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Current Directions in Biomedical Engineering 2020;6(3):20203124 Open Access. © 2019 Benedikt Szabo et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 License. Benedikt Szabo*, Calogero Gueli, Max Eickenscheidt and Thomas Stieglitz Polyimide-based Thin Film Conductors for High Frequency Data Transmission in Ultra- Conformable Implants Abstract: Application-specific integrated circuits (ASICs) embedded in polymers have been subject in implant manufacturing for the recent years. The increased functionality

. Barndorff-Nielsen, O. E., Shephard, N. (2002b), “Estimating Quadratic Variation Using Realized Variance”, Journal of Applied Econometrics, Vol. 17, No. 5, pp. 457-478. 16. Barndorff-Nielsen, O. E., Shephard, N. (2004), “Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics”, Econometrica, Vol. 72, No. 3, pp. 885-925. 17. Baruník, J., Křehlík, T. (2016), “Combining high frequency data with non-linear models for forecasting energy market volatility”, Expert Systems With Applications, Vol. 55, pp. 222

Studies in Nonlinear Dynamics & Econometrics Volume 15, Issue 1 2011 Article 3 Return-Volatility Relationship in High Frequency Data: Multiscale Horizon Dependency Jihyun Lee∗ Tong S. Kim† Hoe Kyung Lee‡ ∗Korea Development Bank, jihyun.lee07@gmail.com †Korea Advanced Institute of Science & Technology, tskim@business.kaist.ac.kr ‡Korea Advanced Institute of Science & Technology, hklee@business.kaist.ac.kr Return-Volatility Relationship in High Frequency Data: Multiscale Horizon Dependency Jihyun Lee, Tong S. Kim, and Hoe Kyung Lee Abstract This study investigates

12 High-Frequency Data and Models Prices recorded several times each hour generate large datasets. Several properties and applications of these high-frequency datasets are described in this chapter, for both equity and foreign exchange markets. Special attention is given to the more precise volatility estimates obtained from high-frequency return data. 12.1 Introduction High-frequency is the adjective used to indicate that prices are recorded more often than daily. The more prices a day, the higher is the frequency of the observations. Complete datasets contain

14 Dynamic Models for High-Frequency Data THE EXPANDING FINANCIAL MARKETS generate extremely large amounts of high-frequency data, which has become accessible for academic and com- mercial purposes. The availability of detailed information on trades and quotes is essentially due to the implementation of electronic trading sys- tems, such as CAC (Cotation Assistee en Continu) (Paris Bourse, Toronto Stock Exchange [TSE], Chicago), SETS (London Stock Exchange), Xetra (Deutsche Borse), and TSA (Amsterdam Stock Exchange). The electronic trading systems fulfill

Abstract

Market Volatility has been investigated at great lengths, but the measure of historical volatility, referred to as the relative volatility, is inconsistent. Using historical return data to calculate the volatility of a stock return provides a measure of the realized volatility. Realized volatility is often measured using some method of calculating a deviation from the mean of the returns for the stock price, the summation of squared returns, or the summation of absolute returns. We look to the stocks that make up the DJIA, using tick-by-tick data from June 2015 - May 2016. This research helps to address the question of what is the better measure of realized volatility? Several measures of volatility are used as proxies and are compared at four estimation time intervals. We review these measures to determine a closer/better fit estimator to the true realized volatility, using MSE, MAD, Diebold-Mariano test, and Pitman Closeness. We find that when using a standard deviation based on transaction level returns, shorter increments of time, while containing some levels of noise, are better estimates of volatility than longer increments.

1 Introduction The availability of high frequency intra-day data has motivated a growing literature devoted to measuring and forecasting volatility of asset returns. Several nonparametric estimators of daily volatility have been proposed, which allow to exploit the information contained in intra-day high frequency data neglecting microstructure effects, see Aït-Sahalia et al. (2005), Zhang, Mykland, and Aït-Sahalia (2005), Hansen and Lunde (2006), Bandi and Russel (2006a), Barndorff-Nielsen et al. (2008a), Jacod et al. (2009). The effectiveness of such estimators

Jahrbücher f. Nationalökonomie u. Statistik (Lucius & Lucius, Stuttgart 2007) Bd. (Vol.) 227/1 Abhandlungen / Original Papers Makroökonomische Nachrichten und die Reaktion des 15-Sekunden-DAX: Eine Ereignisstudie zurWirkung der ZEW-Konjunkturprognose Announcement of Business Cycle Forecasts and the Reaction of the German Stock Market Von Horst Entorf und Christian Steiner, Darmstadt∗ JEL G12, G14 Event study, announcement effect, high-frequency data, intraday data. Summary We study the response of the German stock market index DAX to the announcement of macro

1 Introduction Due to growing automation and developments in information technology sector, several financial markets around the world have set up intra-day databases pertaining to individual transactions, in terms of their price, volume, time of transaction etc. Such data are known as high frequency data (HFD). Availability of such data has necessitated the need for research and analysis for the deeper understanding of the market activity or in other words, the market micro structure. These data are generally recorded as and when they arise and hence they form

negative respectively. 3 Experimental Design 3.1 Datasets Seven weeks of high-frequency data including spot price, futures price and ETF price of Shanghai and Shenzhen 300 (CSI 300) index in China stock market (named S, F and E series) were studied in our research. They were collected over the period of 2016 October 10th to 2016 November 25th (35 days) where the time interval between observations was one minute. The futures series is created by using the nearest contract and switching to the second-nearest contract when the former one expires. Chinese stock market opens