Conditional Jump Dynamics in Stock Market Returns
提出一个新的条件跳跃模型，用于研究股票市场收益中的跳跃动态。通过一个简单过滤器推断跳跃的事后分布，并构建自回归条件跳跃强度模型，发现跳跃强度随时间显著变化，且跳跃大小分布也存在时变性。该模型有助于拟合股市波动并捕捉大跌后的反弹。
AbstractThis article develops a new conditional jump model to study jump dynamics in stock market returns. We propose a simple filter to infer ex post the distribution of jumps. This permits construction of the shock affecting the time t conditional jump intensity and is the main input into an autoregressive conditional jump intensity model. The model allows the conditional jump intensity to be time-varying and follows an approximate autoregressive moving average (ARMA) form. The time series characteristics of 72 years of daily stock returns are analyzed using the jump model coupled with a generalized autoregressive conditional heteroscedasticity (GARCH) specification of volatility. We find significant time variation in the conditional jump intensity and evidence of time variation in the jump size distribution. The conditional jump dynamics contribute to good in-sample and out-of-sample fits to stock market volatility and capture the rally often observed in equity markets following a significant downturn.KEY WORDS: Conditional intensityFilterJump size