The paper proposes a new method of dynamic VaR and CVaR risk measures forecasting. The method is designed for obtaining the forecast estimates of risk measures for volatile time series with long range dependence. The ...The paper proposes a new method of dynamic VaR and CVaR risk measures forecasting. The method is designed for obtaining the forecast estimates of risk measures for volatile time series with long range dependence. The method is based on the heteroskedastic time series model. The FIGARCH model is used for volatility modeling and forecasting. The model is reduced to the AR model of infinite order. The reduced system of Yule-Walker equations is solved to find the autoregression coefficients. The regression equation for the autocorrelation function based on the definition of a long-range dependence is used to get the autocorrelation estimates. An optimization procedure is proposed to specify the estimates of autocorrelation coefficients. The procedure for obtaining of the forecast values of dynamic risk measures VaR and CVaR is formalized as a multi-step algorithm. The algorithm includes the following steps: autoregression forecasting, innovation highlighting, obtaining of the assessments for static risk measures for residuals of the model, forming of the final forecast using the proposed formulas, quality analysis of the results. The proposed method is applied to the time series of the index of the Tokyo stock exchange. The quality analysis using various tests is conducted and confirmed the high quality of the obtained estimates.展开更多
园区微能源网在波动的电力现货价格下常面临调度成本的不确定性,易造成额外的损失成本。然而,常用的随机优化手段——典型场景规划、条件风险价值(condition value at risk,CVaR)存在忽略场景合并损失及置信度主观选值的问题。为此,提...园区微能源网在波动的电力现货价格下常面临调度成本的不确定性,易造成额外的损失成本。然而,常用的随机优化手段——典型场景规划、条件风险价值(condition value at risk,CVaR)存在忽略场景合并损失及置信度主观选值的问题。为此,提出兼顾场景相似度与合并损失下的改进场景缩减优化方法,提取典型市场场景集,将场景缩减后的损失度作为置信度的选值依据,形成改进CVaR日前经济调度模型。算例分析表明,基于场景缩减优化引导置信度选值的方法有效促使CVaR值反映实际调度的损失成本,且较主观选值而言更接近理论最低的尾部风险损失,即表明园区微能源网的日前经济调度成本与实际环境更为接近,并进一步讨论了考虑风电不确定性及其他场景缩减方案下的模型推广性。展开更多
文摘The paper proposes a new method of dynamic VaR and CVaR risk measures forecasting. The method is designed for obtaining the forecast estimates of risk measures for volatile time series with long range dependence. The method is based on the heteroskedastic time series model. The FIGARCH model is used for volatility modeling and forecasting. The model is reduced to the AR model of infinite order. The reduced system of Yule-Walker equations is solved to find the autoregression coefficients. The regression equation for the autocorrelation function based on the definition of a long-range dependence is used to get the autocorrelation estimates. An optimization procedure is proposed to specify the estimates of autocorrelation coefficients. The procedure for obtaining of the forecast values of dynamic risk measures VaR and CVaR is formalized as a multi-step algorithm. The algorithm includes the following steps: autoregression forecasting, innovation highlighting, obtaining of the assessments for static risk measures for residuals of the model, forming of the final forecast using the proposed formulas, quality analysis of the results. The proposed method is applied to the time series of the index of the Tokyo stock exchange. The quality analysis using various tests is conducted and confirmed the high quality of the obtained estimates.
文摘园区微能源网在波动的电力现货价格下常面临调度成本的不确定性,易造成额外的损失成本。然而,常用的随机优化手段——典型场景规划、条件风险价值(condition value at risk,CVaR)存在忽略场景合并损失及置信度主观选值的问题。为此,提出兼顾场景相似度与合并损失下的改进场景缩减优化方法,提取典型市场场景集,将场景缩减后的损失度作为置信度的选值依据,形成改进CVaR日前经济调度模型。算例分析表明,基于场景缩减优化引导置信度选值的方法有效促使CVaR值反映实际调度的损失成本,且较主观选值而言更接近理论最低的尾部风险损失,即表明园区微能源网的日前经济调度成本与实际环境更为接近,并进一步讨论了考虑风电不确定性及其他场景缩减方案下的模型推广性。
