scholarly journals Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6368
Author(s):  
Laís Domingues Leonel ◽  
Mateus Henrique Balan ◽  
Dorel Soares Ramos ◽  
Erik Eduardo Rego ◽  
Rodrigo Ferreira de Mello
Keyword(s):  
Stochastic Optimization ◽  
Utility Function ◽  
Value At Risk ◽  
Financial Risk ◽  
Conditional Value At Risk ◽  
Spot Price ◽  
Model Formulation ◽  
Wholesale Market ◽  
Market Intelligence ◽  
Risk Metric

In the competitive electricity wholesale market, decisions regarding hydro generators are generally made under uncertain conditions, such as pool price, hydrological affluence, and other players’ strategies. From this perspective, this work presents a computational model formulation with associated market intelligence and game theory tools to support a decision-making process in a competitive environment. The idea behind using a market intelligence tool is to apply a stochastic optimization model with an associated conditional value at risk metric defining a utility function, which calculates the weight that the agents attribute to each stochastic variable associated with the problem to be faced. Subsequently, this utility function is used to emulate the other agents’ strategies based on their previous decisions. The final step finds the Nash equilibrium solution between a player and their competitors. The methodology is applied to the monthly allocation of firm energy by hydro generators under the current Brazilian regulatory framework. The results show a change in the generators’ behavior over the years, from risk-neutral agents seeking to maximize their return with 88% of decisions based on spot price forecasts in 2015, to risk-averse agents with 100% of decisions following a factor that is directly impacted by the hydrological affluence forecasts in 2018.

LOCAL HEDGING OF VARIABLE ANNUITIES IN THE PRESENCE OF BASIS RISK

2018 ◽  
Vol 48 (02) ◽  
pp. 611-646 ◽  
Author(s):  
Denis-Alexandre Trottier ◽  
Frédéric Godin ◽  
Emmanuel Hamel
Keyword(s):  
Regime Switching ◽  
Value At Risk ◽  
Capital Requirements ◽  
Optimization Criterion ◽  
Local Optimization ◽  
Conditional Value At Risk ◽  
Variable Annuities ◽  
Basis Risk ◽  
Hedging Strategies ◽  
Risk Metric

AbstractA method to hedge variable annuities in the presence of basis risk is developed. A regime-switching model is considered for the dynamics of market assets. The approach is based on a local optimization of risk and is therefore very tractable and flexible. The local optimization criterion is itself optimized to minimize capital requirements associated with the variable annuity policy, the latter being quantified by the Conditional Value-at-Risk (CVaR) risk metric. In comparison to benchmarks, our method is successful in simultaneously reducing capital requirements and increasing profitability. Indeed the proposed local hedging scheme benefits from a higher exposure to equity risk and from time diversification of risk to earn excess return and facilitate the accumulation of capital. A robust version of the hedging strategies addressing model risk and parameter uncertainty is also provided.

scholarly journals Risk Analysis through the Half-Normal Distribution

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2080
Author(s):  
Maria-Teresa Bosch-Badia ◽  
Joan Montllor-Serrats ◽  
Maria-Antonia Tarrazon-Rodon
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Normal Distribution ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Risk Indicators ◽  
Conditional Value At Risk ◽  
Risk Matrices

We study the applicability of the half-normal distribution to the probability–severity risk analysis traditionally performed through risk matrices and continuous probability–consequence diagrams (CPCDs). To this end, we develop a model that adapts the financial risk measures Value-at-Risk (VaR) and Conditional Value at Risk (CVaR) to risky scenarios that face only negative impacts. This model leads to three risk indicators: The Hazards Index-at-Risk (HIaR), the Expected Hazards Damage (EHD), and the Conditional HIaR (CHIaR). HIaR measures the expected highest hazards impact under a certain probability, while EHD consists of the expected impact that stems from truncating the half-normal distribution at the HIaR point. CHIaR, in turn, measures the expected damage in the case it exceeds the HIaR. Therefore, the Truncated Risk Model that we develop generates a measure for hazards expectations (EHD) and another measure for hazards surprises (CHIaR). Our analysis includes deduction of the mathematical functions that relate HIaR, EHD, and CHIaR to one another as well as the expected loss estimated by risk matrices. By extending the model to the generalised half-normal distribution, we incorporate a shape parameter into the model that can be interpreted as a hazard aversion coefficient.

