Abstract
In this paper, we discuss the problems of arbitrage detection, which is known as change point detection in statistics. There are some classical methods for change point detection, such as the cumulative sum (CUSUM) procedure. However, when utilizing CUSUM, we must be sure about the model of the data before detecting. We introduce a new method to detect the change points by using Hilbert–Huang transformation (HHT) to devise a new algorithm. This new method (called the HHT test in this paper) has the advantage in that no model assumptions are required. Moreover, in some cases, the HHT test performs better than the CUSUM test, and has better simulation results. In the end, an empirical study of the volatility change based on the S&P 500 is also given for illustration.
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Fuh, CD., Pai, SY. (2010). Arbitrage Detection from Stock Data: An Empirical Study. In: Lee, CF., Lee, A.C., Lee, J. (eds) Handbook of Quantitative Finance and Risk Management. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77117-5_106
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DOI: https://doi.org/10.1007/978-0-387-77117-5_106
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-77116-8
Online ISBN: 978-0-387-77117-5
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