Does the fear gauge predict downside risk more accurately than econometric models? Evidence from the US stock market
Does the fear gauge predict downside risk more accurately than econometric models? Evidence from the US stock market
复制标题
恐惧指标是否比计量经济学模型更准确地预测下行风险？
DOI：
10.1080/23322039.2016.1220711
复制
发表时间：
2016
影响因子：
1.9
通讯作者：
Chikashi Tsuji
中科院分区：
文献类型：
--
作者：
Chikashi Tsuji
本文对波动率指数(VIX)中包含的信息与几种广义自回归条件异方差(GARCH)模型预测美国股市下行风险的有效性进行了实证比较。我们的主要发现如下。首先，使用一元Logit和分位数回归模型，我们发现前一天的VIX和来自GARCH、指数GARCH(EGARCH)、幂GARCH(PGARCH)和阈值GARCH(TGARCH)模型的预测S&P500的波动率对S指数的大幅下跌具有统计上显著的预测能力。其次，与多元Logit和分位数回归模型的直接比较表明，来自EGARCH、PGARCH和TGARCH模型的波动率预测主导了前一天VIX的预测能力；我们还澄清了来自EGARCH和TGARCH模型的波动率预测能力要强得多。第三，我们的额外检验进一步表明，在预测美国股市下行风险方面，预测波动率指数、波动率指数的预测波动率和波动率指数第一对数差的预测波动率不能超过计量经济模型中的S&P500波动率预测。第四，我们的向量半(Vech)、Baba-Engle-Kraft-kroner(BEKK)、动态条件相关(DCC)和非对称DCC(ADCC)多元GARCH(MGARCH)分析表明，前一天的波动率指数与来自EGARCH或TGARCH模型的波动率预测之间的时变相关性弱于来自EGARCH和TGARCH模型的波动率预测的相关性。最后，我们的Vech-、BEKK-、DCC-和ADCC-MGARCH分析进一步澄清了围绕美国雷曼兄弟破产在所有三个波动性系列中几乎完美的相关性。本文的主要贡献在于阐明了使用计量经济学模型预测波动率相对于VIX预测美国股市下行风险的优势。我们的结果的主要含义是开发有效的技术模型的重要性，以及在实践中使用计量经济学模型波动性预测的必要性。
Abstract This paper empirically compares the usefulness of information included in the volatility index (VIX) against several generalized autoregressive conditional heteroskedasticity (GARCH) models for predicting downside risk in the US stock market. Our main findings are as follows. First, using the univariate logit and quantile regression models, we reveal that the previous day’s VIX and the forecast S&P 500 volatilities from GARCH, exponential GARCH (EGARCH), power GARCH (PGARCH), and threshold GARCH (TGARCH) models have statistically significant predictive power for large declines in the S&P 500. Second, direct comparisons with the multiple logit and quantile regression models demonstrate that the volatility forecasts from the EGARCH, PGARCH, and TGARCH models dominate the predictive power of the previous day’s VIX; and we also clarify that the predictive power of volatility forecasts from the EGARCH and TGARCH models is much stronger. Third, our additional tests further suggest that the forecast VIX, the forecast volatility of VIX, and the forecast volatility of the first log differences of VIX cannot outperform the S&P 500 volatility forecasts from econometric models in predicting US stock market downside risk. Fourth, our vector-half (VECH), Baba–Engle–Kraft–Kroner (BEKK), dynamic conditional correlation (DCC), and asymmetric DCC (ADCC) multivariate GARCH (MGARCH) analyses demonstrate that the time-varying correlations between the previous day’s VIX and the volatility forecasts from the EGARCH or TGARCH models are weaker than the correlations of volatility forecasts from the EGARCH and TGARCH models. Finally, our VECH-, BEKK-, DCC-, and ADCC-MGARCH analyses further clarify almost perfect correlations around the US Lehman Brothers bankruptcy across all three volatility series. The key contribution of this paper is that it clarifies the superiority of volatility forecasts using econometric models compared with VIX in predicting the US stock market downside risk. The primary implications of our results are the importance of developing effective technical models and the need to use econometric model volatility forecasts in practice.
DOI：
10.1163/1570-6664_iyb_sim_org_2241
发表时间：
2024-01
期刊：
International Journal of Social Science, Innovation and Educational Technologies
影响因子：
--
作者：
Canan Gürbüz
通讯作者：
Canan Gürbüz
影响因子：
6.1
作者：
ENGLE, RF;LILIEN, DM;ROBINS, RP
通讯作者：
ROBINS, RP