News Impact Curves Research Articles

Modeling the News Impact on Volatility to Refugees Movement from LMICs: An APARCH Approach

Political and economic instability, armed conflicts, and weather abnormalities may introduce a high degree of volatility to the refugee movement. Refugee movement volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it differently. APARCH is one of the GARCH extension types of models that are popularly used to capture asymmetric volatility. In this paper, an attempt is made to model the volatility of the yearly refugee movement for the low-and middle-income countries using the APARCH model. The key finding of this study is that GARCH-extension type models indicating past volatility of the annual number of refugees is significant, influencing current volatility. The News Impact Curve is derived from the fitted APARCH model, which confirmed the presence of asymmetry in the volatility of the refugee movement. The News Impact Curve of the models for both groups of countries described that negative shocks of the same magnitude have a larger impact on volatility to refugee movement than positive shocks.

Beyond the Hype: Evaluating the Real Impact of News on Cryptocurrency Market Volatility

This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.

Modeling Asymmetric Volatility: A News Impact Curve Approach

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets.

The analysis of time-varying high-order moment of wind power time series

The efficient wind power forecasting is of great significance for the safety and stability of power grid. Analysis on the high order moment of wind power time series play an important role in improving the forecasting accuracy of wind power. With the rolling subsample generalized auto-regressive conditional heteroskedasticity with skewness and kurtosis (GARCHSK) model sequence, the analysis method for conditional skewness and conditional kurtosis of wind power time series is proposed. The dynamic curves of parameter are presented by estimating the characteristic parameters in the subsample models. Moreover, the time-varying structure of high order moment of the wind power time series is analyzed. A Generalized News Impact Curve (GNIC) is proposed, and the impact of news on the conditional skewness and conditional kurtosis are measured and evaluated by the third order GNIC and the fourth order GNIC. The result of case based on the wind power data of Jiangsu power grid demonstrates that the time-varying structure of high-order moments of wind power time series is steady relatively, and the time-varying characteristics of high-order moments are further verified by GNIC.

Are Crypto Assets Connected to Real World Shocks? The Nexus Between Terrorist Attacks, Bitcoin and NFTs

This study investigates the impact of terrorist attacks on the price fluctuations of Bitcoin prices and NFT sales. Although the value proposition of cryptocurrencies, Decentralized Finance, and the whole blockchain revolution is a quicker, cheaper, and more transparent kind of finance, various terrorist organizations tend to use cryptocurrency anonymously to finance their terrorist activities around the world by bypassing the banking system of the regulated countries. The analyses reveal that returns of Bitcoin and NFT markets are positively associated with the organization and funding phases of the terrorist attacks but negatively associated with the post-terrorist attack circumstances, meaning that it generates positive abnormal returns (AR) prior to the attack but creates negative AR right after the attack. Furthermore, while the Bitcoin news impact curve (NIC) is nearly symmetric, the NFT NIC is asymmetric, with positive shocks having significantly more impact on future volatility than negative shocks of the same magnitude. Since previous studies claim that terrorist attack news is good news for Bitcoin returns, we will enrich our AR analysis results with NICs results.

Impact of COVID-19 on Stock Indices Volatility: Long-Memory Persistence, Structural Breaks, or Both?

The onset of the COVID-19 pandemic has increased volatility in financial markets, motivating researchers to investigate its impact. Some use the GARCH family of models to focus on long-memory persistence, while others use Markov chain models to better identify structural breaks and regimes. However, no study has addressed the occurrence of these two phenomena in a unified framework. Since both are important features of the data, to ignore one or the other could lead to poorly specified models. The outcome would be incorrect risk measurement, with implications for risk management, Value at risk, portfolio decisions, forecasting, and option pricing. This paper aims to fill this gap in the literature. We assemble an international dataset for 16 stock market indices in three continents over the period from August 1, 2019 to February 18, 2022, totalling 669 business days. Using R, we estimate 80 GARCH family models, 16 pure Markov-Switching models, and 900 combined GARCH/ Markov-Switching models using daily stock market log-returns. We allow for two volatility regimes (low and high). We also measure and incorporate News Impact Curves, which show how past shocks affect contemporaneous volatility. Our main finding, across estimated models, is that COVID-19 affected both long-memory persistence and volatility regimes in most markets. To describe the specific impact in each market, we report News Impact Curves. Lastly, the first wave of COVID-19 had a much greater impact on volatility than did subsequent waves linked to the emergence of new variants.

