scholarly journals Using High-Frequency Entropy to Forecast Bitcoin’s Daily Value at Risk

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 102 ◽  
Author(s):  
Daniel Pele ◽  
Miruna Mazurencu-Marinescu-Pele
Keyword(s):  
At Risk ◽  
Time Series ◽  
High Frequency ◽  
Value At Risk ◽  
Explanatory Power ◽  
High Resolution Data ◽  
Symbolic Time Series Analysis ◽  
Daily Returns ◽  
Resolution Data ◽  
Daily Time

In this paper we investigate the ability of several econometrical models to forecast value at risk for a sample of daily time series of cryptocurrency returns. Using high frequency data for Bitcoin, we estimate the entropy of intraday distribution of logreturns through the symbolic time series analysis (STSA), producing low-resolution data from high-resolution data. Our results show that entropy has a strong explanatory power for the quantiles of the distribution of the daily returns. Based on Christoffersen’s tests for Value at Risk (VaR) backtesting, we can conclude that the VaR forecast build upon the entropy of intraday returns is the best, compared to the forecasts provided by the classical GARCH models.

scholarly journals Smoothing methods for histogram-valued time series: an application to value-at-risk

2011 ◽  
Vol 4 (2) ◽  
pp. 216-228 ◽  
Author(s):  
Javier Arroyo ◽  
Gloria González-Rivera ◽  
Carlos Maté ◽  
Antonio Muñoz San Roque
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
Smoothing Methods

Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

2017 ◽  
Vol 67 ◽  
pp. 355-367 ◽  
Author(s):  
Heng-Guo Zhang ◽  
Chi-Wei Su ◽  
Yan Song ◽  
Shuqi Qiu ◽  
Ran Xiao ◽  
...  
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
High Dimensional ◽  
Mapping Model ◽  
Random Mapping

VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS

2006 ◽  
Vol 16 (05) ◽  
pp. 371-382 ◽  
Author(s):  
EDMOND H. C. WU ◽  
PHILIP L. H. YU ◽  
W. K. LI
Keyword(s):  
At Risk ◽  
Time Series ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Risk Estimation ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Garch Models ◽  
Computationally Efficient

We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

scholarly journals Effects of asset frequency components on value-at-risk in emerging and developed markets

2020 ◽  
Vol 40 (1) ◽  
pp. 145
Author(s):  
Milton Biage ◽  
Pierre Joseph Nelcide
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Financial Assets ◽  
Back Testing ◽  
Window Technique ◽  
Rolling Window ◽  
Frequency Components ◽  
Daily Stock Returns ◽  
Daily Returns

<p>Value-at-Risk was estimated using the technique of wavelet decomposition with goal to analyze the frequency components' impacts on variances of daily stock returns, and on  forecasts. Daily returns of twenty-one shares of the Ibovespa and daily returns of twenty-two shares of the DJIA were used. The  model was applied to the reconstructed returns to model and establish the prediction of conditional variance, applying the rolling window technique. The Value-at-Risk was then estimated, and the results showed that the DJIA shares showed more efficient market behavior than those of Ibovespa. The differences in behavior induces to affirm that VaRs, used in the analysis of financial assets from different markets with different governance premises, should be estimated by series of returns reconstructed by aggregations of components of different frequencies. A set of back-testing was applied to confront the estimated , which demonstrated that the estimation of  models are consistent.</p>

scholarly journals Mode Determination by Fourier Analysis of Line-Profile Variations: Application to τ Peg

1993 ◽  
Vol 139 ◽  
pp. 147-147
Author(s):  
E.J. Kennelly ◽  
G.A.H. Walker ◽  
W.J. Merryfield ◽  
J.M. Matthews
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Line Profiles ◽  
High Resolution Data ◽  
Low Degree ◽  
Scuti Star ◽  
High Degree ◽  
Scuti Stars ◽  
Resolution Data ◽  
Good Agreement

AbstractThe identification of modes of oscillation is an important first step towards the seismology of stars. Low- and high-degree nonradial modes of oscillation may appear as variations in the line profiles of rapidly rotating δ Scuti stars. We present a technique whereby complex patterns in the line profiles are decomposed into Fourier components in both time and “Doppler space”. The technique is applied to the 7.3-hour time series of high-resolution data obtained from CFHT for the δ Scuti star τ Peg. In addition to the low-degree mode which has been identified in photometric studies (Breger 1991), we find evidence for at least three high-degree modes near 11 and 15. Correcting for the rotation of the star, most of these modes appear to oscillate with frequencies near 17 cycles day-1. Our results are found to be in good agreement with the theoretical limits imposed on the frequencies of oscillation by the models of Dziembowski (1990).

Time series modeling for risk of stock price with value at risk computation

2015 ◽  
Vol 9 ◽  
pp. 2779-2787 ◽  
Author(s):  
Dodi Devianto ◽  
Maiyastri ◽  
Dian Rezki Fadhilla
Keyword(s):  
At Risk ◽  
Time Series ◽  
Value At Risk ◽  
Stock Price ◽  
Time Series Modeling

scholarly journals Forecasting Value-at-Risk Using High-Frequency Information

Econometrics ◽  
2013 ◽  
Vol 1 (1) ◽  
pp. 127-140 ◽  
Author(s):  
Huiyu Huang ◽  
Tae-Hwy Lee
Keyword(s):  
At Risk ◽  
High Frequency ◽  
Value At Risk ◽  
Frequency Information

Computing value at risk with high frequency data

1999 ◽  
Vol 6 (5) ◽  
pp. 431-455 ◽  
Author(s):  
Andrea Beltratti ◽  
Claudio Morana
Keyword(s):  
At Risk ◽  
High Frequency ◽  
Value At Risk ◽  
High Frequency Data ◽  
Frequency Data
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