English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 65231/98744 (66%)
造訪人次 : 31946379      線上人數 : 2508
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/129022


    題名: Non-Parametric Inference on Risk Measures for Integrated Returns.
    作者: Tsai, Henghsiu;Ho, Hwai-Chung;Chen, Hung-Yin
    關鍵詞: Conditional tail expectation;Equality of tail risks;Inference;Integrated process;Quantile;Stochastic volatility model;Test;Value at risk.
    日期: 2025-06-12
    上傳時間: 2026-03-20 12:08:09 (UTC+8)
    出版者: World Scientific Publishing Company, Singapore
    摘要: When evaluating the market risk of long-horizon equity returns, it is always difficult to provide a statistically sound solution due to the limitation of the sample size. To solve the problem for the value-at-risk (VaR) and the conditional tail expectation (CTE), Ho et al. (2016, 2018) introduce a general multivariate stochastic volatility return model from which asymptotic formulas for the VaR and the CTE are derived for integrated returns with the length of integration increasing to infinity. Based on the formulas, simple non-parametric estimators for the two popular risk measures of the long-horizon returns are constructed. The estimates are easy to implement and shown to be consistent and asymptotically normal. In this chapter, we further address the issue of testing the equality of the CTEs of integrated returns. Extensive finite-sample analysis and real data analysis are conducted to demonstrate the efficiency of the test statistics we propose.
    關聯: Handbook of Financial Econometrics, Statistics, Technology, and Risk Management
    DOI: 10.1142/9789819809950_0072
    顯示於類別:[會計學系暨研究所] 專書之單篇

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML81檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