Electricity Price Modeling with Stochastic Time Change

Borovkova, Svetlana; Schmeck, Maren Diane

doi:10.2139/ssrn.2685045

Cited by 20 publications

(27 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The value of the seasonal component parameters are shown in Table 4. (27) By using Itô's lemma [61], Equation 24can be solved and we can derive the following form:…”

Section: The Indirect Losses Inmentioning

confidence: 99%

Dynamic business continuity assessment using condition monitoring data

Xing

Zeng

Zio

2019

International Journal of Disaster Risk Reduction

View full text Add to dashboard Cite

Concerns on the impacts of disruptive events of various nature on business operations have increased significantly during the past decades. In this respect, business continuity management (BCM) has been proposed as a comprehensive and proactive framework to prevent the disruptive events from impacting the business operations and reduce their potential damages. Most existing business continuity assessment (BCA) models that numerically quantify the business continuity are time-static, in the sense that the analysis done before operation is not updated to consider the aging and degradation of components and systems which influence their vulnerability and resistance to disruptive events. On the other hand, condition monitoring is more and more adopted in industry to maintain under control the state of components and systems. On this basis, in this work, a dynamic and quantitative method is proposed to integrate in BCA the information on the conditions of components and systems. Specifically, a particle filtering-based method is developed to integrate condition monitoring data on the safety barriers installed for system protection, to predict their reliability as their condition changes due to aging. An installment model and a stochastic price model are also employed to quantify the time-dependent revenues and tolerable losses from operating the system. A simulation model is developed to evaluate dynamic business continuity metrics originally introduced. A case study regarding a nuclear power plant (NPP) risk scenario is worked out to demonstrate the applicability of the proposed approach.

show abstract

“…The value of the seasonal component parameters are shown in Table 4. (27) By using Itô's lemma [61], Equation 24can be solved and we can derive the following form:…”

Section: The Indirect Losses Inmentioning

confidence: 99%

Dynamic business continuity assessment using condition monitoring data

Xing

Zeng

Zio

2019

International Journal of Disaster Risk Reduction

View full text Add to dashboard Cite

show abstract

“…This finding led other authors to suggest that changing the time measure might be a suitable approach for the modeling of asset prices (see, e.g., Ané and Geman, 2000;Geman, 2002;Carr and Wu, 2004). Put differently, it is possible to define a non-equidistant time grid such that asset returns have the same distribution in each interval (see, e.g., Borovkova and Schmeck, 2017). The modified time measure is called business time and can be interpreted as the arrival of new information (see, e.g., Tankov, 2003).…”

Section: Stochastic Time Change and Subordinatorsmentioning

confidence: 99%

Pricing industry loss warranties in a Lévy–Frailty framework

Beer

Braun

Marugg

2019

Insurance: Mathematics and Economics

View full text Add to dashboard Cite

We propose a novel risk-neutral pricing approach for industry loss warranties. In doing so, we explicitly take into account the statistical dependence of the losses on individual policies in the underlying insurance portfolio, caused by the occurrence of a natural catastrophe. Inspired by recent advances in the structured credit literature, we model joint claim events in a Lévy-Frailty framework with a stochastic time change. Event time is driven by rare and large jumps of a compound Poisson subordinator and thus elapses more quickly when a natural catastrophe has struck, leading to a clustering of losses. We estimate the model on historical ILW quotes and obtain encouraging fit statistics.

show abstract

“…Today, energy is traded under free market conditions in many countries, and energy prices show characteristics that are rarely seen in other markets. These are, for example, seasonal effects, jumps with fast mean-reversion, and negative prices, and a large number of models have been produced in the recent years in order to capture those features and investigate their structure; see, among many others, Borovkova and Schmeck (2017); Fanelli and Schmeck (2019); Genoese et al (2010); Kaminski (2013); Kiesel et al (2009), as well as the recent review Deschatre et al (2021).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Mean-Reverting Processes in the Pricing of Options in the Energy Market: An Arithmetic Approach

Schmeck

Schwerin²

2021

Risks

Self Cite

View full text Add to dashboard Cite

In this paper we study the effect that mean-reverting components in the arithmetic dynamics of electricity spot price have on the price of a call option on a swap. Our model allows for seasonal effects, spikes, and negative values of the price of electricity. We show that for sufficiently large delivery periods of the swap contract, the error that one makes by neglecting some of the mean-reverting processes affecting the spot price evolution converges to zero. The decay rate is explicitly calculated. This is achieved by exploiting the additive structure of the electricity price process in order to determine an explicit closed-form formula for the price of the call on a swap. The theoretical analysis is then illustrated via a numerical example.

show abstract

Electricity Price Modeling with Stochastic Time Change

Cited by 20 publications

References 31 publications

Dynamic business continuity assessment using condition monitoring data

Dynamic business continuity assessment using condition monitoring data

Pricing industry loss warranties in a Lévy–Frailty framework

The Effect of Mean-Reverting Processes in the Pricing of Options in the Energy Market: An Arithmetic Approach

Contact Info

Product

Resources

About