Forecasting Crude Oil Future Volatilities with a Threshold Zero-Drift GARCH Model

The recent price crash of the New York Mercantile Exchange (NYMEX) crude oil futures contract, which occurred on 20 April 2020, has caused history-writing movements of relative prices. For instance, the West Texas Intermediate (WTI) experienced a negative price. Explosive heteroskedasticity is also evidenced in associated products, such as the Intercontinental Exchange Brent (BRE) and Shanghai International Energy Exchange (INE) crude oil futures. Those movements indicate potential non-stationarity in the conditional volatility with an asymmetric influence of negative shocks. To incorporate those features, which cannot be accommodated by the existing generalized autoregressive conditional heteroskedasticity (GARCH) models, we propose a threshold zero-drift GARCH (TZD-GARCH) model. Our empirical studies of the daily INE returns from March 2018 to April 2020 demonstrate the usefulness of the TZD-GARCH model in understanding the empirical features and in precisely forecasting the volatility of INE. Robust checks based on BRE and WTI over various periods further lead to highly consistent results. Applications of news impact curves and Value-at-Risk (VaR) analyses indicate the usefulness of the proposed TZD-GARCH model in practice. Implications include more effectively hedging risks of crude oil futures for policymakers and market participants, as well as the potential market inefficiency of INE relative to WTI and BRE.

Effects of Multiple Financial News Shocks on Tourism Demand Volatility Modelling and Forecasting

Even though both symmetric and asymmetric conceptions of news impacts are well-established in the disciplines of economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet been delved into or gauged in any tourist destination. This work hypothesises and verifies that the news impact curve (NIC), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the Malaysian tourist industry. Among them, three primarily volatility models (GARCH, EGARCH, and GJRGARCH) are used in conjunction with five financial news shocks (FFNSs), namely the Kuala Lumpur Composite Index (KLCI), the United States Dollar Index (DXY), the stock performance of 500 large companies listed on stock exchanges (S&P500), Crude Oil (CO), and Gold Price (GP). Among the most significant findings of this study are the demonstration of monthly seasonality using conditional mean equations, asymmetry effects in EGARCH-FFNSs, and GJRGARCH-FFNSs models in conditional variance equations and 50 NICs, and the GARCH-FFNSs model’s evaluation of the persistence influence of news shocks on monthly visitor arrivals in Malaysia. The GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold Price have the most substantial impact on the number of tourists to Malaysia. In addition, it should be emphasised that the methodological framework utilised in this study can be a useful tool for creating and forecasting the performance of symmetry and asymmetry impacts on tourism demand volatility.

Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model), and higher-moment spillovers. The matrix exponential transformation guarantees the positive definiteness of the dynamic covariance matrix. We decompose the likelihood function of the RMESV-ALM model into two components: one based on the conventional Kalman filter, and the other evaluated by a Monte Carlo likelihood technique. We consider a two-step quasi-maximum likelihood estimator for maximizing the likelihood function, and examine the finite sample properties of the estimator. The specification enables us to analyze asymmetric and higher-moment spillover effects in the covariance dynamics via news impact curves and impulse response functions. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. Our empirical results suggest the RMESV-ALE specification to be superior, and spillover effects are found from returns or volatility to the remaining volatilities.

Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

Asymmetric volatility structure of equity returns: Evidence from an emerging market

This study aims to evaluate the volatility structure of equity returns in an emerging market. We test the persistence and asymmetries in the volatility structure of equity returns in the Pakistan stock exchange (PSX) between 2006 and 2020. The volatility dynamics are assessed using multiple symmetric and asymmetric variants of GARCH family models. We also introduce News Impact Curves to analyze the presence of asymmetries in the volatility of the returns. Our results demonstrate that the volatility is persistent only in daily returns but not in weekly and monthly returns. Similarly, asymmetries were observed for daily returns implying that news arriving in the market continuously does impact investors' sentiment and behavior. However, this phenomenon subdues when the period is extended, reflecting that PSX is efficient in semi-strong form. These findings have important implications for asset management, instrument pricing, cost of capital estimations, and portfolio optimization.

Bayesian Analysis of Realized Matrix-Exponential GARCH Models

This study develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. An alternative multivariate asymmetric function to develop news impact curves is also considered. We consider Bayesian Markov chain Monte Carlo estimation to allow non-normal posterior distributions and illustrate the usefulness of the algorithm with numerical simulations for two assets. We compare the realized MEGARCH models with existing multivariate GARCH class models for three US financial assets . The empirical results indicate that the realized MEGARCH models outperform the other models regarding out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.

CAViaR and the Empirical Study on China’s Stock Market

The patterns of China’s stock market during 2004 and 2020 are characterized by Value at Risks (VaR) of 3 important stock indexes: Shanghai Security Composite index (SSEC), Shanghai Stock Exchange B Share index (SHBSHR) and Shenzhen Security Component index (SZSC), applying conditional autoregressive value at risk (CAViaR) model. The estimation results fit the real situation well. The unique style of news impact curves illustrates the peculiarity of China’s stock market caused by imperfect market mechanism and the small traders’ psychology-overconfidence. By comparing the estimation results, we found that SAV was a proper model for calculating 1% VaR of both SSEC and SHBSHR, while it was better for SZSC to choose Indirect GARCH model for 1% VaR estimation.

