Results 211 to 220 of about 366,309 (253)

Conditional Expectile: An Alternative to Value at Risk (VaR)

SSRN Electronic Journal, 2021
Various risk measures have been reviewed against the criteria commonly accepted by financial researchers and practitioners: coherence, elicitability, comonotonic additivity, and intuitiveness. It follows that the only risk measure that is both coherent and elicitable is an Expectile based risk measure. But unlike the VaR measure, the Expectile does not
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Review on Three New Value at Risk (VaR) Models

Advances in Economics, Management and Political Sciences, 2023
The emergence of financial derivatives complicates traditional financial products and increases financial market volatility. Individuals and financial institutions are both exposed to more complex and uncontrollable risks in this environment. Because of the risk's uncertainty, we must use reasonable methods to predict and estimate it in order to ...
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Introduction to Var (Value-At-Risk)

1999
Modern financial theory is based on several important principles, two of which are no-arbitrage and risk aversion. The single major source of profit is risk. The expected return depends heavily on the level of risk of an investment. Although the idea of risk seems to be intuitively clear, it is difficult to formalize it.
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Var planning problem considering conditional value-at-risk assessment

2014 IEEE PES T&D Conference and Exposition, 2014
This paper presents the reactive power planning solution under risk assessment through the CVaR (Conditional-Value-at-Risk) using stochastic programming. Load uncertainty is modeled by distribution function. Uncertainty in the reactive power availability of existing and new reactive power sources is modeled through probabilistic constraints with a ρ ...
Julio César López, J. R. S Mantovani, Javier Contreras Sanz   +2 more
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Value at Risk (VaR) in Real Options Analysis

SSRN Electronic Journal, 2003
Cash flow from operations can be controlled using real options. In this normative paper, we derive numerically in a univariate discrete time model, extension of (Kulatilaka, 1988), the expanded NPV of an industrial investment and, simultaneously, state variable thresholds for the whole life of the project to optimally exercise real options.
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CreditMetrics Methodology and Credit Value at Risk (Credit VaR)

SSRN Electronic Journal, 2021
Described by Hull (2011, 2012) as ‘a procedure for calculating credit value at risk’, CreditMetrics methodology (RiskMetrics Group 2007) is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes including credit migration (upgrades and downgrades), as well as, default.
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Value at Risk (VaR) Backtesting Techniques and P-Value Risk Decomposition Analysis

SSRN Electronic Journal, 2014
This paper presents a methodology to analyze the Value at Risk (VaR) backtesting probability values to detect the soundness of the VaR model, the integrity of the VaR input and output as well as providing information about the type of the risk that a subportfolio is exposed to in every trading day. The paper presets statistical methods to back test the
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Value-At-Risk (Var) And Extreme Value Theory (Evt)

2003
Over the past decade or so the concept of Value-at-Risk (VaR) as a risk-management tool has steadily become more and more prominent in the asset-management community. As the concept has increased in sophistication over the years, it has developed from an academic exercise to a viable risk management tool.
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CURRENCY RISK ASSESSMENT USING VALUE-AT-RISK (VAR) METHODOLOGY [PDF]

, 2020
The measurement of currency risk through the VaR metrics is highlighted in this research work. The main VaR methods – relative, Historical Stimulation and Monte Karlo Stimulation are briefly presented. A study is conducted with real data on a specific currency pair – GBP/USD.
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Risk Forecasting Using Value at Risk (VaR)

This research analyses weekly stock data gathered from Google Finance over the previous five years to use the Value at Risk (VaR) approach to evaluate the risk exposure of four companies: Apple, Coca-Cola, Amazon, and McDonald's. To visualize the behavior and patterns of these companies' stocks, two crucial graphs were first created: stock price vs ...
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