Pricing swing options in the electricity markets under regime-switching uncertainty

Wahab, M.I.M.; Yin, Zhijian; Edirisinghe, N. C. P.

doi:10.1080/14697680903547899

Cited by 28 publications

(20 citation statements)

References 19 publications

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“…Regime switching models have been applied to insurance [20], electricity markets [19,36], natural gas [2], and optimal forestry management [10]. This is considered to better model observed risky asset stochastic processes [20], particularly for options having a longer time frame.…”

Section: Regime Switching: American Optionsmentioning

confidence: 99%

Combined Fixed Point and Policy Iteration for Hamilton--Jacobi--Bellman Equations in Finance

Huang¹,

Labahn²

2012

SIAM J. Numer. Anal.

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Section: Regime Switching: American Optionsmentioning

confidence: 99%

Combined Fixed Point and Policy Iteration for Hamilton--Jacobi--Bellman Equations in Finance

Huang¹,

Labahn²

2012

SIAM J. Numer. Anal.

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“…Facing this problem, many researchers employed the dynamic programming method, which lead to the Bellman's equation for the value function. And to deal with the conditional expectations in the Bellman's equation, different kinds of numerical methods were applied; e.g., tree methods were used in [3,22,23], Monte-Carlo method was used in [24,25], and the finite difference method was used in [26,27]. Besides the dynamic programming method, the method of discretizing the underlying probability space was used in [28] to overcome the difficulty of facing infinitely many cases.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, to characterize the electricity price, we use a seasonal regime-switching mean-reverting model with states, after adding the mean-reverting characteristic into the model adopted in [22]. The motivation to add this modification comes from empirical researches of [6], in which electricity spot prices have been verified to have three basic features, including seasonal pattern, mean-reversion, and spikes.…”

Section: Introductionmentioning

confidence: 99%

“…The motivation to add this modification comes from empirical researches of [6], in which electricity spot prices have been verified to have three basic features, including seasonal pattern, mean-reversion, and spikes. As for the constraints of option, besides usual ones, we take refraction time constraint and a penalty function of more general form into consideration based on [22]. Under these settings, we formulate the pricing of swings as an optimal stochastic control problem subject to several constraints, including the refraction time, the local volume, and the global volume.…”

Section: Introductionmentioning

confidence: 99%

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Valuation of Swing Options under a Regime‐Switching Mean‐Reverting Model

Shao

Xiang

2019

Mathematical Problems in Engineering

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In this paper, we study the valuation of swing options on electricity in a model where the underlying spot price is set to be the product of a deterministic seasonal pattern and Ornstein-Uhlenbeck process with Markov-modulated parameters. Under this setting, the difficulties of pricing swing options come from the various constraints embedded in contracts, e.g., the total number of rights constraint, the refraction time constraint, the local volume constraint, and the global volume constraint. Here we propose a framework for the valuation of the swing option on the condition that all the above constraints are nontrivial. To be specific, we formulate the pricing problem as an optimal stochastic control problem, which can be solved by the trinomial forest dynamic programming approach. Besides, empirical analysis is carried out on the model. We collect historical data in Nord Pool electricity market, extract the seasonal pattern, calibrate the Ornstein-Uhlenbeck process parameters in each regime, and also get market price of risk. Finally, on the basis of calibration results, a specific numerical example concerning all typical constraints is presented to demonstrate the valuation procedure.

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“…Recent research has shown that models based on stochastic volatility, jump diffusion, and regime switching processes produce better fits to market data. A nonexhaustive list of regime switching applications includes insurance [22], electricity markets [21,40], natural gas [12,2], optimal forestry management [11], trading strategies [15], valuation of stock loans [44], convertible bond pricing [3], and interest rate dynamics [27]. Regime switching models are intuitively appealing, and computationally inexpensive compared to a stochastic volatility jump diffusion model.…”

Section: Introductionmentioning

confidence: 99%

Methods for Pricing American Options under Regime Switching

Huang¹,

Forsyth²,

Labahn³

2011

SIAM J. Sci. Comput.

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Abstract. We analyze a number of techniques for pricing American options under a regime switching stochastic process. The techniques analyzed include both explicit and implicit discretizations with the focus being on methods which are unconditionally stable. In the case of implicit methods we also compare a number of iterative procedures for solving the associated nonlinear algebraic equations. Numerical tests indicate that a fixed point policy iteration, coupled with a direct control formulation, is a reliable general purpose method. Finally, we remark that we formulate the American problem as an abstract optimal control problem; hence our results are applicable to more general problems as well. Key words. regime switching, American options, iterative methods AMS subject classifications. 65N06, 93C20DOI. 10.1137/1108209201. Introduction. The standard approach to valuation of contingent claims (also known as derivatives) is to specify a stochastic process for the underlying asset and then construct a dynamic, self-financing hedging portfolio to minimize risk. The initial cost of constructing the portfolio is then considered to be the fair value of the contingent claim. This has been used with great success in the case of stochastic processes having constant volatility in the case of both European and (the more difficult) American options.However, it is well known that a financial model which follows a stochastic process having constant volatility is not consistent with market prices. Recent research has shown that models based on stochastic volatility, jump diffusion, and regime switching processes produce better fits to market data. A nonexhaustive list of regime switching applications includes insurance [22] [3], and interest rate dynamics [27]. Regime switching models are intuitively appealing, and computationally inexpensive compared to a stochastic volatility jump diffusion model.In this paper we study numerical techniques for the solution of American option contracts under regime switching. While our examples focus on problems with constant properties in each regime, the numerical methods developed can easily be applied to cases where the properties in each regime are more complex. An example would be the use of price-dependent regime switching (i.e., default hazard rates) in convertible bond pricing [4]. A number of different methods has been proposed for handling American options under regime switching models. Semianalytic approaches have been

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Pricing swing options in the electricity markets under regime-switching uncertainty

Cited by 28 publications

References 19 publications

Combined Fixed Point and Policy Iteration for Hamilton--Jacobi--Bellman Equations in Finance

Combined Fixed Point and Policy Iteration for Hamilton--Jacobi--Bellman Equations in Finance

Valuation of Swing Options under a Regime‐Switching Mean‐Reverting Model

Methods for Pricing American Options under Regime Switching

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