The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets

Naimoli, Antonio (2022): The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets.

Abstract

The aim of this paper is to investigate the impact of public sentiment on tail risk forecasting. In this framework, we extend the Realized Exponential GARCH model to directly incorporate information from realized volatility measures and exogenous variables. Several indices related to social media and journal articles regarding the economy and stock market volatility are considered as potential drivers of volatility dynamics. An application to the prediction of daily Value at Risk and Expected Shortfall for the Standard & Poor's 500 index provides evidence that combining the information content of realized volatility and sentiment measures can lead to significant accuracy gains in forecasting tail risk.

• Item Type: MPRA Paper
• Language: English
• Keywords: Realized Exponential GARCH; sentiment indices; economic policy uncertainty; tail risk forecasting; risk management.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D80 - General; E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E66 - General Outlook and Conditions; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill

References

Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys (2003). Modeling and forecasting realized volatility. Econometrica 71(2), 579–625.

Antonakakis, N., I. Chatziantoniou, and G. Filis (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters 120(1), 87–92.

Artzner, P., F. Delbaen, J.-M. Eber, and D. Heath (1999). Coherent measures of risk. Mathematical finance 9(3), 203–228.

Asgharian, H., C. Christiansen, and A. J. Hou (2015). Effects of macroeconomic uncertainty on the stock and bond markets. Finance Research Letters 13, 10–16.

Asgharian, H., A. J. Hou, and F. Javed (2013). The importance of the macroeconomic variables in forecasting stock return variance: A garch-midas approach. Journal of Forecasting 32(7), 600–612.

Audrino, F., F. Sigrist, and D. Ballinari (2020). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting 36(2), 334–357.

Baker, S. R., N. Bloom, S. Davis, and T. Renault (2021). Twitter-derived measures of economic uncertainty.

Baker, S. R., N. Bloom, and S. J. Davis (2016). Measuring economic policy uncertainty. The quarterly journal of economics 131(4), 1593–1636.

Baker, S. R., N. Bloom, S. J. Davis, and K. J. Kost (2019). Policy news and stock market volatility. Technical report, National Bureau of Economic Research.

Basel Committee (1996). Overview over the amendment to the capital accord to incorporatemarket risks, technical report, Basel committee on banking supervision. Available at https://www.bis.org/publ/bcbs23.htm.

Basel Committee (2013). Fundamental review of the trading book: A revised market risk framework. technical report, bank for international settlements. Available at https://www.bis.org/publ/bcbs265.htm.

Basel Committee (2016). Minimum capital requirements for market risk. technical report, bank for international settlements. Available at https://www.bis.org/bcbs/publ/d352.htm.

Basel Committee (2017). Pillar 3 disclosure requirements - consolidated and enhanced framework. technical report, Basel committee on banking supervision. Available at https://www.bis.org/bcbs/publ/d400.htm.

Basel Committee (2019). Minimum capital requirements for market risk. technical report, bank for international settlements. Available at https://www.bis.org/bcbs/publ/d457.htm. Revised in Februrary 2019.

Bloom, N. (2009). The impact of uncertainty shocks. econometrica 77(3), 623–685.

Chahine, S., M. Daher, and S. Saade (2021). Doing good in periods of high uncertainty: Economic policy uncertainty, corporate social responsibility, and analyst forecast error. Journal of Financial Stability 56, 100919.

Chan, Y.-C., W. Saffar, and K. J. Wei (2021). How economic policy uncertainty affects the cost of raising equity capital: Evidence from seasoned equity offerings. Journal of Financial Stability 53, 100841.

Christoffersen, P. F. (1998). Evaluating interval forecasts. International Economic Review 39(4), 841–862.

Conrad, C. and K. Loch (2015). Anticipating long-term stock market volatility. Journal of Applied Econometrics 30(7), 1090–1114.

Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7(2), 174–196.

