Kejin Wu, Sayar Karmakar, Rangan Gupta
arXiv 25 Aug 2023 · Econometrics
arXiv:2308.13346 · PDF · DOI · OpenAlex · Extracted main text
In this work, we explore the forecasting ability of a recently proposed normalizing and variance-stabilizing (NoVaS) transformation with the possible inclusion of exogenous variables. From an applied point-of-view, extra knowledge such as fundamentals- and sentiments-based information could be beneficial to improve the prediction accuracy of market volatility if they are incorporated into the forecasting process. In the classical approach, these models including exogenous variables are typically termed GARCHX-type models. Being a Model-free prediction method, NoVaS has generally shown more accurate, stable and robust (to misspecifications) performance than that compared to classical GARCH-type methods. This motivates us to extend this framework to the GARCHX forecasting as well. We derive the NoVaS transformation needed to include exogenous covariates and then construct the corresponding prediction procedure. We show through extensive simulation studies that bolster our claim that the NoVaS method outperforms traditional ones, especially for long-term time aggregated predictions. We also provide an interesting data analysis to exhibit how our method could possibly shed light on the role of geopolitical risks in forecasting volatility in national stock market indices for three different countries in Europe.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Politis, D. N (2015) The model-free prediction principle | 1.000 | 5 | 3 | 100% |
| 2 | Caldara, D. and Iacoviello, M (2022) Measuring geopolitical risk | 0.737 | 3 | 2 | 100% |
| 3 | Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity | 0.644 | 2 | 2 | 100% |
| 4 | Chen, J. and Politis, D. N (2019) Optimal Multi-step-ahead Prediction of ARCH/GARCH Models and NoVaS Transformation | 0.644 | 2 | 2 | 100% |
| 5 | Engle, R. F (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation | 0.644 | 2 | 2 | 100% |
| 6 | Francq, C. et al (2019) Qml inference for volatility models with covariates | 0.644 | 2 | 2 | 100% |
| 7 | Politis, D. N (2003) A normalizing and variance-stabilizing transformation for financial time series | 0.644 | 2 | 2 | 100% |
| 8 | Wang, Y. and Politis, D. N (2022) Model-free bootstrap for a general class of stationary time series | 0.644 | 2 | 2 | 100% |
| 9 | Wu, K. and Karmakar, S (2023) A model-free approach to do long-term volatility forecasting and its variants self | 0.644 | 2 | 2 | 100% |
| 10 | Grebe, M., Kandemir, S., and Tillmann, P (2024) Uncertainty about the war in ukraine: Measurement and effects on the german economy | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 37 scored citations.