level for one class it automatically translates into optimal levels for the other classes. 2. 3. We also construct the corresponding surfaces in Fig. 5. Unlike the probability plots, the surfaces in Fig. 5 have different curvatures. For each class surface, we identify the α level which produces maximum return for each Vae .

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