Modeling Autoregressive Conditional Skewness and Kurtosis with Multi-Quantile CAViaR

• 영문명: Modeling Autoregressive Conditional Skewness and Kurtosis with Multi-Quantile CAViaR
• 발행기관: 한국계량경제학회
• 저자명: Halbert White Tae-Hwan Kim Simone Manganelli
• 간행물 정보: 『한국계량경제학회 학술대회 논문집』2008년 하계학술대회, 1~32쪽, 전체 32쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2008.08.30

영문 초록

Engle and Manganelli (2004) propose CAViaR, a class of models suitable for estimating conditional quantiles in dynamic settings. Engle and Manganelli apply their approach to the estimation of Value at Risk, but this is only one of many possible applications. Here we extend CAViaR models to permit joint modeling of multiple quantiles, Multi-Quantile (MQ) CAViaR. We apply our new methods to estimate measures of conditional skewness and kurtosis de…ned in terms of conditional quantiles, analogous to the unconditional quantile-based measures of skewness and kurtosis studied by Kim and White (2004). We investigate the performance of our methods by simulation, and we apply MQ-CAViaR to study conditional skewness and kurtosis of S&P 500 daily returns.

목차

1 Introduction

2 The MQ-CAViaR Process and Model

3 MQ-CAViaR Estimation: Consistency and Asymptotic Normality

4 Consistent Covariance Matrix Estimation

5 Quantile-Based Measures of Conditional Skewness and Kurtosis

6 Application and Simulation

7 Conclusion

키워드

Asset returns CAViaR Conditional quantiles Dynamic quantiles Kurtosis Skewness