Algorithmic estimation of risk factors in financial markets with stochastic drift

Hernández, Janko; Saunders, David; Seco, Luis

doi:10.1016/j.cor.2010.09.007

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…In theory, by considering the dominating volatility and market deregulation, these models should be able to provide a reliable representation of the evolution of electricity prices. One of these models, is the three-factor model for energy and commodity spot price [5]. Now consider the following three-factor model:…”

Section: The Ou Process With Nonlinear Ar Drift Term and A Dependent ...mentioning

confidence: 99%

“…Two-factor models have been used through either permitting the long-run mean or the volatility ruled via a stochastic differential equation. Some numerical implementation of these models can be found in [3][4][5]. Modeling electricity price processes is presently an energetic space of educational analysis.…”

Section: Introductionmentioning

confidence: 99%

“…A functional estimation procedure for homogeneous SDE based on a discrete sample of observation has been studied by Bandi and Philips [22]. Hernandez et al (2012) developed a moment method algorithm for the estimation of the parameters of both the observable process and the unobservable stochastic mean [5]. A maximum likelihood (ML) approach to estimate the parameters of a one-dimensional stationary process of the Ornstein-Uhlenbeck type has been proposed by Valdivieso et al (2009), [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Three-factor mean reverting Ornstein-Uhlenbeck process with stochastic drift term innovations: Nonlinear autoregressive approach with dependent error

Nabati

Hajrajabi

2022

Filomat

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This paper introduces a novel approach, withen the context of energy market, by employing a three-factor mean reverting Ornstein-Uhlenbeck process with a stochastic nonlinear autoregressive drift term having a dependent error. Initially the unique solvability for the given nonlinear system is investigated. Then, to estimate the nonlinear regression function, a semiparametric method, based on the conditional least square estimator for the parametric approach, and the nonparametric kernel method for autoregressive modification estimation have been presented . A maximum likelihood estimator has been used for parameter estimation of the Ornstein-Uhlenbeck process. Finally, some numerical simulations and real data studies have been provided to support the main conclusions of the study.

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Section: The Ou Process With Nonlinear Ar Drift Term and A Dependent ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-factor mean reverting Ornstein-Uhlenbeck process with stochastic drift term innovations: Nonlinear autoregressive approach with dependent error

Nabati

Hajrajabi

2022

Filomat

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“…Applications and analysis of some of these models can be found in Chen and Huang (2013), Chen and Hu (2010), Chiarella, Clewlow, and Musti (2005), Chiarella, Fanelli, and Musti (2011), Date and Wang (2009), de Frutos (2008), Falini (2010, Hainaut (2009), Hernández, Saunders, andSeco (2012), Mitra, Date, Mamon, and Wang (2013), and Weissensteiner (2010), among others. Excellent literature reviews on term structure TSIR models can be seen in some books as, for instance, Andersen and Piterbarg (2010), Brigo and Mercurio (2006), Cairns (2004), Filipović (2009), Hunt and Kennedy (2004), James and Webber (2001), Munk (2011), Nawalkha, Believa, andSoto (2007), and Rebonato (1998) or papers as Boero and Torricelli (1996), Schmidt (2011), or Vetzal (1994, among others.…”

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confidence: 97%

A cyclical square-root model for the term structure of interest rates

Moreno

Platania

2015

European Journal of Operational Research

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Valuation of commodity derivatives with an unobservable convenience yield

Lai

Mellios

2016

Computers & Operations Research

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International audienceThis paper extends the existing literature on commodity derivatives to account for an unobservable stochastic convenience yield. Investors operate in an economy with incomplete information. In contrast to other incomplete information models, analytical formulas for forward and futures prices, as well as for European options on forward and futures contracts are obtained. These formulas reveal the important role played by the initial values of the estimator of the convenience yield and of the estimation error respectively when valuing commodity derivatives. We estimate Schwartz׳s [11] model and the incomplete information model based on the discrete-time Kalman filtering method. For futures prices, the latter seems to perform better than the former. Moreover, Schwartz׳s model provides higher option prices than the incomplete information model. The most important differences are obtained for higher futures prices and for longer options maturities

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Algorithmic estimation of risk factors in financial markets with stochastic drift

Cited by 4 publications

References 13 publications

Three-factor mean reverting Ornstein-Uhlenbeck process with stochastic drift term innovations: Nonlinear autoregressive approach with dependent error

Three-factor mean reverting Ornstein-Uhlenbeck process with stochastic drift term innovations: Nonlinear autoregressive approach with dependent error

A cyclical square-root model for the term structure of interest rates

Valuation of commodity derivatives with an unobservable convenience yield

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