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Stock market return predictability : Google pessimistic sentiments versus fear gauge

Habibah, Ume; Rajput, Suresh Kumar Oad; Sadhwani, Ranjeeta - In: Cogent economics & finance 5 (2017) 1, pp. 1-15

This study aims at comparing Google Search Volume Indices (GSVIs—including market crash and bear market) and VIX (Investor Fear Gauge Index) in terms of explaining the S&P 500 returns. The VIX is found a more robust predictor of stock market returns than Google indices, and it does granger...

Persistent link: https://www.econbiz.de/10011886968

Distillation of news flow into analysis of stock reactions

Zhang, Junni L.; Härdle, Wolfgang; Chen, Cathy Y.; … - 2015

News carry information of market moves. The gargantuan plethora of opinions, facts and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical...

Persistent link: https://www.econbiz.de/10010471736

Measuring China's stock market sentiment

Li, Jia; Chen, Yun; Shen, Yan; Wang, Jingyi; Huang, Zhuo - 2019

This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word...

Persistent link: https://www.econbiz.de/10012125620

A latent dynamic factor approach to forecasting multivariate stock market volatility : conference paper

Gribisch, Bastian - 2013

This paper proposes a latent dynamic factor model for low- as well as high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and allows for flexible dynamic dependence patterns by combining common latent factors driven by HAR dynamics and...

Persistent link: https://www.econbiz.de/10010341025

A multivariate Kernel approach to forecasting the variance covariance of stock market returns

Becker, Ralf; Clements, Adam; O'Neill, Robert - In: Econometrics : open access journal 6 (2018) 1, pp. 1-27

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of...

Persistent link: https://www.econbiz.de/10011823257

The factor-spline-GARCH model for high and low frequency correlations

Rangel, Jose Gonzalo; Engle, Robert F. - 2009

that characterize long-term correlation patterns. We associate such term behavior with low frequency economic variables … improves the empirical fit of equity correlations in the US and correlation forecasts at long horizons. -- Factor models ; Low … frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

Persistent link: https://www.econbiz.de/10003821063

Flight to Safety in Volatility Forecasting

Ruzzi, Dario - 2018

The benefits of using flight-to-safety (FTS) in volatility forecasting are assessed within a multivariate GARCH framework. In particular, we propose realized semi-covariance between falling equity and rising safe haven returns as a proxy of FTS and we use it to model the conditional distribution...

Persistent link: https://www.econbiz.de/10012916710

International Volatility Risk and Chinese Stock Return Predictability

Chen, Jian - 2017

This paper investigates the predictive ability of international volatility risk for the daily aggregate Chinese stock market returns. We employ the innovations in implied volatility indices of seven major international markets as our international volatility risk proxies. We find that...

Persistent link: https://www.econbiz.de/10012972144

Forecasting Realized Volatility of Russian Stocks using Google Trends and Implied Volatility

Bazhenov, Timofey - 2019

This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the...

Persistent link: https://www.econbiz.de/10012888932

The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility

Hizmeri, Rodrigo - 2019

We document the forecasting gains achieved by incorporating measures of signed, finite and infinite jumps in forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that vary by sector, volume and degree of jump activity. We use...

Persistent link: https://www.econbiz.de/10012889687