Filtering of a Discrete-Time HMM-Driven Multivariate Ornstein-Uhlenbeck Model With Application to Forecasting Market Liquidity Regimes

Tenyakov, Anton; Mamon, Rogemar; Davison, Matt

doi:10.1109/jstsp.2016.2549499

Cited by 18 publications

(6 citation statements)

References 26 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%

“…Following Erlwein and Mamon [12] or Tenyakov et al [18], if the parameters in the solution of the OU process in Eq. (1) are HMM-driven then…”

Section: Observed and Hidden State Processesmentioning

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%

“…Following Erlwein and Mamon [12] or Tenyakov et al [18], if the parameters in the solution of the OU process in Eq. (1) are HMM-driven then…”

Section: Observed and Hidden State Processesmentioning

confidence: 99%

See 2 more Smart Citations

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

Tenyakov

Mamon

2017

J Big Data

Self Cite

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“…A dynamic estimation, designed to recover switching parameters and the state of the Markov chain, is via the hidden Markov model filtering technique; see for example, Elliott, et al [10] for continuous-time filters, and Erlwein and Mamon [16] for discrete-time filters, as well as Xi and Mamon [35] for the filters involving a higher-order Markov chain in the Hull-White setting. Filters for the parameter estimation of a multivariate Hull-White setting were devised in Tenyakov, et al [31]. The issue of choosing the appropriate martingale measures, akin to valuing bonds under the Vasicek and CIR models with regime switching, is dealt with in Elliott, et al [13].…”

mentioning

confidence: 99%

Bond pricing formulas for Markov-modulated affine term structure models

Rodrigo¹,

Mamon²

2021

JIMO

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This article provides new developments in characterizing the class of regime-switching exponential affine interest rate processes in the context of pricing a zero-coupon bond. A finite-state Markov chain in continuous time dictates the random switching of time-dependent parameters of such processes. We present exact and approximate bond pricing formulas by solving a system of partial differential equations and minimizing an error functional. The bond price expression exhibits a representation that shows how it is explicitly impacted by the rate matrix and the time-dependent coefficient functions of the short rate models. We validate the bond pricing formulas numerically by examining a regime-switching Vasicek model.

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A two-decrement model for the valuation and risk measurement of a guaranteed annuity option

Zhao

Mamon

Huan³

2018

Econometrics and Statistics

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Filtering of a Discrete-Time HMM-Driven Multivariate Ornstein-Uhlenbeck Model With Application to Forecasting Market Liquidity Regimes

Cited by 18 publications

References 26 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

Bond pricing formulas for Markov-modulated affine term structure models

A two-decrement model for the valuation and risk measurement of a guaranteed annuity option

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