Moneyball in the Turkish Football League revisited: anomalies in Galatasaray, Fenerbahce and Besiktas stock returns during Covid-19 Incident.

Purpose - In this paper, we focus on the news impact analysis of the club stock returns. Galatasaray, Fenerbahce and Besiktas stock prices skyrocketed in the last 3 months right after the suspension of leagued due to pandemic since they were not supported with the financial results of the clubs Methodology - We investigate the anomalies observed in the pandemic for three big football club stock returns by utilizing EGARCH models and News Impact Curves Findings- According to our EGARCH models we find that in Covid-19 period news impact persistency significantly increased for all three big club stock volatilities. Moreover, News Impact curves showed us in Covid-19 period the behavior of Fenerbahce and Besiktas volatility changes. In the pandemic period after suspension of Turkish Football League bad news has more impact on Fenerbahce and Besiktas stock volatility Conclusion- Since the leagues are suspended due to Covid-19 pandemic, and weak financial performances of all teams also do not support the stock price performances there is no fundamental explanation for this hike experienced in the last 3 months. This result confirms our first study conclusion which is sporting performances, excluding the unexpected wins, do not have any impact on the volatility of listed football clubs.

Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.

Volatility asymmetry in functional threshold GARCH model

Modeling volatility is one of the prime objectives of financial time‐series analysis. A significant feature encountered in the modeling of financial data is the asymmetric response to the volatility process of unanticipated shocks. With improvements in data acquisition, functional versions of the heteroskedastic models have emerged to deal with the high‐frequency observations. Although previous studies have developed some functional time‐series methods, it remains a necessity to analyze the variations in the asymmetry of the discrete model and the function model. In this study, we propose a functional threshold GARCH (fTGARCH) model and extend the news impact curve (NIC) and the cumulative impact response function (CIRF) within the functional heteroskedastic framework. We find that the fTGARCH model can describe the asymmetry of the observation data, which are revealed by the sample cross‐correlation functions. The slope of the NIC changes with time for functional GARCH class models, and the changes are asymmetrical for the fTGARCH model. Using the generalized CIRF, we can explore the persistent effects of volatility for the functional GARCH class models. By fitting the models to the S&P 500 stock market index, we conclude that the fTGARCH model has some flexibility and superiority in regard to volatility asymmetry.

Asymmetry and leverage in GARCH models: a News Impact Curve perspective

ABSTRACTModels for conditional heteroskedasticity belonging to the GARCH class are now common tools in many economics and finance applications. Among the many possible competing univariate GARCH models, one of the most interesting groups allows for the presence of the so-called asymmetry or leverage effect. In our view, asymmetry and leverage are two distinct phenomena, both inspired by the seminal work of Black in 1976. We propose definitions of leverage and asymmetry that build on the News Impact Curve, allowing to easily and coherently verify if they are both present. We show that several GARCH models are asymmetric but none is allowing for a proper leverage effect. Finally, we extend the leverage definition to a local leverage effect and show that the AGARCH model is coherent with the presence of local leverage. An empirical analysis completes the paper.

Bayesian Analysis of Realized Matrix-Exponential GARCH Models

The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric function to develop news impact curves. We consider Bayesian MCMC estimation to allow non-normal posterior distributions. For three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The empirical results indicate that the realized MEGARCH models outperform the other models regarding in-sample and out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.

Estimating and Forecasting Volatility Using Leverage Effect

This research provides a theoretical foundation for our previous empirical finding that leverage effect has a role in estimating and forecasting volatility. This empirics is also related to earlier econometric studies of news impact curves (Engle and Ng, Chen and Ghysels). Our new theoretical development is based on the concept of projection on stable subspaces of semi-martingales. We show that this projection provides a framework for forecasting (across time periods) that is internally consistent with the semi-martingale model which is used for the intra-day high frequency asymptotics. The paper shows that the approach provides improved estimation and forecasting both theoretically, in simulation, and in data.

Wind power forecasting based on outlier smooth transition autoregressive GARCH model

The impacts of outlying shocks on wind power time series are explored by considering the outlier effect in the volatility of wind power time series. A novel short term wind power forecasting method based on outlier smooth transition autoregressive (OSTAR) structure is advanced, then, combined with the generalized autoregressive conditional heteroskedasticity (GARCH) model, the OSTAR-GARCH model is proposed for wind power forecasting. The proposed model is further generalized to be with fat-tail distribution. Consequently, the mechanisms of regimes against different magnitude of shocks are investigated owing to the outlier effect parameters in the proposed models. Furthermore, the outlier effect is depicted by news impact curve (NIC) and a novel proposed regime switching index (RSI). Case studies based on practical data validate the feasibility of the proposed wind power forecasting method. From the forecast performance comparison of the OSTAR-GARCH models, the OSTAR-GARCH model with fat-tail distribution proves to be promising for wind power forecasting.