当代经济科学
噹代經濟科學
당대경제과학
Modern Economic Science
2011年
5期
109~118
,共null页
已实现波动率 跳跃 波动率预测 金融风险
已實現波動率 跳躍 波動率預測 金融風險
이실현파동솔 도약 파동솔예측 금융풍험
Realized volatility; Jump ; Volatility Forecasting; Financial Risk
金融资产收益率波动是资产定价和金融风险管理的核心部分,而跳跃是收益率波动中的重要组成部分。基于修正z一检验,本文检测识别我国股市波动中跳跃行为,并且研究了跳跃的时序特征,统计结果表明,在市场大波动时期,和连续成份相比,跳跃对于波动率具有极其重要的贡献。建立包含跳跃的已实现波动率非齐次自回归模型,在波动模型中纳入滞后绝对日收益率和杠杆效应预测股指收益率波动。实证分析结果显示,对于短期的波动预测,包含跳跃和两种影响因素的波动模型表现最好,然而对于提前1月的长期预测,跳跃和连续波动成份分离模型预测明显优于其它模型,这些事实说明跳跃对股指波动率预测具有重要的影响,好坏消息对波动率非对称性具有短期显著影响,而对长期水平的波动率预测影响不显著。
金融資產收益率波動是資產定價和金融風險管理的覈心部分,而跳躍是收益率波動中的重要組成部分。基于脩正z一檢驗,本文檢測識彆我國股市波動中跳躍行為,併且研究瞭跳躍的時序特徵,統計結果錶明,在市場大波動時期,和連續成份相比,跳躍對于波動率具有極其重要的貢獻。建立包含跳躍的已實現波動率非齊次自迴歸模型,在波動模型中納入滯後絕對日收益率和槓桿效應預測股指收益率波動。實證分析結果顯示,對于短期的波動預測,包含跳躍和兩種影響因素的波動模型錶現最好,然而對于提前1月的長期預測,跳躍和連續波動成份分離模型預測明顯優于其它模型,這些事實說明跳躍對股指波動率預測具有重要的影響,好壞消息對波動率非對稱性具有短期顯著影響,而對長期水平的波動率預測影響不顯著。
금융자산수익솔파동시자산정개화금융풍험관리적핵심부분,이도약시수익솔파동중적중요조성부분。기우수정z일검험,본문검측식별아국고시파동중도약행위,병차연구료도약적시서특정,통계결과표명,재시장대파동시기,화련속성빈상비,도약대우파동솔구유겁기중요적공헌。건립포함도약적이실현파동솔비제차자회귀모형,재파동모형중납입체후절대일수익솔화강간효응예측고지수익솔파동。실증분석결과현시,대우단기적파동예측,포함도약화량충영향인소적파동모형표현최호,연이대우제전1월적장기예측,도약화련속파동성빈분리모형예측명현우우기타모형,저사사실설명도약대고지파동솔예측구유중요적영향,호배소식대파동솔비대칭성구유단기현저영향,이대장기수평적파동솔예측영향불현저。
Financial asset return volatility is the core for asset pricing and financial risk management while jump is the important component of returns volatility. Based on the corrected Z-statistical test of jump, this paper identifies the jumps behavior in domestic stock market volatility and studies the time series features of jump. Statistical results show that compared with continuous components, jumps contribute significantly to volatility during market turmoil. Heterogeneous autoregressive models including jumps are built, and by incor- porating lagged absolute daily returns and leverage effects, we fit and forecast stock index returns volatility. Empirical analyses indicate that the model incorporating the jumps and the two factors performs best in forecas- ting at short horizon, however, at longer horizon such as one month ahead, the model separating jumps from the continuous components of volatility outperforms the other volatility models. These facts show that jumps have important impact on volatility forecasts of stock index, while good or bad news have short-term significant influence on volatility asymmetry but have no significant effects on long-term horizon forecasts.