Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?

Castillo-Brais, Brenda; León, Ángel; Mora, Juan

doi:10.3390/math10224329

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…In [35], the Cornish-Fisher expansion method was introduced to tackle uncertainties associated with PV systems. Yet this technique may not offer accurate estimations for problems with an intricate structure and continuous return functions [36]. In [37], a P-OPF method for wind power plants was presented, and its PDF was evaluated using a heuristic technique.…”

Section: Introductionmentioning

confidence: 99%

“…In [41], a model predictive control approach, complemented with the autoregressive integrated moving average (ARIMA) prediction method, is utilized to forecast environmental and load variations in standalone PV/battery systems, addressing the intermittent nature of Renewable Energy Sources. As described in [36,37], the MCS and its variants were employed to determine the WE system's power output PDF. A P-OPF issue was designed in [42,43] according to the MCS method of the power system that includes PV and WE units.…”

Section: Introductionmentioning

confidence: 99%

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Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Hashish,

Hasanien,

Ullah

et al. 2023

Sustainability

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In this study, the Giant Trevally Optimizer (GTO) is employed to solve the probabilistic optimum power flow (P-OPF) issue, considering Renewable Energy Source (RES) uncertainties, achieving notable cost reduction. The objective function is established to minimize the overall generation cost, including the RES cost, which significantly surpassing existing solutions. The uncertain nature of the RES is represented through the employment of a Monte Carlo Simulation (MCS), strengthened by the K-means Clustering approach and the Elbow technique. Various cases are investigated, including various combinations of PV systems, WE systems, and both fixed and fluctuating loads. The study demonstrates that while considering the costs of solar, wind, or both might slightly increase the total generation cost, the cumulative generation cost remains significantly less than the scenario that does not consider the cost of RESs. The superior outcomes presented in this research underline the importance of considering RES costs, providing a more accurate representation of real-world system dynamics and enabling more effective decision making.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Hashish,

Hasanien,

Ullah

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

Multivariate copula-based conditional quantiles: analytic higher-order moments and ratio estimation approaches

Hakim,

Salman,

Syuhada

2024

Journal of Statistical Computation and Simulation

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Formulating MCoVaR to Quantify Joint Transmissions of Systemic Risk across Crypto and Non-Crypto Markets: A Multivariate Copula Approach

Hakim

Syuhada

2023

Risks

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Evidence that cryptocurrencies exhibit speculative bubble behavior is well documented. This evidence could trigger global financial instability leading to systemic risk. It is therefore crucial to quantify systemic risk and investigate its transmission mechanism across crypto markets and other global financial markets. We can accomplish this using the so-called multivariate conditional value-at-risk (MCoVaR), which measures the tail risk of a targeted asset from each market conditional on a set of multiple assets being jointly in distress and on a set of the remaining assets being jointly in their median states. In this paper, we aimed to find its analytic formulas by considering multivariate copulas, which allow for the separation of margins and dependence structures in modeling the returns of the aforementioned assets. Compared to multivariate normal and Student’s t benchmark models and a multivariate Johnson’s SU model, the copula-based models with non-normal margins produced a MCoVaR forecast with superior conditional coverage and backtesting performances. Using a corresponding Delta MCoVaR, we found the crypto assets to be potential sources of systemic risk jointly transmitted within the crypto markets and towards the S&P 500, oil, and gold, which was more apparent during the COVID-19 period encompassing the recent 2021 crypto bubble event.

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Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?

Cited by 3 publications

References 40 publications

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Multivariate copula-based conditional quantiles: analytic higher-order moments and ratio estimation approaches

Formulating MCoVaR to Quantify Joint Transmissions of Systemic Risk across Crypto and Non-Crypto Markets: A Multivariate Copula Approach

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