Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting

Chen, Fuqi; Mamon, Rogemar; Nkurunziza, Sévérien

doi:10.1007/s10463-017-0610-4

Cited by 10 publications

(25 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method being put forward is more flexible than the Gaussian mean-reverting model, even for the generalised mean-reverting framework considered in Chen et al [13,14]. A mixture of normal models is employed and this features a couple of apparent advantages: (a) the modelling framework and methodology for the non-normal noise case depends only on a combination of already known and simple techniques for the normal case and (b) an easy economic interpretation could be put forward for the sudden changes in the behaviour of the process.…”

Section: Background and Related Workmentioning

confidence: 99%

A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach

Tenyakov

Mamon

2017

J Big Data

Self Cite

View full text Add to dashboard Cite

IntroductionPairs trading is an investment strategy used to exploit financial markets that are out of equilibrium. It consists of a long position in one security and a short position in another security at a predetermined ratio; see Elliott et al. [1]. Traders bet on the direction of the stocks relative to each other. Such a trading strategy is typically employed by hedge fund companies. Deviations in prices are monitored closely and used as basis for changing positions, taking advantage of market inefficiencies to obtain some profits. Hence, financial computing technologies with algorithmic trading capability, such as the one Abstract This paper addresses the problem of designing an efficient platform for pairs-trading implementation in real time. Capturing the stylised features of a spread process, i.e., the evolution of the differential between the returns from a pair of stocks, exhibiting a heavy-tailed mean-reverting process is also dealt with. Likewise, the optimal recovery of time-varying parameters in a return-spread model is tackled. It is important to solve such issues in an integrated manner to carry out the execution of trading strategies in a dynamic market environment. The Kalman and hidden Markov model (HMM) multiregime dynamic filtering approaches are fused together to provide a powerful method for pairs-trading actualisation. Practitioners' considerations are taken into account in the way the new filtering method is automated. The synthesis of the HMM's expectationmaximisation algorithm and Kalman filtering procedure gives rise to a set of self-updating optimal parameter estimates. The method put forward in this paper is a hybridisation of signal-processing algorithms. It highlights the critical role and beneficial utility of data fusion methods. Its appropriateness and novelty support the advancements of accurate predictive analytics involving big financial data sets. The algorithm's performance is tested on historical return spread between Coca-Cola and Pepsi Inc. 's equities. Through a back-testing trade, a hypothetical trader might earn a non-zero profit under the assumption of no transaction costs and bid-ask spreads. The method's success is illustrated by a trading simulation. The findings from this work show that there is high potential to gain when the transaction fees are low, and an investor is able to benefit from the proposed interplay of the two filtering methods. Tenyakov and Mamon J Big Data (2017) Data (2017) 4:46 put forward in this paper, are necessary for the efficient and successful implementation of a pairs trade. RESEARCHWhen the relative performance of the short position is better than that of the long position, this strategy is going to be profitable regardless of stock market regimes. So, a speculator who pairs a long position in a retail stock with a short position in a rival stock or an index ETF could end up with a profit. Pairs trading could be viewed alternatively as a mechanism to hedge both sector and market risks. When there is a financial crisis, two stoc...

show abstract

Section: Background and Related Workmentioning

confidence: 99%

A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach

Tenyakov

Mamon

2017

J Big Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this article, the generalized OU process with a change point in the framework of Chen, Mamon & Nkurunziza () is considered. We propose a new method based on the iterative least‐squared technique to estimate the change point and the unknown coefficients for the case of unknown volatility parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In locating the unknown change point, Nkurunziza & Zhang () showed that the previously proven asymptotic properties also hold for any consistent estimator of the rate of change point. Two consistent methods to compute accurately the unknown change point as well as to identify the existence of the change point via an informational approach were developed in Chen, Mamon & Nkurunziza (). Furthermore, the performance of such consistent methods was theoretically analyzed and validated through a numerical implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the model studied in Chen, Mamon & Nkurunziza () assumes that the volatility component remains a known constant. However, such an assumed setting, as mentioned above, may be less realistic for many time series, especially in the financial sector where varying volatility is rampant given the continued uncertainty caused by market sentiments, political events and regulatory intervention, amongst others.…”

Section: Introductionmentioning

confidence: 99%

“…However, such an assumed setting, as mentioned above, may be less realistic for many time series, especially in the financial sector where varying volatility is rampant given the continued uncertainty caused by market sentiments, political events and regulatory intervention, amongst others. We revisit the OU process in Chen, Mamon & Nkurunziza () and address the estimation problem for a volatility that changes under the assumption of an unknown change point.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Inference for a change‐point problem under an OU setting with unequal and unknown volatilities

2019

Self Cite

View full text Add to dashboard Cite

An Ornstein–Uhlenbeck (OU) process is employed as a versatile model to capture the mean‐reverting and stochastic evolution of many variables in various fields of applications including finance and economics. Within the OU setting, we develop a new estimation method to determine the unknown change‐point location under the assumption that the volatilities before and after the change point in a time series are unequal. Our method hinges on the concept of a weighted least sum of squared errors approach and enhanced by a fusion of an iterative algorithm. The consistency of the change‐point estimator is established. This article highlights a numerical implementation on simulated and observed financial market data demonstrating the significant flexibility and accuracy of our proposed modelling and estimation method. The Canadian Journal of Statistics 48: 62–78; 2020 © 2019 Statistical Society of Canada

show abstract

Inference in a multivariate generalized mean-reverting process with a change-point

Nkurunziza

Lei

2019

Stat Inference Stoch Process

View full text Add to dashboard Cite

Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting

Cited by 10 publications

References 38 publications

A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach

A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach

Inference for a change‐point problem under an OU setting with unequal and unknown volatilities

Inference in a multivariate generalized mean-reverting process with a change-point

Contact Info

Product

Resources

About