Dorion, C. (2016). Option valuation with macro-finance variables. Journal of Financial and Quantitative Analysis 51(4), 1359–1389.

Engle, R. F., E. Ghysels, and B. Sohn (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics 95(3), 776–797.

Engle, R. F. and S. Manganelli (2004). Caviar: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics 22(4), 367–381.

Engle, R. F. and J. G. Rangel (2008). The spline-garch model for low-frequency volatility and its global macroeconomic causes. The review of financial studies 21(3), 1187–1222.

Fissler, T. and J. F. Ziegel (2016, 08). Higher order elicitability and osband’s principle. Ann. Statist. 44(4), 1680–1707.

Fissler, T., J. F. Ziegel, and T. Gneiting (2016). Expected shortfall is jointly elicitable with value at risk-implications for backtesting. Risk Magazine, 58–61.

Fornari, F. and A. Mele (2013). Financial volatility and economic activity. Journal of Financial Management, Markets and Institutions 1(2), 155–198.

Gerlach, R., A. Naimoli, and G. Storti (2020). Time-varying parameters realized garch models for tracking attenuation bias in volatility dynamics. Quantitative Finance 20(11), 1849–1878.

Gneiting, T. (2011). Making and evaluating point forecasts. Journal of the American Statistical Association 106(494), 746–762.

Gonzalez-Rivera, G., T.-H. Lee, and S. Mishra (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of Forecasting 20(4), 629–645.

Han, H. (2015). Asymptotic properties of garch-x processes. Journal of Financial Econometrics 13(1), 188–221.

Hansen, P. and A. Lunde (2011). Forecasting volatility using high frequency data. The Oxford Handbook of Economic Forecasting, Oxford: Blackwell, 525–556.

Hansen, P. R. and Z. Huang (2016). Exponential garch modeling with realized measures of volatility. Journal of Business & Economic Statistics 34(2), 269–287.

Hansen, P. R., Z. Huang, and H. H. Shek (2012). Realized garch: a joint model for returns and realized measures of volatility. Journal of Applied Econometrics 27(6), 877–906.

Hansen, P. R., A. Lunde, and J. M. Nason (2011). The model confidence set. Econometrica 79(2), 453–497.

Lang, Q., X. Lu, F. Ma, and D. Huang (2021). Oil futures volatility predictability: Evidence based on twitter-based uncertainty. Finance Research Letters, 102536.

Li, Y.-N., Y. Zhang, and C. Zhang (2019). Statistical inference for measurement equation selection in the log-realgarch model. Econometric Theory 35(5), 943–977.

Liu, L. and T. Zhang (2015). Economic policy uncertainty and stock market volatility. Finance Research Letters 15, 99–105.

Ma, F., M. Wahab, J. Liu, and L. Liu (2018). Is economic policy uncertainty important to forecast the realized volatility of crude oil futures? Applied Economics 50(18), 2087–2101.

Naimoli, A., R. Gerlach, and G. Storti (2022). Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators. Economic Modelling 107, 105701.

Pastor, L. and P. Veronesi (2013). Political uncertainty and risk premia. Journal of financial Economics 110(3), 520–545.

Patton, A. J., J. F. Ziegel, and R. Chen (2019). Dynamic semiparametric models for expected shortfall (and value-at-risk). Journal of Econometrics 211(2), 388 – 413.

Xu, Y., X. Wang, and H. Liu (2021). Quantile-based garch-midas: Estimating value-at-risk using mixed-frequency information. Finance Research Letters 43, 101965.

Yu, X. and Y. Huang (2021). The impact of economic policy uncertainty on stock volatility: Evidence from garch–midas approach. Physica A: Statistical Mechanics and its Applications 570, 125794.

Zhu, S., Q. Liu, Y.Wang, Y.Wei, and G.Wei (2019). Which fear index matters for predicting us stock market volatilities: Text-counts or option based measurement? Physica A: Statistical Mechanics and its Applications 536, 122567.