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CAViaR Model Selection Via Adaptive Lasso

Author

Listed:
  • Zongwu Cai

    (Department of Economics, University of Kansa, Lawrence, KS 66045, USA)

  • Ying Fang

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China)

  • Dingshi Tian

    (Department of Statistics, School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China)

Abstract

The estimation and model selection of conditional autoregressive value at risk (CAViaR) model may be computationally intensive and even impractical when the true order of the quantile autoregressive components or the dimension of the other regressors are high. On the other hand, automatic variable selection methods cannot be directly applied to this problem because the quantile lag components are latent. In this paper, we propose to identify the optimal CAViaR model using a two-step approach. The estimation procedure consists of an approximation of the conditional quantile in the first step, followed by an adaptive Lasso penalized quantile regression of the regressors as well as the estimated quantile lag components in the second step. We show that under some mild regularity conditions, the proposed adaptive Lasso penalized quantile estimators enjoy the oracle properties. Finally, the proposed method is illustrated by Monte Carlo simulation study and applied to analyzing the daily data of the S&P500 return series.

Suggested Citation

  • Zongwu Cai & Ying Fang & Dingshi Tian, 2024. "CAViaR Model Selection Via Adaptive Lasso," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202403, University of Kansas, Department of Economics, revised Jan 2024.
    • Handle: RePEc:kan:wpaper:202403
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    File URL: http://www2.ku.edu/~kuwpaper/2024Papers/202403.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    CAViaR model; Adaptive Lasso; Model selection; Tail risk.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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