Heterogeneous Autoregressive Realized Volatility Model Research Articles

Business applications and state‐level stock market realized volatility: A forecasting experiment

AbstractWe analyze the predictive value of (the surprise component of) state‐level business applications, as a proxy of local investor sentiment, for the state‐level realized US stock‐market volatility. We use high‐frequency data for the period from September 2011 to October 2021 to compute realized volatility. Using an extended version of the popular heterogeneous autoregressive realized volatility model and accounting for the possibility that users of forecasts have an asymmetric loss function, we show that business applications tend to have predictive value for realized state‐level stock‐market volatility, as well as for upside (“good”) and downside (“bad”) realized volatility, for users of forecasts who suffer a larger loss from an underprediction of realized volatility than from an overprediction of the same (absolute) seize, after controlling for realized moments (realized skewness, realized kurtosis, realized jumps, and realized tail risks). We also highlight that the COVID‐19 period is a major driver of our empirical results.

Jumps and gold futures volatility prediction

This paper mainly checks whether jump component is efficient to predict Chinese gold futures volatility. Based on the high-frequency data, we find jump component can provide valuable information to forecast the volatility of Chinese gold futures market based on the heterogeneous autoregressive-realized volatility model. This paper tries to examine the role of jump in Chinese gold futures market.

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

AbstractThis paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market.

Impact of crude oil volatility jumps on sustainable investments: Evidence from India

AbstractThis study examines the impact of crude oil volatility jumps on the realized volatility (RV) of green and dirty stocks in India. In doing so, we first estimate the time‐varying jumps in oil market implied volatility index (OVX) and then augment the heterogeneous autoregressive (HAR) process with the information on such jumps. Our sample runs from December 2012 to April 2022, which includes 2328 data points. Comparing a range of HAR‐type models, we find that crude oil volatility jumps provide additional information, which is not contained even in the OVX index itself. In particular, the HAR–RV model that considers both leverage effects and the information on volatility jumps produces superior forecasts compared with the existing approaches. The economic significance of these results is also supported by a simple value‐at‐risk analysis.

Sample frequency robustness and accuracy in forecasting Value-at-Risk for Brent Crude Oil futures

In this paper we examine how sensitive Value-at-Risk (VaR) forecasts based on simple linear quantile regressions are to the sampling frequency used to calculate realized volatility. We use sampling frequencies from one to 108 min for ICE Brent Crude Oil futures and test the out-of-sample performance of a set of quantile regression models using formal coverage tests. The results show that a one-factor model performs exceptionally well for most sampling frequencies used to calculate realized volatility. In comparison with the well-known Heterogeneous Auto-regressive Model of Realized Volatility (HAR-RV) and a quantile regression version of the HAR model (HAR-QREG), we also find that the one-factor model is much less sensitive to the sampling frequency used to calculate realized volatility.

Uncertainty indices and stock market volatility predictability during the global pandemic: evidence from G7 countries

ABSTRACT This article attempts to examine the predictability of a significant number of uncertainty indices for the G7 stock market volatility based on a Heterogeneous AutoRegressive Realized Volatility (HARRV) model and a combination forecast framework during the global pandemic COVID-19. We include in our analysis the Infectious Disease Equity Market Volatility (IDEMV), the VIX, the Economic Policy Uncertainty (EPU), the Equity Market Volatility (EMV), the Geopolitical risk (GPR) as well as the crude oil futures’ realized volatility. Out-of-sample evidence shows that models incorporating all uncertainty indices improve forecasting performance for most stock markets’ volatility during a long out-of-sample period and also during the coronavirus period. The results are robust using an alternative volatility model, an alternative realized measure and a recursive window analysis. The predictability of the uncertainty indices is also evaluated through risk management and portfolio loss functions and results suggest that the LASSO combination and a HARRV model including all indices are the most profitable for all stock markets during the global pandemic.

Forecasting stock market realized volatility: the role of global terrorist attacks

ABSTRACT In this study, we provide the predictive linkage between global terrorist attacks and stock market volatility. We propose the predictive model by extending the prevailing heterogeneous autoregressive model for realized volatility (HAR-RV) with global terrorism and denote it as HAR-RV-GT. According to the Diebold – Mariano test and the model confidence set, we consistently find the superior forecasting performance delivered by the HAR-RV-GT model. For comprehensive empirical results, we extend the key finding to various settings including the consideration of popular jump and leverage effects, the use of more types of forecasting models, and the inclusion of long-horizon global terrorist attacks as well as the domestic terrorist attacks in the US. Additionally, the substantial economic gains based on a mean-variance investor confirm the valuable forecasting role of terrorist attacks. Our results are robust to a wide range of checks.

News-based sentiment and bitcoin volatility

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.

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

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.

A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction

Modelling and forecasting the stock price constitute an important area of financial research for both academics and practitioners. This study seeks to determine whether improvements can be achieved by forecasting the stock index and volatility with the use of a hybrid model and incorporating the financial variables. We extend the literature of stock market forecasting by applying a hybrid model which combines adaptive wavelet transform (AWT), long short-term memory (LSTM) and ARIMAX-GARCH family models to predict stock index and combines AWT, LSTM and heterogeneous autoregressive model of realized volatility (HAR-RV) model to predict stock volatility for two major indexes in the U.S. stock market including the Dow Jones Industrial Average (DJIA) and Nasdaq Composite (IXIC). The results indicate an overall improvement in forecasting of stock index using the AWT-LSTM-ARMAX-FIEGARCH model with student’s t distribution as compared to the benchmark models. The robust test proves that this model has a higher prediction accuracy in prediction of different time horizons (1-, 10-, 15-, 20-, 30-, and 60-days ahead) for both stock indexes. Also, AWT-LSTM improves ability of HAR (3) X-RV model in prediction of the stocks realized volatility for mentioned time horizons.

