Risk assessment for ancillary services

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.

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Risk Analysis through the Half-Normal Distribution

Mathematics ◽

10.3390/math8112080 ◽

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.

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Risk Asset Portfolio Choice Models under Three Risk Measures

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.537 ◽

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.

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Концепт параметра вриједности при ризику и контрола ризика у Црној Гори // The concept of value at risk (VaR) and risk regulatory in Montenegro

ACTA ECONOMICA ◽

10.7251/ace1421105c ◽

2014 ◽

Vol 12 (21) ◽

pp. 105

Author(s):

Јулија Церовић

Keyword(s):

At Risk ◽

Risk Management ◽

Value At Risk ◽

Financial Risk ◽

Financial Time Series ◽

Risk Measures ◽

Risk Control ◽

Banking System ◽

Risk Characteristics ◽

Managing Risk

Резиме: Концепт вриједности при ризику (Value at risk - VaR) је мјера која се све више користи за оцјену степена изложености ризику учесника на финансијским тржиштима. Циљ овог концепта који је почео да преовладава у свијету управљања ризиком од 1994. године, јесте оцјена максималног губитка финансијске позиције у одређеном временском периоду за дату вјероватноћу. Постоји велики број мјера које квантификују ризик, и циљ рада је да се ове мјере изложе, са посебним акцентом на VaR. Такође, код мјерења финансијског ризика треба имати у виду особине финансијских временских серија, па су стога посебно истакнуте у раду. Други дио рада објашњава како су ове мјере ризика обухваћене правном регулативом у контроли ризика. Задатак рада је да се анализира контрола ризика у Црној Гори, као и важност стандарда који су на снази, у доприносу побољшања контроле ризика. Идеја рада је мотивисана жељом да се у Црној Гори озбиљније приступи квантификовању ризика, као и самом управљању ризиком. У наредном периоду, у оквиру мјера Централне банке Црне Горе за јачање финансијског система, континуирано ће се пратити и анализирати стање у банкарском систему, уз предузимање благовремених корективних мјера у управљању ризицима у банкама, као и даља имплементација међународно прихваћених стандарда и принципа пословања у овој области.Summary: The concept of value at risk (Value at Risk - VaR) is a measure that is increasingly used for assessing the level of exposure of financial markets’ participants. The aim of this concept, which has begun to prevail in the world of risk management since 1994, is estimation of the maximum loss of financial position at a given time for a given probability. Many methods have been developed to quantify risk. There are a number of measures to quantify the risk, and the aim of this paper is to expose these measures, with special emphasis on VaR. Also, when measuring financial risk, characteristics of financial time series should be taken into account, and therefore are particularly prominent in the work. The second part of the paper explains how these risk measures are covered by the regulations in risk control. The task of this paper is to analyze the risk control in Montenegro, and the importance of standards in force in contribution to the improvement of risk control. The idea of this paper is motivated by the desire to approach quantifying and managing risk in Montenegro more seriously. In the future, within the framework of the measures of the Central Bank to strengthen the financial system, the situation in the banking system will be continuously monitored and analyzed, by taking timely corrective measures in risk management in banks, as well as the further implementation of internationally accepted standards and principles in this field.

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An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm

Sustainability ◽

10.3390/su132413542 ◽

2021 ◽

Vol 13 (24) ◽

pp. 13542

Author(s):

Nitesh Kumar Singh ◽

Chaitali Koley ◽

Sadhan Gope ◽

Subhojit Dawn ◽

Taha Selim Ustun

Keyword(s):

At Risk ◽

Risk Analysis ◽

Power System ◽

Hybrid System ◽

Value At Risk ◽

Conditional Value At Risk ◽

Analysis Tool ◽

Economic Risk ◽

Confidence Levels ◽

System Risk

Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.

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Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Journal of Risk and Financial Management ◽

10.3390/jrfm14110540 ◽

2021 ◽

Vol 14 (11) ◽

pp. 540

Author(s):

Eyden Samunderu ◽

Yvonne T. Murahwa

Keyword(s):

At Risk ◽

Normal Distribution ◽

Hedge Funds ◽

Value At Risk ◽

Financial Risk ◽

Risk Measures ◽

Risk Measure ◽

Extensive Literature ◽

Data Set ◽

Normally Distributed

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

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On the existence of an optimal estimation window for risk measures

Applied Finance Letters ◽

10.24135/afl.v4i1and2.30 ◽

2015 ◽

Vol 4 (1and2) ◽

pp. 28

Author(s):

Marcelo Brutti Righi ◽

Paulo Sergio Ceretta

Keyword(s):

At Risk ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Value At Risk ◽

Financial Risk ◽

Risk Measures ◽

Optimal Estimation ◽

Expected Shortfall ◽

Estimation Bias ◽

Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

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