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Optimization algorithms for multiple-asset portfolios with machine learning techniques
Published in Noura Metawa, M. Kabir Hassan, Saad Metawa, Artificial Intelligence and Big Data for Financial Risk Management, 2023
One of the most significant advances in the past three decades in the field of measuring and managing financial risks is the development and the ever-growing use of VaR methodology. VaR has become the standard measure that financial analysts use to quantify financial risks including equity risk. VaR represents the potential loss in the market value of a portfolio of equities with a given probability over a certain time horizon. The main advantage of VaR over other risk quantitative measures is that it is theoretically simple. As such, VaR can be used to summarize the risk of an individual equity position or the risk of large portfolios of equity assets. Thus, VaR reduces the risk associated with any portfolio of equities (or other multiple-asset) to just one number—the expected potential loss associated with a given probability over a defined holding period (Al Janabi, 2008, 2014).
Hedging Tools for Managing Risks in Electricity Markets
Published in Mohammad Shahidehpour, Muwaffaq Alomoush, Restructured Electrical Power Systems, 2017
Mohammad Shahidehpour, Muwaffaq Alomoush
VaR is a number (an estimate) that tells a market participant, due to market movements, how much its portfolio or position may lose in a particular time period (horizon) for a given probability of occurrence. The given probability is called confidence level, which represents the degree of certainty of the VaR estimate. The common value of confidence level is 0.95 (or 95%) which means that 95% of the time the participant’s losses will be less than VaR, and 5% of the time the losses will be more than VaR. Figure 6.1 shows the idea of confidence level. Using VaR would enable participants to minimize the variability in their revenues. VaR is also a decision tool that tells the participant which risk is worth taking and to what extent each risk should be hedged. In addition, VaR gives a participant the diversification benefits for having different positions in the portfolio.
Value At Risk: Recent Advances
Published in George Anastassiou, Handbook of Analytic-Computational Methods in Applied Mathematics, 2019
Irina N. Khindanova, Svetlozar T. Rachev
Regulators and the financial industry advisory committees recommend VAR as a way of risk measuring. In July 1993, the Group of Thirty first advocated the VAR approaches in the study “Derivatives: Practices and Principles”.1 In 1993, the European Union instructed setting capital reserves to balance market risks in the Capital Adequacy Directive “EEC 6-93”, effective from January 1996.2 It was an improvement with respect to the 1988 Basle Capital Adequacy Accord of G10, which centered on credit risks and did not consider market risks in details.3
Measuring risk spillover effects on dry bulk shipping market: a value-at-risk approach
Published in Maritime Policy & Management, 2022
Jialin Yang, Xin Zhang, Ying-En Ge
The VaR measures the risk of loss. In finance, it means how much a set of investments might lose with a given confidence level. There are three methods for calculating VaR, i.e. historical simulation (non-parametric) method, variance-covariance (parametric) method and Monte Carlo simulation (Linsmeier and Pearson 1996). However, it cannot measure the risk beyond the confidence level. Therefore, the Conditional Value-at-Risk (CVaR) is proposed. Yu, Yip, and Choy (2019) measure the risks of ship leasing investments by CVaR. However, freight rates are excessively fluctuating with uncertain distribution probability function compared to traditional financial markets which are not suitable for CVaR. Besides, this paper focuses on the risks within the confidence level. As a consequence, this paper measures the risk of the dry bulk shipping market using historical data-based simulation to generate VaR so as to overcome the shortcomings of using freight rates (Angelidis and Skiadopoulos 2008).
Pricing real options based on linear loss functions and conditional value at risk
Published in The Engineering Economist, 2020
The option value determined by the linear loss function approach simply sets the upper bound on how much to pay for an option premium. To determine the appropriate amount to pay for an actual option premium, we may introduce the Conditional Value at Risk concept. The Value at Risk (VaR) is one of the well-known risk measures used by financial institutions to capture the potential loss in value of their traded portfolios from market movements over a specified period. Once the potential loss amount is determined, then this can be compared to their available capital and cash reserves to see if the losses can be covered without putting the firms at risk. When the concept is applied to non-financial firms, the Net Present Value at Risk (NPVaR) represents the worst outcome at a given confidence level on the NPV distribution. In this section, we propose to adopt the expected loss amount obtained from the Conditional Net Present Value at Risk (CNPVaR) as a basis to determine what the appropriate price to pay for option premium.
Strategic Bidding in Transmission Constrained Electricity Markets Using Artificial Bee Colony Algorithm
Published in Electric Power Components and Systems, 2012
A. K. Jain, S. C. Srivastava, S. N. Singh, L. Srivastava
VaR is a risk-assessment tool that is used by financial institutions to estimate the expected maximum loss over a target horizon within a determined confidence level. VaR is more efficient than a symmetric risk measure, such as variance, as it measures downside risk [12]. In electricity markets, VaR determines the monetary risk associated with the generation schedule PG and fluctuations in LMPs at a given confidence level. In this work, LMPs have been obtained by simulating the market clearing process. However, these simulated LMPs may differ from the actual LMPs obtained in the market, which may introduce the financial risk to the supplier. It was discussed in [20] that the predicted market price and the actual market price deviation follow a probability density function, with predicted price as its expected value. In such cases, the normal probability density function is most widely used to model the price volatility [20]. Thus, expression of VaR in terms of generation schedule PG and fluctuations in LMPs is modeled as [11]