Download or read book Volatility Forecast Using GARCH News Sentiment and Implied Volatility written by Jamie Atkinson and published by . This book was released on 2019 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to its significance, forecasting asset volatility has been an active area of research in recent decades. In this whitepaper we aim to take into account the stylised facts of volatility to improve predictive power of a simple GARCH model. We investigate the power of three GARCH models (GARCH, EGARCH, GJR- GARCH) using implied volatility and news sentiment data as external regressors in order to enhance forecasts of stock return volatility. We also explore the impact of the use of fat-tailed and skewed distributions. Analysis is conducted on 5 constituents of the S&P500. In terms of in-sample performance, the findings suggest that a GJR-GARCH(1,1) model incorporating a student-t distribution, implied volatility and news sentiment data consistently out-performs a simple GARCH(1,1) with a normal distribution. When comparing out-of-sample forecast performance, the enhanced models were able to improve volatility predictions for four out of five stocks.
Download or read book Volatility Forecast with GARCH Model and News Analytics written by Andrea Cantamessa and published by . This book was released on 2019 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study we investigate how the prediction of future volatility is improved by using news (meta)data. We use three input time series, namely: (i) market data, (ii) news sentiment impact scores, as explained by Yu (2014), and (iii) the news volume. We compare the results of predicting volatility by using a “vanilla” GARCH model, which uses market data only, and the news enhanced GARCH, as described above. Finally, the forecasted volatility is compared with the realized volatility, allowing an assessment of the robustness and precision of the model. RavenPack and Thomson Reuters provided news data and market data, respectively. The main findings are that the inclusion of scheduled news and the inclusion of news volume characterized by negative sentiment improve the forecasted volatility. The added value of scheduled news to volatility predictions is in line with Li and Engle (1998).
Download or read book A Practical Guide to Forecasting Financial Market Volatility written by Ser-Huang Poon and published by John Wiley & Sons. This book was released on 2005-08-19 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.
Download or read book Improved Volatility Prediction and Trading Using StockTwits Sentiment Data written by Shradha Berry and published by . This book was released on 2020 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility prediction plays an important role in the financial domain. The GARCH family of prediction models is very popular and efficient in using past returns to forecast volatility. It has also been observed that news, scheduled and unscheduled, have an impact on return volatility of assets. An enhanced GARCH model, called News Augmented GARCH (NAGARCH) includes an additional component for news sentiment. With the rise in popularity of the world wide web and social media, it has become a rich source for opinions and sentiments. Twitter is one such platform. It is a micro-blogging site and a popular source for public view on different topics. StockTwits is a social media platform that started as an application built using Twitter's API. It has since grown into an independent financial social media platform for news and sentiment. StockTwits is a rich source of opinions from subject experts and analysts. This data provides first systematic exploration of social media. It reflects raw sentiments of traders, investors, media, public companies, and investment professionals as opposed to sentiments from curated news wires. This research attempts to determine if the sentiment on stocks from StockTwits micro-blogs can improve volatility prediction. The experiment is performed on 9 NASDAQ100 stocks. The GARCH model with stock returns, and the NA-GARCH model with stock returns and micro-blog sentiment are tuned and their prediction results are evaluated. NA-GARCH, with the sentiment data from StockTwits performed better than the GARCH model in 7 out of the 9 cases.
Download or read book Asset Price and Volatility Forecasting Using News Sentiment written by Zryan Sadik and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Volatility Forecasting in Futures Markets written by Theo Athanasiadis and published by . This book was released on 2015 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility forecasting has paramount importance in position sizing and risk management of CTAs. In this paper we examine the out-of-sample forecasts of widely used volatility estimators for the S&P 500 and the 10-Year US Note from a statistical and Value-at-Risk perspective. Although we do not find evidence for a volatility estimator that is statistically superior, we show that the volatility process of each asset is different with asymmetric GARCH models generating superior forecasts for the S&P 500, whereas symmetric GARCH, the Yang-Zhang estimator along with the implied volatility forecasting better the 10-Year US Note volatility. We also show that the volatility of the 10-Year US Note is more forecastable than that of the S&P 500 producing smaller errors. More importantly, we find that improving the volatility forecast can generate superior VaR estimates that can be accurate under the normal distribution failing only at the lowest quantiles mainly because the distribution is mispecified and badly approximated by the normal. Semi-parametric QML-GARCH models that use the empirical quantiles of the distribution along with GARCH forecasts address that issue and generate superior VaR estimates outperforming all other methods.
Download or read book Volatility Forecasting written by Torben Gustav Andersen and published by . This book was released on 2005 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Download or read book An International Comparison of Implied Realized and GARCH Volatility Forecasts written by Apostolos Kourtis and published by . This book was released on 2016 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare the predictive ability and economic value of implied, realized and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the most accurate predictions while an implied volatility model that corrects for the volatility risk premium is superior at the monthly horizon. Widely used GARCH models have inferior performance in almost all cases considered. All methods perform significantly worse over the 2008-09 crisis period. Finally, implied volatility offers significant improvements against historical methods for international portfolio diversification.
