News-Enhanced Market Risk Management

Arbex-Valle, Cristiano; Erlwein-Sayer, Christina; Kochendoerfer, Alexandra; Kuebler, Bernhard; Mitra, Gautam; Nzouankeu-Nana, Giles-Arnaud; Nouwt, Barry; Stalknecht, Bram

doi:10.2139/ssrn.2322668

Cited by 3 publications

(1 citation statement)

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“…The structure of our NA-GARCH model is novel and our study vindicates the findings of other researchers, namely, Mitra et al (2009) and Arbex-Valle et al (2013) who have used factor models as predictors of realized volatility. The broad conclusion of the earlier studies which is reinforced by our study is that in the financial markets the use of news sentiment leads to better predictions of the volatility of asset returns.…”

Section: Introductionsupporting

confidence: 74%

News augmented GARCH(1,1) model for volatility prediction

Sadik

Date²,

Mitra³

2018

IMA Journal of Management Mathematics

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Forecasting the volatility of stock return plays an important role in the financial markets. The GARCH model is one of the most common models used for predicting asset price volatility from the return time series. In this study, we have considered quantified news sentiment and its impact on the movement of asset prices as a second source of information, which is used together with the asset time series data to predict the volatility of asset price returns. We call this NA-GARCH (news augmented GARCH) model. Our empirical investigation compares volatility prediction of returns of 12 different stocks (from two different stock markets), with 9 data sets for each stock. Our results demonstrate that NA-GARCH provides a superior prediction of volatility than the "plain vanilla" GARCH and EGARCH models. These results vindicate some recent findings regarding the utility of news sentiment as a predictor of volatility, and also vindicate the utility of our novel model structure combining the proxies for past news sentiments and the past asset price returns.

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Section: Introductionsupporting

confidence: 74%