北京师范大学学报(自然科学版)
北京師範大學學報(自然科學版)
북경사범대학학보(자연과학판)
JOURNAL OF BEIJING NORMAL UNIVERSITY
2014年
4期
384-389
,共6页
VaR长记忆%ARMA模型%ARFIMA模型
VaR長記憶%ARMA模型%ARFIMA模型
VaR장기억%ARMA모형%ARFIMA모형
VaR long memory%ARMA model%ARFIMA model
提出了 VaR时间序列动态预测的方法。首先以上证综合指数和深证综合指数日内分钟数据为基础,根据不同方法计算出每日 VaR 值,然后给出了 VaR 时间序列的统计特征,包括平稳性和长记忆性,最后对 VaR 序列建立ARMA模型和 ARFIMA模型,并比较了两种模型预测效果。我们的结果表明:1)基于德尔塔正态法的 VaR 序列其ARMA模型预测效果好于历史模拟法和蒙特卡洛模拟法的预测效果;2)尽管 VaR序列存在长记忆性,但所有 VaR序列的 ARMA模型预测效果好于 ARFIMA模型的预测效果。
提齣瞭 VaR時間序列動態預測的方法。首先以上證綜閤指數和深證綜閤指數日內分鐘數據為基礎,根據不同方法計算齣每日 VaR 值,然後給齣瞭 VaR 時間序列的統計特徵,包括平穩性和長記憶性,最後對 VaR 序列建立ARMA模型和 ARFIMA模型,併比較瞭兩種模型預測效果。我們的結果錶明:1)基于德爾塔正態法的 VaR 序列其ARMA模型預測效果好于歷史模擬法和矇特卡洛模擬法的預測效果;2)儘管 VaR序列存在長記憶性,但所有 VaR序列的 ARMA模型預測效果好于 ARFIMA模型的預測效果。
제출료 VaR시간서렬동태예측적방법。수선이상증종합지수화심증종합지수일내분종수거위기출,근거불동방법계산출매일 VaR 치,연후급출료 VaR 시간서렬적통계특정,포괄평은성화장기억성,최후대 VaR 서렬건립ARMA모형화 ARFIMA모형,병비교료량충모형예측효과。아문적결과표명:1)기우덕이탑정태법적 VaR 서렬기ARMA모형예측효과호우역사모의법화몽특잡락모의법적예측효과;2)진관 VaR서렬존재장기억성,단소유 VaR서렬적 ARMA모형예측효과호우 ARFIMA모형적예측효과。
A method is proposed to predict dynamically the VaR time series.Daily VaR value was calculated differently based on daily data in min of Shanghai Composite Index and Shenzhen Composite Index. Statistic features of VaR time series were given,including stability and long-term memory characteristics. ARMA model and ARFIMA model were then built from VaR time series and the two models were compared to find the best predicted result.Analysis indicated that ARMA model of VaR time series based on delta-normal method had better predictive effect than that based on historical simulation method and Monte Carlo simulation method;Although VaR time series was of long memory,ARMA model of VaR time series demonstrated better predictive result than ARFIMA model.