De-noising option prices with the wavelet method

Haven, Emmanuel; Liu, Xiaoquan; Shen, Li-Jiuan

doi:10.1016/j.ejor.2012.04.020

Cited by 83 publications

(48 citation statements)

References 43 publications

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“…2(c)). Such tiny fluctuations are usually considered as the "contamination" of financial data [8]. The "noisy" part is where the manipulation patterns occur.…”

Section: B Additional Featuresmentioning

confidence: 99%

“…Therefore, retrieving "noisy" short-term oscillation information from the price is crucial for detecting particular patterns. The wavelet analysis feature of separating the low and high frequency components of a signal while localising the high frequency components in time enables the application in the fields of economics and finance for de-noising financial time series [8]. The power of the wavelet method in analysing frequency components of a signal and localising components in time could be utilised for feature extraction.…”

Section: B Additional Featuresmentioning

confidence: 99%

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A Hidden Markov Model with Abnormal States for Detecting Stock Price Manipulation

Cao

Coleman

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Price manipulation refers to the act of using illegal trading behaviour to manually change an equity price with the aim of making profits. With increasing volumes of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. Effective approaches for analysing and real-time detection of price manipulation are yet to be developed. This paper proposes a novel approach, called Hidden Markov Model with Abnormal States (HMMAS), which models and detects price manipulation activities. Together with the wavelet decomposition for features extraction and Gaussian Mixture Model for Probability Density Function (PDF) construction, the HMMAS model detects price manipulation and identifies the type of the detected manipulation. Evaluation experiments of the model were conducted on six stock tick data from NASDAQ and London Stock Exchange (LSE). The results showed that the proposed HMMAS model can effectively detect price manipulation patterns.

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“…2(c)). Such tiny fluctuations are usually considered as the "contamination" of financial data [8]. The "noisy" part is where the manipulation patterns occur.…”

Section: B Additional Featuresmentioning

confidence: 99%

Section: B Additional Featuresmentioning

confidence: 99%

A Hidden Markov Model with Abnormal States for Detecting Stock Price Manipulation

Cao

Coleman

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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show abstract

“…This procedure may be carried out because the zeros added up do not affect the calculation of the WCs ̃ and ̃ generate in (2) (see e.g. [17]), preserving the auto-correlation and its components, in (2), for all t, where . After obtaining the WCs in (2), that is, ̃ and ̃ ( , ..., ( )), they are individually modeled by an adequate ARIMA-GARCH model in order to produce their out-of-sample forecasts.…”

Section: Wavelet Decomposition Of Level Rmentioning

confidence: 99%

Time series forecasting with the WARIMAX-GARCH method

et al. 2016

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Time series forecasting with the WARIMAX-GARCH method, Neurocomputing, http://dx.doi.org/10. 1016/j.neucom.2016.08.046 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractIt is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet "EVs" (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting.

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“…Perturbations and inaccuracies often impair financial time series data (Haven, Liu, and Shen, 2012). The Kalman filter represents a recursive approach to linear filtering problems with discrete data (Kalman, 1960).…”

Section: Price Decompositionmentioning

confidence: 99%

“…The Kalman filter decomposes discrete datasets, such as time series of prices, into both a de-noised fundamental price and a noise component. Utilizing the Kalman filter to decompose market prices is a widely-used approach for financial time series (Brogaard, Hendershott, and Riordan, 2014;Haven, Liu, and Shen, 2012;Hendershott and Menkveld, 2014;Hendershott, Menkveld, Li, and Seasholes, 2013;Lopes and Tsay, 2011;Schwartz and Smith, 2000;Wong, 2010). We describe the mechanisms of the Kalman filter in the following.…”

Section: Price Decompositionmentioning

confidence: 99%