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Journal articles on the topic 'High frequency volatility'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Catania, Leopoldo, and Mads Sandholdt. "Bitcoin at High Frequency." Journal of Risk and Financial Management 12, no. 1 (2019): 36. http://dx.doi.org/10.3390/jrfm12010036.

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This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility o

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2

Matei, Rovira, and Agell. "Bivariate Volatility Modeling with High-Frequency Data." Econometrics 7, no. 3 (2019): 41. http://dx.doi.org/10.3390/econometrics7030041.

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We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate

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3

Sanfelici, Simona, Imma Valentina Curato, and Maria Elvira Mancino. "High-frequency volatility of volatility estimation free from spot volatility estimates." Quantitative Finance 15, no. 8 (2015): 1331–45. http://dx.doi.org/10.1080/14697688.2015.1032542.

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4

Corsi, Fulvio, Gilles Zumbach, Ulrich A. Muller, and Michel M. Dacorogna. "Consistent High-precision Volatility from High-frequency Data." Economic Notes 30, no. 2 (2001): 183–204. http://dx.doi.org/10.1111/j.0391-5026.2001.00053.x.

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5

Wright, Jonathan H., and Tim Bollerslev. "High Frequency Data, Frequency Domain Inference and Volatility Forecasting." International Finance Discussion Paper 1999, no. 649 (1999): 1–27. http://dx.doi.org/10.17016/ifdp.1999.649.

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6

Bollerslev, Tim, and Jonathan H. Wright. "High-Frequency Data, Frequency Domain Inference, and Volatility Forecasting." Review of Economics and Statistics 83, no. 4 (2001): 596–602. http://dx.doi.org/10.1162/003465301753237687.

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7

Alan, Chow, and Lahtinen Kyre. "Equity Risk: Measuring Return Volatility Using Historical High-Frequency Data." Studies in Business and Economics 14, no. 3 (2019): 60–71. http://dx.doi.org/10.2478/sbe-2019-0043.

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AbstractMarket 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 researc

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8

Fan, Jianqing, and Yazhen Wang. "Spot volatility estimation for high-frequency data." Statistics and Its Interface 1, no. 2 (2008): 279–88. http://dx.doi.org/10.4310/sii.2008.v1.n2.a5.

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9

Lee, G. J., and Sun Young Hwang. "Multivariate volatility for high-frequency financial series." Korean Journal of Applied Statistics 30, no. 1 (2017): 169–80. http://dx.doi.org/10.5351/kjas.2017.30.1.169.

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10

H. Vo, Long. "Estimating Financial Volatility with High-Frequency Returns." Journal of Finance & Economics Research 2, no. 2 (2017): 84–111. http://dx.doi.org/10.20547/jfer1702201.

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11

Sheppard, Kevin, and Wen Xu. "Factor High-Frequency-Based Volatility (HEAVY) Models*." Journal of Financial Econometrics 17, no. 1 (2018): 33–65. http://dx.doi.org/10.1093/jjfinec/nby028.

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12

Wang, Yazhen, and Jian Zou. "Volatility analysis in high-frequency financial data." Wiley Interdisciplinary Reviews: Computational Statistics 6, no. 6 (2014): 393–404. http://dx.doi.org/10.1002/wics.1330.

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13

Taylor, Nicholas. "Market and idiosyncratic volatility: high frequency dynamics." Applied Financial Economics 20, no. 9 (2010): 739–51. http://dx.doi.org/10.1080/09603100903459923.

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14

Noureldin, Diaa, Neil Shephard, and Kevin Sheppard. "Multivariate high-frequency-based volatility (HEAVY) models." Journal of Applied Econometrics 27, no. 6 (2011): 907–33. http://dx.doi.org/10.1002/jae.1260.

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15

BARUCCI, EMILIO, and MARIA ELVIRA MANCINO. "COMPUTATION OF VOLATILITY IN STOCHASTIC VOLATILITY MODELS WITH HIGH FREQUENCY DATA." International Journal of Theoretical and Applied Finance 13, no. 05 (2010): 767–87. http://dx.doi.org/10.1142/s0219024910005991.

