Volatility Forecasts by Clustering: Applications for VAR Estimation

44 Pages Posted: 23 Mar 2023

See all articles by Zijin Wang

Zijin Wang

Southwestern University of Finance and Economics

Peimin Chen

Shanghai Business School

Peng Liu

Cornell University; Cornell SC Johnson College of Business

Chunchi Wu

The State University of New York (SUNY) at Buffalo - School of Management

Abstract

It is well known that volatility is time-varying and clustered. However, few studies have explored the information content of volatility clustering and its implications for investors’ risk aversion. This information is particularly important in turbulent periods, such as financial crisis. We present a volatility cluster partition model to forecast volatility and apply it to risk management. We find that our model substantially outperforms the GARCH model and improves financial risk management using the value-at-risk metric.

Keywords: Volatility forecasts, Fisher's optimal dissection, value-at-risk

Suggested Citation

Wang, Zijin and Chen, Peimin and Liu, Peng and Wu, Chunchi, Volatility Forecasts by Clustering: Applications for VAR Estimation. Available at SSRN: https://ssrn.com/abstract=4386146 or http://dx.doi.org/10.2139/ssrn.4386146

Zijin Wang

Southwestern University of Finance and Economics ( email )

Peimin Chen (Contact Author)

Shanghai Business School ( email )

2271, Zhongshan Road (W)
Hong Kou District
Shanghai, 200235
China

Peng Liu

Cornell University ( email )

448 Statler Hall
Ithaca, NY 14853
United States
6072542960 (Phone)

Cornell SC Johnson College of Business ( email )

Ithaca, NY 14850
United States

Chunchi Wu

The State University of New York (SUNY) at Buffalo - School of Management ( email )

134 Jacobs Hl
Buffalo, NY 14260
United States

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