A statistical technique for cleaning option price data (2501.11164v1)

Abstract: Recorded option pricing datasets are not always freely available. Additionally, these datasets often contain numerous prices which are either higher or lower than can reasonably be expected. Various reasons for these unexpected observations are possible, including human error in the recording of the details associated with the option in question. In order for the analyses performed on these datasets to be reliable, it is necessary to identify and remove these options from the dataset. In this paper, we list three distinct problems often found in recorded option price datasets alongside means of addressing these. The methods used are justified using sound statistical reasoning and remove option prices violating the standard assumption of no arbitrage. An attractive aspect of the proposed technique is that no option pricing model-based assumptions are used. Although the discussion is restricted to European options, the procedure is easily modified for use with exotic options as well. As a final contribution, the paper contains a link to six option pricing datasets which have already been cleaned using the proposed methods and can be freely used by researchers.

• The paper presents a systematic statistical methodology to clean option price data by identifying and removing entries that violate no-arbitrage principles, are statistical outliers, or are inconsistent duplicates.
• These data cleaning techniques improve the reliability and accuracy of empirical financial research and model validation using real-world option price datasets.
• The non-parametric outlier detection method is model-independent, enhancing its broad applicability across diverse financial option price datasets.

Data Cleaning Techniques for Option Price Datasets

The paper "A statistical technique for cleaning option price data" by I.J.H. Visagie addresses a significant issue in financial analytics: the integrity of recorded option price data. With the ever-increasing reliance on empirical data analyses in developing and validating financial models, ensuring the accuracy and reliability of recorded option prices is crucial. This paper presents a specialized statistical methodology for cleaning option price datasets, ensuring they are free from errors that could lead to inaccurate analyses and potential arbitrage opportunities.

Main Contributions

The paper identifies three common issues in option price datasets and proposes systematic approaches to address each:

1. Arbitrage Opportunities: The paper highlights the need to remove option prices that violate the no-arbitrage principle, which is foundational in finance. Arbitrage opportunities typically arise when the recorded prices fall outside theoretical bounds, as established for European call and put options. Utilizing these bounds, the paper provides a method to systematically identify and eliminate options whose prices suggest the existence of arbitrage opportunities, ensuring the data align with realistic market conditions.
2. Outlier Identification: Outliers in option prices can distort analyses and invalidate assumptions about market behavior. The paper introduces a non-parametric approach using polynomial regression to detect anomalous prices. By fitting a second-degree polynomial to options grouped by time to maturity, residuals are analyzed to identify and remove significant outliers. This method is independent of specific option pricing models, enhancing its applicability across diverse datasets.
3. Duplicated Options: Errors can also manifest as duplicated entries with different prices for options of the same strike price and maturity. Such inconsistencies can lead to arbitrage if not corrected. The paper suggests using open interest as a filtering measure, retaining only the value with the highest open interest, reflecting the most frequently traded (and presumably more reliable) option price.

Practical Implications

The pragmatic considerations of this paper lie in its application to real-world datasets obtained from prominent financial indices and companies such as the S&P 500, PowerShares, and Google Inc. The dataset cleaning techniques ensure that the curated datasets lead to robust and reliable model estimations. By leveraging this cleansed data, financial practitioners and researchers can enhance the calibration and validation of their models without the distortions introduced by erroneous price entries.

Furthermore, an ancillary value contribution is the provision of six cleaned datasets, which are freely accessible to researchers interested in empirical option pricing studies. These datasets reflect the market conditions during a historically volatile period (May 2012), offering opportunities for exploring option pricing behaviors under stress.

Theoretical and Future Directions

The techniques presented enhance theoretical understandings of data consistency in finance through a structured approach to error identification and correction. By focusing on logical consistency (no-arbitrage) and leveraging statistical robustness (non-parametric outlier detection), the proposed framework lays a foundation for subsequent methodological advances in financial dataset cleaning.

Future research may extend these techniques to accommodate other types of options, such as exotic options, or adapt them to evolving market conditions and new financial instruments. Additionally, developing algorithms that integrate machine learning with outlier detection and error correction could further enhance the efficiency and scalability of these cleaning methods in today's data-intensive financial landscape.

In conclusion, Visagie's paper provides a comprehensive methodology for ensuring the integrity of option price data, a critical element for reliable financial research and practice. The absence of model-dependence in the suggested techniques broadens their applicability, offering considerable utility to the financial analytics community.