By Nikolaus Hautsch
The provision of monetary information recorded on high-frequency point has encouraged a examine quarter which over the past decade emerged to a tremendous region in econometrics and information. The transforming into approval for high-frequency econometrics is pushed by way of technological growth in buying and selling structures and an expanding value of intraday buying and selling, liquidity possibility, optimum order placement in addition to high-frequency volatility. This ebook presents a state-of-the artwork evaluation at the significant ways in high-frequency econometrics, together with univariate and multivariate autoregressive conditional suggest techniques for various sorts of high-frequency variables, intensity-based ways for monetary element techniques and dynamic issue versions. It discusses implementation info, offers insights into houses of high-frequency information in addition to institutional settings and offers functions to volatility and liquidity estimation, order ebook modelling and marketplace microstructure research.
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Extra info for Econometrics of Financial High-Frequency Data
Studied by Stoll (1978), Ho and Stoll (1981), Stoll (1989) and Huang and Stoll (1997), among others. 3 Major Implications for Trading Variables The main theoretical findings on the properties and determinants of key microstructure variables and relationships thereof are summarized as follows: 1. Transaction volumes: In the Easley and O’Hara (1987) model, traders are allowed to trade either small or large quantities, but are not allowed to refrain from trading. Thus, large quantities indicate the existence of information.
The trade price equals to the best bid (ask) and the best bid (ask) size is less than the previous one, or, 2. the best bid (ask) decreases (increases) between two consecutive records, then, match the trade with the corresponding order book entry. This step accounts for the possibility that trades might be executed against hidden liquidity. If for none of the order book records a match can be achieved in the given time window, the trade remains unmatched and we move to Step 3. Step 3: Round time matching.
Such recording errors are most easily identified if transaction prices or quotes show severe jumps between consecutive observations which are reverted immediately thereafter. To remove such types of errors, a set of filters, similar to those shown below, is commonly applied: 1. Delete observations which are directly indicated to be incorrect, delayed or subsequently corrected. 2. Delete entries outside the regular trading hours. 3. Delete entries with a quote or transaction price equal to zero or being negative.