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 Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Qu ◽  
Ping Ji
Keyword(s):  
Genetic Algorithm ◽  
Stock Markets ◽  
High Frequency ◽  
Time Horizon ◽  
Realized Volatility ◽  
Heterogeneous Structure ◽  
Moving Window ◽  
Heterogeneous Structures ◽  
Chinese Stock Markets ◽  
New Framework

The heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and propose a new framework, adaptive heterogeneous autoregressive (AHAR) models, whose time horizon structures are optimized by a genetic algorithm. Our models can be applied to markets with different heterogeneous structures, and their time horizon structures can be adjusted adaptively as the market's heterogeneous structure varies. Moving window tests with five-minute returns of the CSI 300 index indicate that the (1,5,22) structure originally proposed for American stock markets is not the best choice for Chinese stock markets, and Chinese stock markets’ heterogeneous structure does vary over time. Using four common loss functions, we find that the AHAR models outperform the corresponding HAR models in most of the forecast windows and thus are reasonable choices for volatility forecasting practices.

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 Think again: volatility asymmetry and volatility persistence

2018 ◽  
Vol 23 (1) ◽  
Author(s):  
Dirk G. Baur ◽  
Thomas Dimpfl
Keyword(s):  
Quantile Regression ◽  
Realized Volatility ◽  
Leverage Effect ◽  
Variance Estimator ◽  
Volatility Persistence ◽  
Volatility Models ◽  
Volatility Asymmetry ◽  
High Volatility ◽  
Volatility Regimes ◽  
Volatility Feedback

Abstract We use a leveraged quantile heterogeneous autoregressive model of realized volatility to illustrate that volatility persistence and the asymmetric “leverage” effect are high volatility phenomena. More specifically, we find that (i) low volatility is not persistent, but high volatility all the more, even featuring properties of explosive processes; and (ii) asymmetry of volatility is only a high volatility phenomenon and there is no asymmetry in low volatility regimes. Our results turn out to be robust to the choice of the realized variance estimator, in particular with respect to jumps. The analysis illustrates that quantile regression can provide information that is hidden in commonly used GARCH or realized volatility models. The quantile regression results can also be linked to the weak empirical evidence of the leverage effect and the volatility feedback effect.

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.

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

Herd Behaviour: How Decisive is the Noise in the NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Paritosh Chandra Sinha
Keyword(s):  
Behavioral Finance ◽  
Stock Markets ◽  
High Frequency ◽  
Stock Exchange ◽  
Time Stamp ◽  
Trade Volume ◽  
Herding Behavior ◽  
Equilibrium Pricing ◽  
Indian Context ◽  
Herd Behaviour

Do investors in the stock markets act/react on true information or noise? Do they believe on their own information or simply herd? The study seeks to explore these typical research queries from the behavioral finance perspectives. In particular, it develops a new theory of herding behavior and extends the models of Banerjee (1992) and Bikhchandani, Hirshleifer, and Welch (1992). The study also empirically tests the same on the Indian context with the high frequency intraday trading data for the real trade-time or time-stamp, trade-volume, and trade-price of ten sample scripts listed for their trading in both markets - the Bombay Stock Exchange (BSE) and the National stock Exchange (NSE). The study contributes to the literature with original findings. It shows that investors in the two Indian stock markets show crowd of positive and negative herding as well significantly and there is huge noise along with information in the markets equilibrium pricing mechanism.

Relative clauses in Agul from a corpus-based perspective

2020 ◽  
Vol 73 (1) ◽  
pp. 113-158
Author(s):  
Timur Maisak
Keyword(s):  
High Frequency ◽  
Relative Clauses ◽  
High Ratio ◽  
The Other ◽  
The Core ◽  
Text Corpora ◽  
The Asymmetry ◽  
Intransitive Verbs ◽  
Syntactic Restrictions ◽  
Locative Case

AbstractThis paper gives an account of participial clauses in Agul (Lezgic, Nakh-Daghestanian), based on a sample of 858 headed noun-modifying clauses taken from two text corpora, one spoken and one written. Noun-modifying clauses in Agul do not show syntactic restrictions on what can be relativized, and hence they instantiate the type known as GNMCCs, or general noun-modifying clause constructions. As the text counts show, intransitive verbs are more frequent than transitives and experiencer verbs in participial clauses, and among intransitive verbs, locative statives with the roots ‘be’ and ‘stay, remain’ account for half of all the uses. The asymmetry between the different relativization targets is also significant. Among the core arguments, the intransitive subject (S) is the most frequent target, patient (P) occupies second place, and agent (A) is comparatively rare. The preference of S and, in general, of S and P over A also holds true for most other Nakh-Daghestanian languages for which comparable counts are available. At the same time, Agul stands apart from the other languages by its high ratio of non-core relativization which accounts for 42% of all participial clauses. Addressee, arguments and adjuncts encoded with a locative case, as well as more general place and time relativizations show especially high frequency, outnumbering such arguments as experiencers, recipients, and predicative and adnominal possessors. Possible reasons for the high ratio of non-argument relativization are discussed in the paper.

scholarly journals The asymmetry of female meiosis reduces the frequency of inheritance of unpaired chromosomes

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Daniel B Cortes ◽  
Karen L McNally ◽  
Paul E Mains ◽  
Francis J McNally
Keyword(s):  
Caenorhabditis Elegans ◽  
X Chromosome ◽  
High Frequency ◽  
Germ Line ◽  
Extra Chromosome ◽  
Polar Body ◽  
Contractile Ring ◽  
Female Meiosis ◽  
Random Segregation ◽  
The Asymmetry

Trisomy, the presence of a third copy of one chromosome, is deleterious and results in inviable or defective progeny if passed through the germ line. Random segregation of an extra chromosome is predicted to result in a high frequency of trisomic offspring from a trisomic parent. Caenorhabditis elegans with trisomy of the X chromosome, however, have far fewer trisomic offspring than expected. We found that the extra X chromosome was preferentially eliminated during anaphase I of female meiosis. We utilized a mutant with a specific defect in pairing of the X chromosome as a model to investigate the apparent bias against univalent inheritance. First, univalents lagged during anaphase I and their movement was biased toward the cortex and future polar body. Second, late-lagging univalents were frequently captured by the ingressing polar body contractile ring. The asymmetry of female meiosis can thus partially correct pre-existing trisomy.

Does VPIN provide predictive information for realized volatility forecasting: evidence from Chinese stock index futures market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Conghua Wen ◽  
Fei Jia ◽  
Jianli Hao
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
Futures Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Trading Behavior ◽  
Volatility Forecasting ◽  
Content Type ◽  
Index Futures ◽  
Intraday Data

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

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