scholarly journals Bitcoin at High Frequency

2019 ◽  
Vol 12 (1) ◽  
pp. 36 ◽  
Author(s):  
Leopoldo Catania ◽  
Mads Sandholdt
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Prediction Accuracy ◽  
Realized Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Forecast Horizon ◽  
Price Changes

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 of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.

scholarly journals Regime-Dependent Good and Bad Volatility of Bitcoin

2020 ◽  
Vol 13 (12) ◽  
pp. 312
Author(s):  
Kislay Kumar Jha ◽  
Dirk G. Baur
Keyword(s):  
Stock Markets ◽  
High Frequency ◽  
Realized Volatility ◽  
Forecast Horizon ◽  
Volatility Asymmetry ◽  
Volatility Regimes ◽  
The Asymmetry

This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

scholarly journals Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

2006 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Marcelo C. Carvalho ◽  
Marco Aurélio S. Freire ◽  
Marcelo Cunha Medeiros ◽  
Leonardo R. Souza
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Value At Risk ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Normality Assumption ◽  
Out Of Sample ◽  
Management Area

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

scholarly journals Investor Happiness and Predictability of the Realized Volatility of Oil Price

2020 ◽  
Vol 12 (10) ◽  
pp. 4309 ◽  
Author(s):  
Matteo Bonato ◽  
Konstantinos Gkillas ◽  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Potential Function ◽  
High Frequency ◽  
Realized Volatility ◽  
Oil Price ◽  
Financial Assets ◽  
Forecast Horizon ◽  
Full Sample ◽  
Out Of Sample ◽  
Intraday Data ◽  
Shed Light

We use the the heterogeneous autoregressive realized volatility (HAR-RV) model to analyze both in sample and out-of-sample whether a measure of investor happiness predicts the daily realized volatility of oil-price returns, where we use high-frequency intraday data to measure realized volatility. Full-sample estimates reveal that realized volatility is significantly negatively linked to investor happiness at a short forecast horizon. Similarly, out-of-sample results indicate that investor happiness significantly improves the accuracy of forecasts of realized volatility at a short forecast horizon. Results for a medium and a long forecast horizon are insignificant. We argue that our results shed light on the role played by speculation in oil products and the potential function of oil-related products as a hedge against risks in traditional financial assets.

scholarly journals Realized Measures to Explain Volatility Changes over Time

2020 ◽  
Vol 13 (6) ◽  
pp. 125
Author(s):  
Christos Floros ◽  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Athanasios Tsagkanos
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Leverage Effect ◽  
Frequency Data ◽  
Specific Group ◽  
Volatility Feedback ◽  
Changes Over Time ◽  
The Relationship ◽  
Over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data

Long memory models for daily and high frequency commodity futures returns

2007 ◽  
Vol 27 (7) ◽  
pp. 643-668 ◽  
Author(s):  
Richard T. Baillie ◽  
Young-Wook Han ◽  
Robert J. Myers ◽  
Jeongseok Song
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Memory Models ◽  
Commodity Futures

Excess Volatility in Bitcoin: Extreme Value Volatility Estimation

2021 ◽  
pp. 227797522098768
Author(s):  
Parthajit Kayal ◽  
G. Balasubramanian
Keyword(s):  
Investment Decisions ◽  
Extreme Value ◽  
Time Varying ◽  
Price Changes ◽  
Volatility Estimation ◽  
Policy Choices ◽  
Rolling Window ◽  
Excess Volatility ◽  
High Volatility ◽  
Over Time

This article investigates the excess volatility in Bitcoin prices using an unbiased extreme value volatility estimator. We capture the time-varying nature of the excess volatility using bootstrap, multi-horizon, sub-sampling and rolling-window approaches. We observe that Bitcoin price changes are almost efficient. Although Bitcoin prices exhibit high volatility and show signs of excess volatility for a few periods, it is decreasing over time. After controlling for the outliers, we also notice that the Bitcoin market shows signs of increasing maturity. Overall, Bitcoin prices show a sign of increasing efficiency with decreasing volatility. Our findings have implications for investors making investment decisions and for regulators making policy choices.

scholarly journals Frequency variations of gravity waves interacting with a time-varying tide

2013 ◽  
Vol 31 (10) ◽  
pp. 1731-1743 ◽  
Author(s):  
C. M. Huang ◽  
S. D. Zhang ◽  
F. Yi ◽  
K. M. Huang ◽  
Y. H. Zhang ◽  
...  
Keyword(s):  
Gravity Waves ◽  
High Frequency ◽  
Lower Atmosphere ◽  
Horizontal Wind ◽  
Frequency Variation ◽  
Time Varying ◽  
Transient Nature ◽  
Critical Layers ◽  
Favorable Conditions ◽  
Frequency Variations

Abstract. Using a nonlinear, 2-D time-dependent numerical model, we simulate the propagation of gravity waves (GWs) in a time-varying tide. Our simulations show that when a GW packet propagates in a time-varying tidal-wind environment, not only its intrinsic frequency but also its ground-based frequency would change significantly. The tidal horizontal-wind acceleration dominates the GW frequency variation. Positive (negative) accelerations induce frequency increases (decreases) with time. More interestingly, tidal-wind acceleration near the critical layers always causes the GW frequency to increase, which may partially explain the observations that high-frequency GW components are more dominant in the middle and upper atmosphere than in the lower atmosphere. The combination of the increased ground-based frequency of propagating GWs in a time-varying tidal-wind field and the transient nature of the critical layer induced by a time-varying tidal zonal wind creates favorable conditions for GWs to penetrate their originally expected critical layers. Consequently, GWs have an impact on the background atmosphere at much higher altitudes than expected, which indicates that the dynamical effects of tidal–GW interactions are more complicated than usually taken into account by GW parameterizations in global models.

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