A multivariate HAR-RV model with heteroscedastic errors and its WLS estimation

This work considers a multivariate heterogeneous autoregressive-realized volatility (HAR-RV) model in the presence of heteroscedasticity and aims to analyze realized volatilities of multiple assets that possess non-standard features, such as non-Gaussianity, time varying volatility and long-memory dependence. For capturing the long-memory, a HAR-RV model is employed, while for a heavy-tailed distribution, a GARCH process is adopted on the noise term. To estimate coefficients of the HAR-RV-GARCH model, we suggest weighted least squares estimator (WLSE) based on an observed weighting scheme and prove its asymptotic normality. Simulation results show a good performance on the WLSE. The multivariate HAR-RV-GARCH model fitted by the WLSE is illustrated with an application to realized volatilities of multiple financial data.

Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model

The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.

Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model

AbstractIn this study, we investigate the impacts of common information between oil futures and the United States stock markets on forecasting oil volatility using the multivariate heterogeneous autoregressive realized volatility model. We have several noteworthy findings. First, the in‐sample estimation results show that the negative returns and the ‘bad’ jumps have larger influence on oil futures volatility than positive returns and ‘good’ jumps, respectively. Second, the out‐of‐sample results further indicate that our multivariate heterogeneous autoregressive (HAR) model can generate higher forecast accuracy than individual HAR model for the realized volatility‐type models, which highlights the importance of incorporating volatility information from the stock Market. Third, our findings are robust across the direction‐of‐change test and different windows. Furthermore, from the perspective of the investors' allocation portfolio in the future, the use of common information can also bring greater gains.

A note on oil price shocks and the forecastability of gold realized volatility

ABSTRACT We examine the predictive power of disentangled oil price shocks over gold market volatility via the heterogeneous autoregressive realized volatility (HAR-RV) model. Our in- and out-of-sample tests show that combining the information from both oil supply and demand shocks with the innovations associated with financial market risks improves the forecast accuracy of realized volatility of gold. While financial risk shocks are important on their own, including oil price shocks in the model provides additional forecasting power in out-of-sample tests. Compared to the benchmark HAR-RV model, the extended model with all the three shocks included outperforms, in a statistically significant manner, all other variants of the HAR-RV framework for short-, medium, and long-run forecasting horizons. The findings highlight the predictive power of cross-market information in commodities and suggest that disentangling supply- and demand-related factors associated with price shocks could help improve the accuracy of forecasting models.

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

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.

The predictive power of oil price shocks on realized volatility of oil: A note

This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.

Forecasting Realized Volatility of Bitcoin: The Role of the Trade War

We analyze the role of the US–China trade war in forecasting out-of-sample daily realized volatility of Bitcoin returns. We study intraday data spanning from 1st July 2017 to 30th June 2019. We use the heterogeneous autoregressive realized volatility model (HAR-RV) as the benchmark model to capture stylized facts such as heterogeneity and long-memory. We then extend the HAR-RV model to include a metric of US–China trade tensions. This is our primary forecasting variable of interest, and it is based on Google Trends. We also control for jumps, realized skewness, and realized kurtosis. For our empirical analysis, we use a machine-learning technique that is known as random forests. Our findings reveal that US–China trade uncertainty does improve forecast accuracy for various configurations of random forests and forecast horizons.

Forecasting the volatility of Chinese stock market: An international volatility index

AbstractTo forecast the volatility of Chinese stock market, we use information from 28 international markets rather than relying on the Chinese market alone. A common factor, which we call the international volatility index, is constructed by the first principal component of all the 28 cross‐national stock market volatilities. We then add the international volatility index into the prevailing heterogeneous autoregressive model of realized volatility (HAR‐RV). The in‐sample estimation results show that the impact of this common index on future Chinese market volatility is statistically significant and positive. More importantly, the out‐of‐sample forecasting results suggest that our proposed model outperforms competing models including the HAR‐RV, kitchen sink model, and combination approaches. The results are similar when we use a wide range of robustness checks. Furthermore, the international volatility index also yields the highest economic value from an asset allocation perspective.

A note on investor happiness and the predictability of realized volatility of gold

We apply the heterogeneous autoregressive realized volatility (HAR-RV) model to examine the importance of investor happiness in predicting the daily realized volatility of gold returns. We estimate daily realized volatility by employing intraday data providing both in-sample and out-of-sample predictions. Our in-sample results reveal that realized volatility is negatively linked to investor happiness. Moreover, our out-of-sample results show that extending the HAR-RV model to include investor happiness significantly improves the accuracy of forecasts of realized volatility at short- and medium-run forecast horizons.

Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss

We analyze the role of global and regional measures of financial stress in forecasting realized volatility of the oil market based on 5-min intraday data covering the period of 4th January, 2000 until 26th May, 2017. In this regard, we use various variants of the Heterogeneous Autoregressive (HAR) model of realized volatility (HAR-RV). Our main finding is that indexes of financial stress help to improve forecasting performance, with it being important to differentiate between regional sources of financial stress (United States, other advanced economies, emerging markets). Another key finding is that the shape of the forecaster loss function that one uses to evaluate forecasting performance plays an important role. More specifically, forecasters who attach a higher cost to an overprediction of realized volatility as compared to an underprediction of the same absolute size should pay particular attention to financial stress originating in the U.S. But, in case an underprediction is more costly than a comparable overprediction, then forecasters should closely monitor financial stress caused by developments in emerging-market economies. In sum, financial stress does have predictive value for realized oil-price volatility, with alternative types of investors benefiting from monitoring different regional sources of financial stress.