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Detecting Intra-Day Jumps in Stock Prices with High-Frequency Option Data

We develop a novel, option-based approach for detecting intraday jumps in stock prices. One of the components involved in intraday jump detection is instantaneous volatility, by which intraday returns are scaled. The existing intraday jump detection approaches assume that volatility does not change drastically over a short period, which, however, is in conflict with empirical evidence that volatility can exhibit large movements. We tackle this problem by introducing a method with a completely new proxy for instantaneous volatility, extracted from the option-implied volatility index. This allows us to detect jumps while there are large movements in volatility. We verify our approach with extensive ex-post Monte Carlo experiments. The results show that our approach is of high statistical power, is more robust to variation in volatility, and outperforms the baseline approaches from the literature in terms of both spurious and actual jumps. In the empirical part, we use eight years of high-frequency data on S&P 500 index options. We find that, in comparison with the conventional baseline approaches, our approach identifies fewer jumps, suggesting that true price variation coming from jumps is overstated. Moreover, our method identifies different locations for a large portion of jumps, which emphasizes the important role played by the volatility proxy.