Prediction regions for interval‐valued time series
针对区间值时间序列，提出用双变量向量自回归模型拟合中心/对数极差系统，再转化为中心/极差和上下界系统的预测区域，蒙特卡洛模拟显示自助法更优，并用标普500高低回报数据展示了其交易策略的盈利性。
Summary We approximate probabilistic forecasts for interval‐valued time series by offering alternative approaches. After fitting a possibly non‐Gaussian bivariate vector autoregression (VAR) model to the center/log‐range system, we transform prediction regions (analytical and bootstrap) for this system into regions for center/range and upper/lower bounds systems. Monte Carlo simulations show that bootstrap methods are preferred according to several new metrics. For daily S&P 500 low/high returns, we build joint conditional prediction regions of the return level and volatility. We illustrate the usefulness of obtaining bootstrap forecasts regions for low/high returns by developing a trading strategy and showing its profitability when compared to using point forecasts.