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We consider general stochastic volatility models driven by continuous Brownian semimartingales, we show that the volatility of the variance and the leverage component (covariance between the asset price and the variance) can be reconstructed pathwise by exploiting Fourier analysis from the observation of the asset price. Specifying parametrically the asset price model we show that the method allows us to compute the parameters of the model. We provide a Monte Carlo experiment to recover the volatility and correlation parameters of the Heston model.

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16

Dufour, J. M., R. Garcia, and A. Taamouti. "Measuring High-Frequency Causality Between Returns, Realized Volatility, and Implied Volatility." Journal of Financial Econometrics 10, no. 1 (2011): 124–63. http://dx.doi.org/10.1093/jjfinec/nbr007.

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17

Wahyu Santosa, Perdana. "Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia." Investment Management and Financial Innovations 17, no. 1 (2020): 175–87. http://dx.doi.org/10.21511/imfi.17(1).2020.16.

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This article analyzes whether the factors of the mechanism of high-frequency trading (HFT) or intraday trading affect the process of price reversal and continuation. The price reversal phenomenon is gaining importance rapidly due to the increasingly intensive use of IT/Fintech-based trading automation facilities on the Indonesia Stock Exchange. However, one knows little about how their trading affects volatility and liquidity pressures that cause price reversals. A new research approach uses the factors of market microstructure mechanism based on high-frequency data (HFD-intraday). The researc

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18

Chen, Jiangrui, Lianqian Yin, Sizhe Hou, Wei Zhang, Xiaojie Liu, and Haoting Li. "Jump Volatility Estimates of High Frequency Data and Analysis Based on HHT." International Journal of Economics and Finance 7, no. 11 (2015): 242. http://dx.doi.org/10.5539/ijef.v7n11p242.

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<p>As the global financial market risk increases, countries stress more onthe management and prevention of financial risks. These financial risks come from the volatility of the market, and thus we can build more comprehensive understanding of financial markets by analyzing the composition and the law of the financial volatility in different frequency. Based on Hilbert Huang Transform, the realized volatility analysis model is establishedto decompose the volatility into various signal in dissimilar frequency. First of all, the realized leap volatility is obtained through the previous res

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19

Chin, Wen Cheong, and Min Cherng Lee. "S&P500 volatility analysis using high-frequency multipower variation volatility proxies." Empirical Economics 54, no. 3 (2017): 1297–318. http://dx.doi.org/10.1007/s00181-017-1345-z.

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20

Degiannakis, Stavros, and Christos Floros. "Modeling CAC40 volatility using ultra-high frequency data." Research in International Business and Finance 28 (May 2013): 68–81. http://dx.doi.org/10.1016/j.ribaf.2012.09.001.

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21

Ysusi, Carla. "Estimating integrated volatility using absolute high-frequency returns." International Journal of Monetary Economics and Finance 1, no. 2 (2008): 177. http://dx.doi.org/10.1504/ijmef.2008.019221.

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22

Nava, Noemi, T. Di Matteo, and Tomaso Aste. "Anomalous volatility scaling in high frequency financial data." Physica A: Statistical Mechanics and its Applications 447 (April 2016): 434–45. http://dx.doi.org/10.1016/j.physa.2015.12.022.

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23

Mansor, Mahayaudin M., David A. Green, and Andrew V. Metcalfe. "Directionality and volatility in high-frequency time series." High Frequency 1, no. 2 (2018): 70–86. http://dx.doi.org/10.1002/hf2.10008.

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24

Zu, Yang, and H. Peter Boswijk. "Estimating spot volatility with high-frequency financial data." Journal of Econometrics 181, no. 2 (2014): 117–35. http://dx.doi.org/10.1016/j.jeconom.2014.04.001.

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25

Katsiampa, Paraskevi, Shaen Corbet, and Brian Lucey. "High frequency volatility co-movements in cryptocurrency markets." Journal of International Financial Markets, Institutions and Money 62 (September 2019): 35–52. http://dx.doi.org/10.1016/j.intfin.2019.05.003.

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26

Sensoy, Ahmet, Thiago Christiano Silva, Shaen Corbet, and Benjamin Miranda Tabak. "High-frequency return and volatility spillovers among cryptocurrencies." Applied Economics 53, no. 37 (2021): 4310–28. http://dx.doi.org/10.1080/00036846.2021.1899119.

