Limit Moves as Censored Observations of Equilibrium Futures Price in GARCH Processes
提出一种算法，用于估计含有删失观测的时间序列的GARCH模型，该算法适用于存在涨跌停限制的期货市场，通过期望最大化方法处理未观测的均衡价格，并用国库券期货数据验证了其有效性。
Abstract We develop an algorithm for estimating generalized autoregressive conditional heteroscedasticity models for time series containing some censored observations. Motivation for the algorithm comes from those futures markets and some equity markets that have limits constraining the maximum allowable movement in price in a day. When a limit is reached, trading stops and the equilibrium price is not observed. We maximize the likelihood function by replacing the unobservable squared error terms with their expected values. We evaluate the algorithm performance by extensive simulation and apply it to treasury-bill futures data from a period of high volatility and frequent limit moves. KEY WORDS: EM algorithmPrice limitsRational expectations