scholarly journals Single-stage gradient-based stellarator coil design: stochastic optimization

2021 ◽  
Author(s):  
Florian Wechsung ◽  
Andrew Giuliani ◽  
M. Landreman ◽  
Antoine J Cerfon ◽  
Georg Stadler
Keyword(s):  
Stochastic Optimization ◽  
Value At Risk ◽  
Optimization Problems ◽  
Numerical Study ◽  
Single Stage ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Coil Design ◽  
Risk Neutral ◽  
Gradient Based

Abstract We extend the single-stage stellarator coil design approach for quasi-symmetry on axis from [Giuliani et al, 2020] to additionally take into account coil manufacturing errors. By modeling coil errors independently from the coil discretization, we have the flexibility to consider realistic forms of coil errors. The corresponding stochastic optimization problems are formulated using a risk-neutral approach and risk-averse approaches. We present an efficient, gradient-based descent algorithm which relies on analytical derivatives to solve these problems. In a comprehensive numerical study, we compare the coil designs resulting from deterministic and risk-neutral stochastic optimization and find that the risk-neutral formulation results in more robust configurations and reduces the number of local minima of the optimization problem. We also compare deterministic and risk-neutral approaches in terms of quasi-symmetry on and away from the magnetic axis, and in terms of the confinement of particles released close to the axis. Finally, we show that for the optimization problems we consider, a risk-averse objective using the Conditional Value-at-Risk leads to results which are similar to the risk-neutral objective.

scholarly journals Risk assessment for ancillary services

2019 ◽  
Vol 9 (3) ◽  
pp. 1561
Author(s):  
Omer Hadzic ◽  
Smajo Bisanovic
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Power System ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Ancillary Services ◽  
Conditional Value At Risk ◽  
Risk Optimization ◽  
System Risk

The power trading and ancillary services provision comprise technical and financial risks and therefore require a structured risk management. Focus in this paper is on financial risk management that is important for the system operator faces when providing and using ancillary services for balancing of power system. Risk on ancillary services portfolio is modeled through value at risk and conditional value at risk measures. The application of these risk measures in power system is given in detail to show how to using the risk concept in practice. Conditional value at risk optimization is analysed in the context of portfolio selection and how to apply this optimization for hedging a portfolio consisting of different types of ancillary services.

Risk Asset Portfolio Choice Models under Three Risk Measures

2011 ◽  
Vol 204-210 ◽  
pp. 537-540
Author(s):  
Yu Ling Wang ◽  
Jun Hai Ma ◽  
Yu Hua Xu
Keyword(s):  
At Risk ◽  
Portfolio Choice ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Empirical Support ◽  
Real Data ◽  
Conditional Value At Risk ◽  
Asset Portfolio ◽  
Special Value

Mean-variance model, value at risk and Conditional Value at Risk are three chief methods to measure financial risk recently. The demonstrative research shows that three optional questions are equivalence when the security rates have a multivariate normal distribution and the given confidence level is more than a special value. Applications to real data provide empirical support to this methodology. This result has provided new methods for us about further research of risk portfolios.

scholarly journals Strategic Wholesale Pricing for an Incumbent Supplier Facing with a Competitive Counterpart

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jianwu Sun
Keyword(s):  
Risk Management ◽  
Value At Risk ◽  
Financial Risk ◽  
Wholesale Price ◽  
Pricing Strategy ◽  
Conditional Value At Risk ◽  
Profit Function ◽  
Financial Risk Management ◽  
Future Loss ◽  
Wholesale Pricing

