Results 181 to 190 of about 60,954 (219)

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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Range-based models in estimating value-at-risk (VaR) [PDF]

Philippine Review of Economics, 2008
This paper introduces new methods of estimating Value-at-Risk (VaR) using range-based GARCH (general autoregressive conditional heteroskedasticity) models. These models, which could be based on either the Parkinson range or the Garman-Klass range, are applied to ten stock market indices of selected countries in the Asia-Pacific region.
Mapa, Dennis, Beronilla, Nikkin
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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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Debt Risk Research on PPP Model Based on VAR (Value at Risk) Model

2021
The report of the 19th national congress points out that from now on to 2020 is the decisive period for building a moderately prosperous society in an all-round way, while PPP project investment involves 19 industries, such as transportation, comprehensive development of cities and towns, education, health care, pension, etc., providing more ...
Guangli Yang, Chao Wang, Wenmin Kuang
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Value-at-Risk dynamics: a copula-VAR approach

The European Journal of Finance, 2019
In financial research and among risk management practitioners the estimation of a correct measure of the Value-at-Risk still proves interesting.
Giovanni de Luca, Giorgia Rivieccio, Stefania Corsaro   +2 more
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LIMITATIONS OF VALUE-AT-RISK (VAR) FOR BUDGET ANALYSIS

2004
Value-at-risk (VaR) is increasingly being applied to problems in agriculture, especially valuation of crop insurance and agricultural lending risk exposure. VaR conveys the probability that losses exceeding a threshold will likely occur within a specified timeframe. However, it does not provide the expected value of losses, should they happen.
Gustafson, Cole R., Gustafson, Cole R.
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On the Cognitive Surprise in Risk Management: An Analysis of the Value-at-Risk (VaR) Historical

2015
Financial markets are environments in which a variety of products are negotiated by heterogeneous agents. In such environments, agents need to cope with uncertainty and with different kinds of risks. In trying to assess the risks they face, agents use a myriad of different approaches to somewhat quantify the occurrence of risks and events that may have
Davi Baccan, Elton Sbruzzi, Luís Macedo
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Value added reseller or value at risk: The dark side of relationships with VARs

Industrial Marketing Management, 2016
Abstract This paper examines the dark side of using reseller networks to provide after-sales services. A proposal made by previous research studies on the use of brand-reseller relationships for management of value to be delivered remotely through reseller networks was reviewed.
Gupta S, Vaatanen J, Khaneja S
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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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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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