Market risk

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Market risk is the risk of losses in positions arising from movements in market prices. [1]

Risk is the potential for uncontrolled loss of something of value. Values can be gained or lost when taking risk resulting from a given action or inaction, foreseen or unforeseen. Risk can also be defined as the intentional interaction with uncertainty. Uncertainty is a potential, unpredictable, and uncontrollable outcome; risk is an aspect of action taken in spite of uncertainty.

Contents

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:

Equity risk is "the financial risk involved in holding equity in a particular investment". Equity risk often refers to equity in companies through the purchase of stocks, and does not commonly refer to the risk in paying into real estate or building equity in properties.

Stock financial instrument

The stock of a corporation is all of the shares into which ownership of the corporation is divided. In American English, the shares are commonly known as "stocks". A single share of the stock represents fractional ownership of the corporation in proportion to the total number of shares. This typically entitles the stockholder to that fraction of the company's earnings, proceeds from liquidation of assets, or voting power, often dividing these up in proportion to the amount of money each stockholder has invested. Not all stock is necessarily equal, as certain classes of stock may be issued for example without voting rights, with enhanced voting rights, or with a certain priority to receive profits or liquidation proceeds before or after other classes of shareholders.

The EURO STOXX 50 is a stock index of Eurozone stocks designed by STOXX, an index provider owned by Deutsche Börse Group. According to STOXX, its goal is "to provide a blue-chip representation of Supersector leaders in the Eurozone". It is made up of fifty of the largest and most liquid stocks. The index futures and options on the EURO STOXX 50, traded on Eurex, are among the most liquid such products in Europe and the world.

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.

Risk management Set of measures for the systematic identification, analysis, assessment, monitoring and control of risks

Risk management is the identification, evaluation, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability or impact of unfortunate events or to maximize the realization of opportunities.

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.

Value at risk risk measure on a specific portfolio of financial assets.

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.

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]

Historical simulation in finance's value at risk (VaR) analysis is a procedure for predicting the value at risk by 'simulating' or constructing the cumulative distribution function (CDF) of assets returns over time. Unlike parametric VaR models, historical simulation does not assume a particular distribution of the asset returns. Also, it is relatively easy to implement. However, there are a couple of shortcomings of historical simulation. Historical simulation applies equal weight to all returns of the whole period; this is inconsistent with the diminishing predictability of data that are further away from the present.

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 address deficiencies relating to:

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

Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade". Its concern is thus the interrelation of financial variables, such as prices, interest rates and shares, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital.

Capital asset pricing model CAPM

In finance, the capital asset pricing model (CAPM) is a model used to determine a theoretically appropriate required rate of return of an asset, to make decisions about adding assets to a well-diversified portfolio.

Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of diversification in investing, the idea that owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. It uses the variance of asset prices as a proxy for risk.

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.

In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform. Copulas are used to describe the dependence between random variables. Their name comes from the Latin for "link" or "tie", similar but unrelated to grammatical copulas in linguistics. Copulas have been used widely in quantitative finance to model and minimize tail risk and portfolio-optimization applications.

Financial risk Any of various types of risk associated with financing

Financial risk is any of various types of risk associated with financing, including financial transactions that include company loans in risk of default. Often it is understood to include only downside risk, meaning the potential for financial loss and uncertainty about its extent.

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.

In the fields of actuarial science and financial economics there are a number of ways that risk can be defined; to clarify the concept theoreticians have described a number of properties that a risk measure might or might not have. A coherent risk measure is a function that satisfies properties of monotonicity, sub-additivity, homogeneity, and translational invariance.

The following outline is provided as an overview of and topical guide to finance:

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) degree of variation of a trading price series over time

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

In finance, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist. Rebonato in 2002 defines model risk as "the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market".

News analysis refers to the measurement of the various qualitative and quantitative attributes of textual news stories. Some of these attributes are: sentiment, relevance, and novelty. Expressing news stories as numbers and metadata permits the manipulation of everyday information in a mathematical and statistical way. This data is often used in financial markets as part of a trading strategy or by businesses to judge market sentiment and make better business decisions.

Portfolio optimization is the process of selecting the best portfolio, out of the set of all portfolios being considered, according to some objective. The objective typically maximizes factors such as expected return, and minimizes costs like financial risk. Factors being considered may range from tangible to intangible.

In probability and statistics, an elliptical distribution is any member of a broad family of probability distributions that generalize the multivariate normal distribution. Intuitively, in the simplified two and three dimensional case, the joint distribution forms an ellipse and an ellipsoid, respectively, in iso-density plots.

Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets. Generally, mathematical finance will derive and extend the mathematical or numerical models without necessarily establishing a link to financial theory, taking observed market prices as input. Mathematical consistency is required, not compatibility with economic theory. Thus, for example, while a financial economist might study the structural reasons why a company may have a certain share price, a financial mathematician may take the share price as a given, and attempt to use stochastic calculus to obtain the corresponding value of derivatives of the stock. The fundamental theorem of arbitrage-free pricing is one of the key theorems in mathematical finance, while the Black–Scholes equation and formula are amongst the key results.

Financial correlations measure the relationship between the changes of two or more financial variables over time. For example, the prices of equity stocks and fixed interest bonds often move in opposite directions: when investors sell stocks, they often use the proceeds to buy bonds and vice versa. In this case, stock and bond prices are negatively correlated.

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.

An X-Value Adjustment is a generic term referring collectively to a number of different “Valuation Adjustments” in relation to derivative instruments held by banks. The purpose of these is twofold: primarily to hedge for possible losses due to counterparty default; but also, to determine the amount of capital required under Basel III. For a discussion as to the impact of xVA on the bank's overall balance sheet, return on equity, and dividend policy, see: XVA has, in many institutions, led to the creation of specialized desks. Note that the various XVA require careful and correct aggregation without double counting.

References

  1. Bank for International Settlements: A glossary of terms used in payments and settlement systems
  2. Artzner, P.; Delbaen, F.; Eber, J.; Heath, D. (July 1999). "Coherent measure of risk". Mathematical Finance. 9 (3): 203–228. doi:10.1111/1467-9965.00068.
  3. Rockafellar, R.; Uryasev, S. (July 2002). "Conditional value-at-risk for general loss distributions". Journal of Banking & Finance. 26 (7): 1443–1471. doi:10.1016/S0378-4266(02)00271-6. hdl:10338.dmlcz/140763.
  4. Low, R.K.Y.; Faff, R.; Aas, K. (2016). "Enhancing mean-variance portfolio selection by modeling distributional asymmetries" (PDF). Journal of Economics and Business. 85: 49. doi:10.1016/j.jeconbus.2016.01.003.
  5. Fantazzinni, D. (2009). "The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study". Computational Statistics & Data Analysis. 53 (6): 2168–2188. doi:10.1016/j.csda.2008.02.002.
  6. Low, R.K.Y.; Alcock, J.; Faff, R.; Brailsford, T. (2013). "Canonical vine copulas in the context of modern portfolio management: Are they worth it?". Journal of Banking & Finance. 37 (8): 3085. doi:10.1016/j.jbankfin.2013.02.036.
  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