Download or read book Volatility Forecasts and the At the Money Implied Volatility written by Gilles O. Zumbach and published by . This book was released on 2008 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a given time horizon $ DT$, this article explores the relationship between the realized volatility (the volatility that will occur between $t$ and $t DT$), the implied volatility (corresponding to at-the-money option with expiry at $t DT$), and several forecasts for the volatility build from multi-scales linear ARCH processes. The forecasts are derived from the process equations, and the parameters set { it a priori}. An empirical analysis across multiple time horizons $ DT$ shows that a forecast provided by an I-GARCH(1) process (1 time scale) does not capture correctly the dynamic of the realized volatility. An I-GARCH(2) process (2 time scales, similar to GARCH(1,1)) is better, while a long memory LM-ARCH process (multiple time scales) replicates correctly the dynamic of the realized volatility and delivers consistently good forecast for the implied volatility. The relationship between market models for the forward variance and the volatility forecasts provided by ARCH processes is investigated. The structure of the forecast equations is identical, but with different coefficients. Yet the process equations for the variance are very different (postulated for a market model, induced by the process equations for an ARCH model), and not of any usual diffusive type when derived from ARCH.
Download or read book Predicting Financial Volatility written by Martin Martens and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent evidence suggests option implied volatility provides better forecasts of financial volatility than time-series models based on historical daily returns. In particular it is found that daily GARCH forecasts have no or little incremental information over that already contained in implied volatilities. In this study both the measurement and the forecasting of financial volatility is improved using high-frequency data and the latest proposed model for volatility, a long memory model. The results indicate that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with implied volatility and sometimes even outperform implied volatility.
Download or read book Predicting Volatility Using Sentiment and Announcement Data written by Roman Christian Sittl and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the relationship between firm-specific news and public news sentiment on return volatility. Using the comprehensive news and sentiment database RavenPack News Analytics, 40 stocks from 8 GICS sectors are analyzed for the sample period 2010-2017. Based on intraday 30-minute returns, conditionally heteroscedastic volatility models are augmented with news and sentiment proxies. Sentiment and news effects are estimated under different regimes, applying the two-state Markov Regime Switching GARCH and proposing a threshold GARCH model where news event volume determines the volatility regime. To compare and identify stocks and sectors that show higher susceptibility to investor sentiment, estimated sentiment effects are related to firm characteristics that are commonly found to cause return anomalies. Reduction of volatility persistence is observed with the introduction of news and sentiment variables that have varying effects in calm and turbulent regimes. Stocks that are more prone to sentiment effects are usually high-growth firms and tend to have higher variation in firm characteristics that make them harder to evaluate. The efficacy of modeling conditional volatility under different regimes is confirmed by in-sample forecast evaluations. Results for the proposed threshold GARCH model indicate that regimes are driven by news event volume.
Download or read book Implied GARCH Volatility Forecasting written by Thorsten Lehnert and published by . This book was released on 2002 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper empirically investigates a method to quantify volatility using the information content of index options. We derive the parameters of a GARCH option pricing model from the term structure of the observed market smile of DAX 30 index. We find the EGARCH option pricing model (Duan, 1995) performs well in determining the shape of the volatility smile for different maturities in the period of January 2000 to August 2001. Based on the implied EGARCH methodology we use the information in option prices to derive a theoretically sound 'new' measure for local volatility and analyze how well it explains and forecasts actual realized volatility. The daily realized volatility measure is constructed with 5-minute interval transaction prices in the DAX 30 future. The local volatility measure explains a large part of realized volatility and performs considerably better in one day ahead volatility forecasting than conventional time-series models.
Download or read book The Performance of Implied Volatility in Forecasting Future Volatility written by Vladimir Michae Ionesco and published by . This book was released on 2011 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we investigate whether implied volatility is an efficient estimator of future one-month volatility from an informational perspective and whether it outperforms historical volatility in this regard. We first compare the predictive powers of implied volatility, simple historical volatility, and exponential historical volatility, using monthly observations of the S&P 500, FTSE 100, and DAX equity and option markets from 2004 to 2010. Then, we introduce a GARCH(1,1) model and compare in-sample GARCHfitted volatility and implied volatility from 2004 to 2010, as well as out-ofsample GARCH-forecasted volatility and implied volatility from 2005 to 2010, using data on the S&P 500. We find that implied volatility is not only an efficient estimator of future volatility, but also that its information content is at least as good, if not much better, than that of historical volatility. Our results also suggest that implied volatility systematically subsumes the information included in historical volatility, even when a GJR-GARCH model is utilized.
Download or read book Volatility Forecasting A Performance Measure of Garch Techniques With Different Distribution Models written by Hemanth Kumar P. and published by . This book was released on 2019 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility Forecasting is an interesting challenging topicin current financial instruments as it is directly associated with profits. There are many risks and rewards directly associated with volatility. Hence forecasting volatility becomes most dispensable topic in finance. The GARCH distributions play an important role in the risk measurement and option pricing. In this paper the motive is to measure the performance of GARCH techniques for forecasting volatility by using different distribution model. We have used 9 variations in distribution models that are used to forecast the volatility of a stock entity. The different GARCH distribution models observed in this paper are Std, Norm, SNorm, GED, SSTD, SGED, NIG, GHYP and JSU. Volatility is forecasted for 10 days in advance and values are compared with the actual values to find out the best distribution model for volatility forecast. From the results obtain it has been observed that GARCH with GED distribution models has outperformed all models.
Download or read book Handbook of Volatility Models and Their Applications written by Luc Bauwens and published by John Wiley & Sons. This book was released on 2012-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
