Modelling high-frequency FX rate dynamics: A zero-delay multi-dimensional HMM-based approach

Tenyakov, Anton; Mamon, Rogemar; Davison, Matt

doi:10.1016/j.knosys.2016.03.014

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…The increase in the computational time can be explained by the numerical implementation of the new method's structure. Comprehensive details regarding the implementation of the multi-regime filter procedure can be found further in Tenyakov et al [18,23], and Erlwein et al [20].…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…Although the filters in [18] and [23] are HMM-based, they do not fuse Kalman filters as in the case of this paper and their applications are also different. In particular, a onestep delay set up is considered in [18] with application to liquidity risk modelling and forecasting whilst a zero-delay framework is examined in [23] with application to highfrequency foreign exchange rates data.…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…In particular, a onestep delay set up is considered in [18] with application to liquidity risk modelling and forecasting whilst a zero-delay framework is examined in [23] with application to highfrequency foreign exchange rates data.…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…The cost structure needs to be defined and embedded into the procedure. Also, the use of highfrequency data (e.g., [35]) may be considered, given the current practice of hedge funds companies and other institutional investors. Further examination is required on how to produce filtered parameter estimates within their specified constraints without, or at least with minimal, additional numerical optimisation procedures.…”

Section: Discussionmentioning

confidence: 99%

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

Tenyakov

Mamon

2017

J Big Data

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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...

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Section: Preliminary Resultsmentioning

confidence: 99%

Section: Preliminary Resultsmentioning

confidence: 99%

Section: Preliminary Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Tenyakov

Mamon

2017

J Big Data

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“…[15][16][17][18]. Reference [19] proposed a zero-delay MDHMM in terms of the fitting capacity and prediction power to capture the evolution of the foreign exchange rate data under different frequent trading environments. Furthermore, this method demonstrates that MDHMM can better and more accurately characterize the dynamics than HMM does.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Switched Control Systems Under Common‐source Digital Upsets Modeled by MDHMM

Wang

Sun

et al. 2018

Complexity

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This paper proposes the theoretical model to analyze the performance degradation of control systems subject to common-source digital upsets. In this paper, a multidimensional hidden Markov model (MDHMM) is used to characterize the correlated upsets and reveals the relationship between complex environments and stochastic random digital upsets injected into the control systems. These digital upsets coming from artificial complex environments are operated on distributed redundant processing controllers. Furthermore, this paper develops the theoretical analysis model for performance degradation of control systems under common-source digital interferences modeled by MDHMM. Theoretical estimations for different redundant configurations are analyzed. Then corresponding simulation verifications for a specific control system are also conducted in details and compared with the theoretical analysis results. These analyses can help to select an optimal redundant design and provide an example for control systems design. This analysis also helps to balance the performance of system, reliability of system, and costs of system design in applications.

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Online estimation for a predictive analytics platform with a financial-stability-analysis application

Mamon

Duprey

et al. 2021

European Journal of Control

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Modelling high-frequency FX rate dynamics: A zero-delay multi-dimensional HMM-based approach

Cited by 15 publications

References 24 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

Performance Analysis of Switched Control Systems Under Common‐source Digital Upsets Modeled by MDHMM

Online estimation for a predictive analytics platform with a financial-stability-analysis application

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