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.

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.

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 Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dawit Yeshiwas ◽  
Yebelay Berelie
Keyword(s):  
Crude Oil ◽  
High Frequency ◽  
Realized Volatility ◽  
Multivariate Garch ◽  
High Frequency Data ◽  
Frequency Data ◽  
Asset Return ◽  
Coffee Arabica ◽  
Forecasting Performance ◽  
Intraday Data

Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.

scholarly journals El Niño and forecastability of oil-price realized volatility

2021 ◽  
Vol 144 (3-4) ◽  
pp. 1173-1180
Author(s):  
Elie Bouri ◽  
Rangan Gupta ◽  
Christian Pierdzioch ◽  
Afees A. Salisu
Keyword(s):  
El Niño ◽  
Southern Oscillation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Realized Volatility ◽  
Oil Price ◽  
Out Of Sample ◽  
Asymmetric Loss Function ◽  
Standard Tests

AbstractWe forecast monthly realized volatility (RV) of the oil price based on an extended heterogenous autoregressive (HAR)-RV model that incorporates the role of the El Niño Southern Oscillation (ENSO), as captured by the Equatorial Southern Oscillation Index (EQSOI). Based on the period covering 1986 January to 2020 December and studying various rolling-estimation windows and forecast horizons, we find that the EQSOI has predictive value for oil-price RV particularly at forecast horizons from 2 to 4 years, and for rolling-estimation windows of length 4 to 6 years. We show that this result holds not only based on standard tests of out-of-sample predictability, but also under an asymmetric loss function.

scholarly journals Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-Of-Sample Forecasting Experiment

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8085
Author(s):  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Risk Factors ◽  
Computational Linguistics ◽  
Oil And Gas ◽  
Realized Volatility ◽  
Climate Risk ◽  
Shrinkage Estimators ◽  
Forecast Horizon ◽  
Climate Risks ◽  
Out Of Sample ◽  
Oil And Natural Gas

We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV) model to examine the out-of-sample forecasting value of climate-risk factors for the realized volatility of movements of the prices of crude oil, heating oil, and natural gas. The climate-risk factors have been constructed in recent literature using techniques of computational linguistics, and consist of daily proxies of physical (natural disasters and global warming) and transition (U.S. climate policy and international summits) risks involving the climate. We find that climate-risk factors contribute to out-of-sample forecasting performance mainly at a monthly and, in some cases, also at a weekly forecast horizon. We demonstrate that our main finding is robust to various modifications of our forecasting experiment, and to using three different popular shrinkage estimators to estimate the extended HAR-RV model. We also study longer forecast horizons of up to three months, and we account for the possibility that policymakers and forecasters may have an asymmetric loss function.

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

scholarly journals UNIT ROOT TEST WITH HIGH-FREQUENCY DATA

2021 ◽  
pp. 1-59
Author(s):  
Sébastien Laurent ◽  
Shuping Shi
Keyword(s):  
Random Walk ◽  
Unit Root ◽  
High Frequency ◽  
Asset Prices ◽  
Composite Index ◽  
Mean Reversion ◽  
Unit Root Tests ◽  
High Frequency Data ◽  
Frequency Data ◽  
Intraday Data

Deviations of asset prices from the random walk dynamic imply the predictability of asset returns and thus have important implications for portfolio construction and risk management. This paper proposes a real-time monitoring device for such deviations using intraday high-frequency data. The proposed procedures are based on unit root tests with in-fill asymptotics but extended to take the empirical features of high-frequency financial data (particularly jumps) into consideration. We derive the limiting distributions of the tests under both the null hypothesis of a random walk with jumps and the alternative of mean reversion/explosiveness with jumps. The limiting results show that ignoring the presence of jumps could potentially lead to severe size distortions of both the standard left-sided (against mean reversion) and right-sided (against explosiveness) unit root tests. The simulation results reveal satisfactory performance of the proposed tests even with data from a relatively short time span. As an illustration, we apply the procedure to the Nasdaq composite index at the 10-minute frequency over two periods: around the peak of the dot-com bubble and during the 2015–2106 stock market sell-off. We find strong evidence of explosiveness in asset prices in late 1999 and mean reversion in late 2015. We also show that accounting for jumps when testing the random walk hypothesis on intraday data is empirically relevant and that ignoring jumps can lead to different conclusions.

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