教師資料查詢 | 類別: 期刊論文 | 教師: 陳虹吟CHEN, HUNG-YIN (瀏覽個人網頁)

標題:Non-parametric Estimation of Conditional Tail Expectation for Long-Horizon Returns
學年109
學期1
出版(發表)日期2021/01/01
作品名稱Non-parametric Estimation of Conditional Tail Expectation for Long-Horizon Returns
作品名稱(其他語言)
著者Hwai-Chung Ho; Hung-Yin Chen; Henghsiu Tsai
單位
出版者
著錄名稱、卷期、頁數Statistica Sinica 31, p.547-569
摘要When evaluating the tail risk of stock portfolio returns, providing statistically sound solutions for long return horizons is important, but difficult. Furthermore, there are drawbacks to using traditional parametric methods that rely on
strong model assumptions or simulations. This study investigates the problem by
focusing on an important risk measure, the conditional tail expectation (CTE), under a general multivariate stochastic volatility model. To overcome the estimation
difficulties caused by the long period, we derive an asymptotic formula to approximate the CTE. Based on this formula, we propose a simple nonparametric estimate
of the unconditional CTE, and show that it is both consistent and asymptotically
normal. Next, we forecast the CTE using a modified form of the nonparametric
estimator. With the help of the asymptotic formula, we evaluate the accuracy of
the CTE predictor by treating it as an interval forecast for furure returns. Simulation studies demonstrate the applicability of our approach. Lastly, we apply the
proposed estimation and predictor to daily S&P 500 index returns.
關鍵字Asymptotic normality; conditional tail expectation; integrated process; interval forecast; long-horizon returns; stochastic volatility model
語言英文
ISSN1017-0405
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,電子版,紙本
SDGs
Google+ 推薦功能,讓全世界都能看到您的推薦!