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27

Shi, Weihua, and Cheng-Few Lee. "Volatility Persistence of High-Frequency Returns in the Japanese Government Bond Futures Market." Review of Pacific Basin Financial Markets and Policies 11, no. 04 (2008): 511–30. http://dx.doi.org/10.1142/s0219091508001453.

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The availability of a two-year high-frequency transaction data of the Japanese Government Bond (JGB) futures provides us with an opportunity to uncovering volatility persistence in high-frequency returns and testing the mixed-distribution-hypothesis (MDH) in this market. Both time-domain and frequency domain methods show that the degrees of volatility persistence are very similar across various frequencies, which supports the MDH. The result also shows that the method of filtering out the intraday pattern annihilates the complex interaction of the intraday periodicity and the volatility persis

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28

Christoffersen, Peter, Bruno Feunou, Kris Jacobs, and Nour Meddahi. "The Economic Value of Realized Volatility: Using High-Frequency Returns for Option Valuation." Journal of Financial and Quantitative Analysis 49, no. 3 (2014): 663–97. http://dx.doi.org/10.1017/s0022109014000428.

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AbstractMany studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so, we develop a new class of affine discrete-time option valuation models that use daily returns as well as realized volatility. We derive convenient closed-form option valuation formulas, and we assess the option valuation properties using Standard & Poor’s (S&P) 500 return and option data.

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29

Yoon, J. E., and S. Y. Hwang. "Choice of weights in a hybrid volatility based on high-frequency realized volatility." Korean Journal of Applied Statistics 29, no. 3 (2016): 505–12. http://dx.doi.org/10.5351/kjas.2016.29.3.505.

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30

Martens, Martin, and Jason Zein. "Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility." Journal of Futures Markets 24, no. 11 (2004): 1005–28. http://dx.doi.org/10.1002/fut.20126.

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31

Petrov, Vladimir, Anton Golub, and Richard Olsen. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time." Journal of Risk and Financial Management 12, no. 2 (2019): 54. http://dx.doi.org/10.3390/jrfm12020054.

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We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates, as well as a stock market index. We demonstrate the long memory of instantaneou

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32

KREMER, MARCEL, FRED ESPEN BENTH, BJÖRN FELTEN, and RÜDIGER KIESEL. "VOLATILITY AND LIQUIDITY ON HIGH-FREQUENCY ELECTRICITY FUTURES MARKETS: EMPIRICAL ANALYSIS AND STOCHASTIC MODELING." International Journal of Theoretical and Applied Finance 23, no. 04 (2020): 2050027. http://dx.doi.org/10.1142/s0219024920500272.

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This paper investigates the relationship between volatility and liquidity on the German electricity futures market based on high-frequency intraday prices. We estimate volatility by the time-weighted realized variance acknowledging that empirical intraday prices are not equally spaced in time. Empirical evidence suggests that volatility of electricity futures decreases as time approaches maturity, while coincidently liquidity increases. Established continuous-time stochastic models for electricity futures prices involve a growing volatility function in time and are thus not able to capture our

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33

Hanson, Thomas A. "High frequency traders in a simulated market." Review of Accounting and Finance 15, no. 3 (2016): 329–51. http://dx.doi.org/10.1108/raf-02-2015-0023.

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Purpose An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes. Design/methodology/approach A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simu

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34

Yan, Mu, Yuan Huiling, and Zhou Yong. "High-dimensional integrated volatility matrix estimation for high-frequency financial data." SCIENTIA SINICA Mathematica 48, no. 2 (2018): 319. http://dx.doi.org/10.1360/n012016-00047.

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35

Fan, Jianqing, and Donggyu Kim. "Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model." Journal of the American Statistical Association 113, no. 523 (2018): 1268–83. http://dx.doi.org/10.1080/01621459.2017.1340888.

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36

Jin, M. K., J. E. Yoon, and S. Y. Hwang. "Choice of frequency via principal component in high-frequency multivariate volatility models." Korean Journal of Applied Statistics 30, no. 5 (2017): 747–57. http://dx.doi.org/10.5351/kjas.2017.30.5.747.

