Filtering and forecasting commodity futures prices under an HMM framework

Date, Paresh; Mamon, Rogemar; Tenyakov, Anton

doi:10.1016/j.eneco.2013.05.016

Cited by 33 publications

(14 citation statements)

References 22 publications

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“…In addition to the above analysis on predictive ability of models, we have carried out an empirical analysis of two cases for two factor model with jumps: when the jump risk premium is set to zero and when jump risk premium is given with formulae (11). Tables 8-9 show this comparison for out-of-sample data sets for MRAE and RMSE errors.…”

Section: Resultsmentioning

confidence: 99%

“…More recently, a regime switching model with different regimes for positive and negative spikes has been proposed in [10] for hourly price forward curves. A regime swiching model is also used in [11] for pricing energy commodity futures, where the authors do not use commodity spot price process and model the evolution of arbitrage-free futures price process directly. A numerical algorithm based on approximating the underlying stochastic process by a continuous time Markov chain was proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

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Electricity futures price models: Calibration and forecasting

Islyaev

Date

2015

European Journal of Operational Research

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A new one factor model with a random volatility parameter is presented in this paper for pricing of electricity futures contracts. It is shown that the model is more tractable than multi-factor jump diffusion models and yields an approximate closed-form pricing formula for the electricity futures prices. On real market data, it is shown that the performance of the new model compares favorably with two existing models in the literature, viz. a two factor jump diffusion model and its jump free version, i.e., a two factor linear Gaussian model, in terms of ability to predict one day ahead futures prices. Further, a multi-stage procedure is suggested and implemented for calibration of the two factor jump diffusion model, which alleviates the difficulty in calibration due to a large number of parameters and pricing formulae which involve numerical evaluation of integrals. We demonstrate the utility of our new model, as well as the utility of the calibration procedure for the existing two factor jump diffusion model, by model calibration and price forecasting experiments on three different futures price data sets from Nord pool electricity data. For the jump diffusion model, we also investigate empirically whether it performs better in terms of futures price prediction than a corresponding, jump-free linear Gaussian model. Finally, we investigate whether an explicit calibration of jump risk premium in the jump diffusion model adds value to the quality of futures price prediction. Our experiments do not yield any evidence that modelling jumps leads to a better price prediction in electricity markets.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electricity futures price models: Calibration and forecasting

Islyaev

Date

2015

European Journal of Operational Research

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“…Proof The proofs follow similar idea to the derivations of the filtering equations in Erlwein and Mamon [12] or Date et al [21].…”

Section: Theorem 1 Let D Be the Matrix Defined In (18) Thenmentioning

confidence: 95%

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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“…For instance, wireless underwater communication channels [38] and vehicular communication channels [165] are time-varying, so that channel identification and equalization on such channels require tracking. The system model of (2.18) has also been applied to tracking financial markets [46], [58], [106], to mobile robotics [144], [147] and many other problems in signal processing and communication [15].…”

Section: The Tracking Problemmentioning

confidence: 99%

Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible.This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler.Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods.Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods.The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems.The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.Thesis Supervisor: James C. Preisig Title: Scientist Emeritus, Woods Hole Oceanographic Institution AcknowledgmentsPiglet noticed that even though he had a Very Small Heart, it could hold a rather large amount of Gratitude. Winnie the PoohA.A.MilneMy years at MIT have been about so much more than a degree. In the past six years you have introduced to more worlds than I ever knew existed. As this long and wonderful journey ends, I find that my heart can, too, hold so much gratitude, for everything that you have given.Of course, I haven't said who "you" is, so here goes.It is impossible to write any list of acknowledgements without having at the very top my thesis advisor Dr. James Preisig, who has truly been a wide-angle lens f...

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Filtering and forecasting commodity futures prices under an HMM framework

Cited by 33 publications

References 22 publications

Electricity futures price models: Calibration and forecasting

Electricity futures price models: Calibration and forecasting

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

Graphical model driven methods in adaptive system identification

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