We introduce a wholesale pricing strategy for an incumbent supplier facing with a competitive counterpart. We propose a profit function which considers both the present loss and future loss from a wholesale price and then study the optimal wholesale prices for different objectives about this profit function for the incumbent supplier. First, we achieve an optimal wholesale price for the incumbent supplier to maximize his expected profit. Then, to reduce the risk originating from the fluctuation in the competitive supplier’s wholesale price, we integrate the conditional value-at-risk (CVaR) measure in financial risk management into this study and derive an optimal wholesale price to maximize CVaR about profit for the incumbent supplier. Besides, the properties of the two optimal wholesale prices are discussed. Finally, some management insights are suggested for the incumbent supplier in a competitive setting.

Extended Robust Support Vector Machine Based on Financial Risk Minimization

2014 ◽  
Vol 26 (11) ◽  
pp. 2541-2569 ◽  
Author(s):  
Akiko Takeda ◽  
Shuhei Fujiwara ◽  
Takafumi Kanamori
Keyword(s):  
At Risk ◽  
Support Vector Machine ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measure ◽  
Conditional Value At Risk ◽  
Support Vector ◽  
Risk Minimization ◽  
Hinge Loss ◽  
Margin Distribution

Financial risk measures have been used recently in machine learning. For example, [Formula: see text]-support vector machine ([Formula: see text]-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than [Formula: see text]-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM’s possibility of achieving a better prediction performance with proper parameter setting.

scholarly journals Comparison of Electricity Spot Price Modelling and Risk Management Applications

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4698
Author(s):  
Ethem Çanakoğlu ◽  
Esra Adıyeke
Keyword(s):  
Risk Management ◽  
Regime Switching ◽  
Value At Risk ◽  
Optimization Problems ◽  
Management Tool ◽  
Conditional Value At Risk ◽  
Spot Price ◽  
Hedging Strategies ◽  
Regime Switching Models ◽  
Switching Models

In dealing with sharp changes in electricity prices, contract planning is considered as a vital risk management tool for stakeholders in deregulated power markets. In this paper, dynamics of spot prices in Turkish electricity market are analyzed, and predictive performance of several models are compared, i.e., time series models and regime-switching models. Different models for derivative pricing are proposed, and alternative portfolio optimization problems using mean-variance optimization and conditional value at risk (CVaR) are solved. Expected payoff and risk structure for different hedging strategies for a hypothetical electricity company with a given demand are analyzed. Experimental studies show that regime-switching models are able to capture electricity characteristics better than their standard counterparts. In addition, evaluations with various risk management models demonstrate that those models are highly competent in providing an effective risk control practice for electricity markets.

Stochastic Optimal Dispatch Model Considering Security Risk Constraint

2012 ◽  
Vol 591-593 ◽  
pp. 2603-2606
Author(s):  
Xiong Zhou ◽  
Hong Ming Yang ◽  
Jia Jie Wu ◽  
Bao Ping Liu
Keyword(s):  
At Risk ◽  
Power System ◽  
Stochastic Optimization ◽  
Transmission Line ◽  
Value At Risk ◽  
Sample Average Approximation ◽  
Conditional Value At Risk ◽  
Security Risk ◽  
Optimal Dispatch ◽  
Risk Constraint

Due to the natural disasters, the transmission line fault has randomness. In order to describe the uncertainty of the transmission line fault, the conditional value-at-risk (CVaR) theory is introduced to quantify the uncertainty risk caused by transmission line failure. A stochastic optimal dispatch model of the power system considering the security risk constraint is proposed in this paper, using the sample average approximation (SAA) method and the analytic method to solve the model. The influence on the result of stochastic optimization dispatching is analysed by different conditional value at risk and confidence levels. The simulations demonstrate that the stochastic optimal dispatch model considering the security risk constraint is reasonable and provides a theoretical basis for the stochastic optimization dispatching of the power system considering line failure.

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