Volatility Estimation and Jump Detection for drift-diffusion Processes

Sébastien Laurent and Shuping Shi

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Abstract: Logarithms of prices of financial assets are conventionally assumed to follow drift-diffusion processes. While the drift term is typically ignored in the infill asymptotic theory and applications, the presence of nonzero drifts is an undeniable fact. The finite sample theory and extensive simulations provided in this paper reveal that the drift component has a nonnegligible impact on the estimation accuracy of volatility and leads to a dramatic power loss of a class of jump identification procedures. We propose an alternative construction of volatility estimators and jump tests and observe significant improvement of both in the presence of nonnegligible drift. As an illustration, we apply the new volatility estimators and jump tests, along with their original versions, to 21 years of 5-minute log-returns of the NASDAQ stock price index.

Keywords: diffusion process; nonzero drift; finite sample theory; volatility estimation; jumps (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-rmg
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01944449v1
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Related works:
Journal Article: Volatility estimation and jump detection for drift–diffusion processes (2020)
Working Paper: Volatility estimation and jump detection for drift–diffusion processes (2020)
Working Paper: Volatility Estimation and Jump Detection for drift-diffusion Processes (2018)
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