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37

Tao, Minjing, Yazhen Wang, Qiwei Yao, and Jian Zou. "Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches." Journal of the American Statistical Association 106, no. 495 (2011): 1025–40. http://dx.doi.org/10.1198/jasa.2011.tm10276.

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38

Zhou, Bin. "High-Frequency Data and Volatility in Foreign-Exchange Rates." Journal of Business & Economic Statistics 14, no. 1 (1996): 45. http://dx.doi.org/10.2307/1392098.

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39

Virgilio, Gianluca. "The Impact of High-Frequency Trading on Market Volatility." Journal of Trading 11, no. 2 (2016): 55–63. http://dx.doi.org/10.3905/jot.2016.11.2.055.

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40

Griffith, Todd G., Bonnie F. Van Ness, and Robert A. Van Ness. "High-Frequency Trading Patterns around Short-Term Volatility Spikes." Journal of Trading 12, no. 3 (2017): 48–68. http://dx.doi.org/10.3905/jot.2017.12.3.048.

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41

Bibinger, Markus, and Mathias Trabs. "Volatility estimation for stochastic PDEs using high-frequency observations." Stochastic Processes and their Applications 130, no. 5 (2020): 3005–52. http://dx.doi.org/10.1016/j.spa.2019.09.002.

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42

Zhou, Bin. "High-Frequency Data and Volatility in Foreign-Exchange Rates." Journal of Business & Economic Statistics 14, no. 1 (1996): 45–52. http://dx.doi.org/10.1080/07350015.1996.10524628.

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43

Wang, Yazhen, and Jian Zou. "Vast volatility matrix estimation for high-frequency financial data." Annals of Statistics 38, no. 2 (2010): 943–78. http://dx.doi.org/10.1214/09-aos730.

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44

Xu, Jing, and Susheng Wang. "High frequency volatility forecasting considering jump and persistent leverage." Journal of Statistics and Management Systems 20, no. 2 (2017): 275–96. http://dx.doi.org/10.1080/09720510.2016.1249088.

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45

Bollerslev, T. "Leverage and Volatility Feedback Effects in High-Frequency Data." Journal of Financial Econometrics 4, no. 3 (2006): 353–84. http://dx.doi.org/10.1093/jjfinec/nbj014.

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46

Liu, Fei, Athanasios A. Pantelous, and Hans-Jörg von Mettenheim. "Forecasting and trading high frequency volatility on large indices." Quantitative Finance 18, no. 5 (2018): 737–48. http://dx.doi.org/10.1080/14697688.2017.1414489.

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47

Shi, Yanlin, and Kin-Yip Ho. "Modeling high-frequency volatility with three-state FIGARCH models." Economic Modelling 51 (December 2015): 473–83. http://dx.doi.org/10.1016/j.econmod.2015.09.008.

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48

Hua, Jian, and Sebastiano Manzan. "Forecasting the return distribution using high-frequency volatility measures." Journal of Banking & Finance 37, no. 11 (2013): 4381–403. http://dx.doi.org/10.1016/j.jbankfin.2013.08.002.

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49

Aït-Sahalia, Yacine, Per A. Mykland, and Lan Zhang. "Ultra high frequency volatility estimation with dependent microstructure noise." Journal of Econometrics 160, no. 1 (2011): 160–75. http://dx.doi.org/10.1016/j.jeconom.2010.03.028.

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50

LEE, WO-CHIANG. "FORECASTING HIGH-FREQUENCY FINANCIAL DATA VOLATILITY VIA NONPARAMETRIC ALGORITHMS: EVIDENCE FROM TAIWAN'S FINANCIAL MARKETS." New Mathematics and Natural Computation 02, no. 03 (2006): 345–59. http://dx.doi.org/10.1142/s1793005706000543.

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This paper uses two computational intelligence algorithms, namely, artificial neural networks (ANN) and genetic programming (GP), for forecasting the volatility of high-frequency TAIEX financial data with four different horizons and compares the out-sample forecasting performance with the GARCH(1,1), EGRACH(1,1) and GJR-GARCH(1,1) models. Based on intraday integrated volatility, the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Theil's U and the VaR backtest are used as performance indexes. Our empirical results reveal that the GP and ANN perform r

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