Market risk

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Market risk is the risk of losses in positions arising from movements in market prices. [1] There is no unique classification as each classification may refer to different aspects of market risk. Nevertheless, the most commonly used types of market risk are:


The capital requirement for market risk is addressed under a revised framework known as "Fundamental Review of the Trading Book" (FRTB).

Risk management

All businesses take risks based on two factors: the probability an adverse circumstance will come about and the cost of such adverse circumstance. Risk management is the study of how to control risks and balance the possibility of gains.

Measuring the potential loss amount due to market risk

As with other forms of risk, the potential loss amount due to market risk may be measured in several ways or conventions. Traditionally, one convention is to use value at risk (VaR). The conventions of using VaR are well established and accepted in the short-term risk management practice.

However, VaR contains a number of limiting assumptions that constrain its accuracy. The first assumption is that the composition of the portfolio measured remains unchanged over the specified period. Over short time horizons, this limiting assumption is often regarded as reasonable. However, over longer time horizons, many of the positions in the portfolio may have been changed. The VaR of the unchanged portfolio is no longer relevant. Other problematic issues with VaR is that it is not sub-additive, and therefore not a coherent risk measure. [2] As a result, other suggestions for measuring market risk is conditional value-at-risk (CVaR) that is coherent for general loss distributions, including discrete distributions and is sub-additive. [3]

The variance covariance and historical simulation approach to calculating VaR assumes that historical correlations are stable and will not change in the future or breakdown under times of market stress. However these assumptions are inappropriate as during periods of high volatility and market turbulence, historical correlations tend to break down. Intuitively, this is evident during a financial crisis where all industry sectors experience a significant increase in correlations, as opposed to an upward trending market. This phenomenon is also known as asymmetric correlations or asymmetric dependence. Rather than using the historical simulation, Monte-Carlo simulations with well-specified multivariate models are an excellent alternative. For example, to improve the estimation of the variance-covariance matrix, one can generate a forecast of asset distributions via Monte-Carlo simulation based upon the Gaussian copula and well-specified marginals. [4] Allowing the modelling process to allow for empirical characteristics in stock returns such as auto-regression, asymmetric volatility, skewness, and kurtosis is important. Not accounting for these attributes lead to severe estimation error in the correlation and variance-covariance that have negative biases (as much as 70% of the true values). [5] Estimation of VaR or CVaR for large portfolios of assets using the variance-covariance matrix may be inappropriate if the underlying returns distributions exhibit asymmetric dependence. In such scenarios, vine copulas that allow for asymmetric dependence (e.g., Clayton, Rotated Gumbel) across portfolios of assets are most appropriate in the calculation of tail risk using VaR or CVaR. [6]

Besides, care has to be taken regarding the intervening cash flow, embedded options, changes in floating rate interest rates of the financial positions in the portfolio. They cannot be ignored if their impact can be large.

Regulatory views

The Basel Committee set revised minimum capital requirements for market risk in January 2016. [7] These revisions, the "Fundamental Review of the Trading Book", address deficiencies relating to the existing Internal models and Standardised approach for the calculation of market-risk capital, and in particular discuss the following:

Use in annual reports of U.S. corporations

In the United States, a section on market risk is mandated by the SEC [8] in all annual reports submitted on Form 10-K. The company must detail how its results may depend directly on financial markets. This is designed to show, for example, an investor who believes he is investing in a normal milk company, that the company is also carrying out non-dairy activities such as investing in complex derivatives or foreign exchange futures.

See also

Related Research Articles

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Value at risk quantitative risk measure on a specific portfolio of financial assets based on statistical model calculations

Value at risk (VaR) is a measure of the risk of loss for investments. It estimates how much a set of investments might lose, given normal market conditions, in a set time period such as a day. VaR is typically used by firms and regulators in the financial industry to gauge the amount of assets needed to cover possible losses.

Capital asset pricing model CAPM

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In finance, the beta of an investment is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors.

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Financial risk Any of various types of risk associated with financing

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Financial modeling is the task of building an abstract representation of a real world financial situation. This is a mathematical model designed to represent the performance of a financial asset or portfolio of a business, project, or any other investment.

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The RiskMetrics variance model was first established in 1989, when Sir Dennis Weatherstone, the new chairman of J.P. Morgan, asked for a daily report measuring and explaining the risks of his firm. Nearly four years later in 1992, J.P. Morgan launched the RiskMetrics methodology to the marketplace, making the substantive research and analysis that satisfied Sir Dennis Weatherstone's request freely available to all market participants.

Volatility (finance) the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns

In finance, volatility is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns.

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A vine is a graphical tool for labeling constraints in high-dimensional probability distributions. A regular vine is a special case for which all constraints are two-dimensional or conditional two-dimensional. Regular vines generalize trees, and are themselves specializations of Cantor trees. Combined with bivariate copulas, regular vines have proven to be a flexible tool in high-dimensional dependence modeling. Copulas are multivariate distributions with uniform univariate margins. Representing a joint distribution as univariate margins plus copulas allows the separation of the problems of estimating univariate distributions from the problems of estimating dependence. This is handy in as much as univariate distributions in many cases can be adequately estimated from data, whereas dependence information is rough known, involving summary indicators and judgment. Although the number of parametric multivariate copula families with flexible dependence is limited, there are many parametric families of bivariate copulas. Regular vines owe their increasing popularity to the fact that they leverage from bivariate copulas and enable extensions to arbitrary dimensions. Sampling theory and estimation theory for regular vines are well developed and model inference has left the post . Regular vines have proven useful in other problems such as (constrained) sampling of correlation matrices, building non-parametric continuous Bayesian networks.


  1. Bank for International Settlements: A glossary of terms used in payments and settlement systems
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  7. "Minimum capital requirements for market risk". 2016-01-14.Cite journal requires |journal= (help)
  8. FAQ on the United States SEC Market Disclosure Rules