Non-parametric news impact curve: a variational approach
In this paper, we propose an innovative algorithm for modelling the news impact curve. The news impact curve provides a nonlinear relation between past returns and current volatility and thus enables to forecast volatility. Our news impact curve is the solution of a dynamic optimization problem based on variational calculus. Consequently, it is a non-parametric and smooth curve. The technique we propose is directly inspired from noise removal techniques in signal theory. To our knowledge, this is the first time that such a method is used for volatility modelling. Applications on simulated heteroskedastic processes as well as on financial data show a better accuracy in estimation and forecast for this approach than for standard parametric (symmetric or asymmetric ARCH) or non-parametric (Kernel-ARCH) econometric techniques.
KeywordsVolatility modelling News impact curve Calculus of variations Wavelet theory ARCH
This work was achieved through the Laboratory of Excellence on Financial Regulation (Labex ReFi) supported by PRES heSam under the reference ANR10LABX0095. It benefited from a French government support managed by the National Research Agency (ANR) within the project Investissements d’Avenir Paris Nouveaux Mondes (investments for the future Paris New Worlds) under the reference ANR11IDEX000602.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
- Anatolyev S, Petukhov A (2016) Uncovering the skewness news impact curve. J Financ Econom 14(4):746–771Google Scholar
- Caporin M, Costola M (2019) Asymmetry and leverage in GARCH models: a news impact curve perspective. Appl Econ 51(31):3345–3364Google Scholar
- Chen WY, Gerlach RH (2019) Semiparametric GARCH via Bayesian model averaging. J Bus Econ Stat to appear Google Scholar
- Engle RF, Ng VK (1993) Measuring and testing the impact of news on volatility. J Finance 48(5):1749–1778Google Scholar
- Fu Y, Zheng Z (2019) Volatility modeling and the asymmetric effect for China’s carbon trading pilot market. Phys A Stat Mech Appl to appear Google Scholar
- Garcin M (2015) Empirical wavelet coefficients and denoising of chaotic data in the phase space. In: Skiadas C (ed) Handbook of applications of chaos theory. CRC/Taylor & Francis, AbingdonGoogle Scholar
- Garcin M (2019) Fractal analysis of the multifractality of foreign exchange rates. Working paperGoogle Scholar
- Glosten LR, Jagannathan R, Runkle D (1993) On the relationship between the expected value and the volatility of the nominal excess return on stocks. J Finance 48(5):1779–1801Google Scholar
- Han H, Kristensen D (2015) Semiparametric multiplicative GARCH-X model: adopting economic variables to explain volatility. Working PaperGoogle Scholar
- Klaassen F (2002) Improving GARCH volatility forecasts with regime-switching GARCH. Advances in Markov-switching models. Springer, Berlin, pp 223–254Google Scholar
- Lahmiri S (2015) Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis. Phys A 437:130–138Google Scholar
- Mallat S (2000) Une exploration des signaux en ondelettes. Éditions de l’École PolytechniqueGoogle Scholar
- Mandelbrot B (1963) The variation of certain speculative prices. J Bus 36(1):392–417Google Scholar
- Nigmatullin RR, Agarwal P (2019) Direct evaluation of the desired correlations: verification on real data. Phys A 534:121558Google Scholar
- Pagan AR, Schwert GW (1990) Alternative models for conditional stock volatility. J Econom 45(1):267–290Google Scholar
- Patton AJ, Sheppard K (2009) Evaluating volatility and correlation forecasts. Handbook of financial time series. Springer, Heidelberg, pp 801–838Google Scholar
- Poon S, Granger CW (2005) Practical issues in forecasting volatility. Financ Anal J 61(1):45–56Google Scholar
- Rudin L, Lions P-L, Osher S (2003) Multiplicative denoising and deblurring: theory and algorithms. Geometric level set methods in imaging, vision, and graphics. Springer, New York, pp 103–119Google Scholar
- Zheng Z, Qiao Z, Takaishi T, Stanley HE, Li B (2014) Realized volatility and absolute return volatility: a comparison indicating market risk. PLoS ONE 9(7):e102940Google Scholar