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{{Basel II}}
{{Basel II}}


'''Market risk''' is the [[risk]] of losses in positions arising from movements in market prices.<ref>Bank for International Settlements: A glossary of terms used in payments and settlement systems [http://www.bis.org/publ/cpss00b.pdf]</ref>
'''Market risk''' is the [[risk]] of losses in positions arising from movements in market variables like prices and [[volatility (finance)|volatility]].<ref>Bank for International Settlements: A glossary of terms used in payments and settlement systems [http://www.bis.org/publ/cpss00b.pdf]</ref>

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:
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]]'', the risk that [[stock]] or [[stock indexes|stock indices]] (e.g. [[Euro Stoxx 50]], etc.) prices or their [[implied volatility]] will change.
* ''[[Equity risk]]'', the risk that [[stock]] or [[stock indexes|stock indices]] (e.g. [[Euro Stoxx 50]], etc.) prices or their [[implied volatility]] will change.
* ''[[Interest rate risk]]'', the risk that [[interest rate]]s (e.g. [[Libor]], [[Euribor]], etc.) or their implied volatility will change.
* ''[[Interest rate risk]]'', the risk that [[interest rate]]s (e.g. [[Libor]], [[Euribor]], etc.) or their implied volatility will change.
* ''[[Currency risk]]'', the risk that foreign exchange rates (e.g. [[Currency pair|EUR/USD]], [[Currency pair|EUR/GBP]], etc.) or their implied volatility will change.
* ''[[Currency risk]]'', the risk that foreign exchange rates (e.g. [[Currency pair|EUR/USD]], [[Currency pair|EUR/GBP]], etc.) or their implied volatility will change.
* ''[[Commodity risk]]'', the risk that [[commodity]] prices (e.g. [[corn]], [[crude oil]]) or their implied volatility will change.
* ''[[Commodity risk]]'', the risk that [[commodity]] prices (e.g. [[grain|corn]], [[crude oil]]) or their implied volatility will change.
* ''[[Margining risk]]'' results from uncertain future cash outflows due to [[Margin (finance)|margin]] calls covering adverse value changes of a given position.
* ''[[Margining risk]]'' results from uncertain future cash outflows due to [[Margin (finance)|margin]] calls covering adverse value changes of a given position.
* ''[[Shape risk]]''
* ''[[Shape risk]]''
* ''[[Holding period risk]]''
* ''[[Holding period risk]]''
* ''[[Basis risk]]''
* ''[[Basis risk]]''

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


==Risk management==
==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.
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]] is then the study of how to control risks and balance the possibility of gains.
For a discussion of the practice of (market) risk management in banks, investment firms, and corporates more generally see {{slink|Financial risk management#Application}}.


==Measuring the potential loss amount due to market risk==
==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.
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.<ref>{{cite journal|last1=Artzner|first1=P.|last2=Delbaen|first2=F.|last3=Eber|first3=J.|last4=Heath|first4=D.|title=Coherent measure of risk|journal=Mathematical Finance|date=July 1999|volume=9|issue=3|pages=203–228|doi=10.1111/1467-9965.00068}}</ref> 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.<ref>{{cite journal|last1=Rockafellar|first1=R.|last2=Uryasev|first2=S.|title=Conditional value-at-risk for general loss distributions|journal=Journal of Banking & Finance|date=July 2002|volume=26|issue=7|pages=1443–1471|doi=10.1016/S0378-4266(02)00271-6|hdl=10338.dmlcz/140763}}</ref>
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 [[Subadditivity|sub-additive]], and therefore not a [[coherent risk measure]].<ref>{{cite journal|last1=Artzner|first1=P.|last2=Delbaen|first2=F.|last3=Eber|first3=J.|last4=Heath|first4=D.|title=Coherent measure of risk|journal=Mathematical Finance|date=July 1999|volume=9|issue=3|pages=203–228|doi=10.1111/1467-9965.00068|s2cid=6770585 }}</ref> 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.<ref>{{cite journal|last1=Rockafellar|first1=R.|last2=Uryasev|first2=S.|title=Conditional value-at-risk for general loss distributions|journal=Journal of Banking & Finance|date=July 2002|volume=26|issue=7|pages=1443–1471|doi=10.1016/S0378-4266(02)00271-6|hdl=10338.dmlcz/140763|hdl-access=free}}</ref>


The [[variance covariance]] and [[Historical simulation (finance)|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.<ref>{{cite journal|last1=Low|first1=R.K.Y.|last2=Faff|first2=R.|last3=Aas|first3=K.|title=Enhancing mean-variance portfolio selection by modeling distributional asymmetries|journal=Journal of Economics and Business|volume=85|pages=49|date=2016|doi=10.1016/j.jeconbus.2016.01.003|url=https://espace.library.uq.edu.au/view/UQ:377912/UQ377912_OA.pdf}}</ref> 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).<ref>{{cite journal|last1=Fantazzinni|first1=D.|title=The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study.|journal=Computational Statistics & Data Analysis|date=2009|volume=53|issue=6|pages=2168–2188|doi=10.1016/j.csda.2008.02.002}}</ref> 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.<ref>{{cite journal|last1=Low|first1=R.K.Y.|last2=Alcock|first2=J.|last3=Faff|first3=R.|last4=Brailsford|first4=T.|title=Canonical vine copulas in the context of modern portfolio management: Are they worth it?|journal=Journal of Banking & Finance|date=2013|volume=37|issue=8|pages=3085|doi=10.1016/j.jbankfin.2013.02.036}}</ref>
The [[variance covariance]] and [[Historical simulation (finance)|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.<ref>{{cite journal|last1=Low|first1=R.K.Y.|last2=Faff|first2=R.|last3=Aas|first3=K.|title=Enhancing mean-variance portfolio selection by modeling distributional asymmetries|journal=Journal of Economics and Business|volume=85|pages=49–72|date=2016|doi=10.1016/j.jeconbus.2016.01.003|url=https://espace.library.uq.edu.au/view/UQ:377912/UQ377912_OA.pdf}}</ref> 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).<ref>{{cite journal|last1=Fantazzinni|first1=D.|title=The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study.|journal=Computational Statistics & Data Analysis|date=2009|volume=53|issue=6|pages=2168–2188|doi=10.1016/j.csda.2008.02.002}}</ref> 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.<ref>{{cite journal|last1=Low|first1=R.K.Y.|last2=Alcock|first2=J.|last3=Faff|first3=R.|last4=Brailsford|first4=T.|title=Canonical vine copulas in the context of modern portfolio management: Are they worth it?|journal=Journal of Banking & Finance|date=2013|volume=37|issue=8|pages=3085|doi=10.1016/j.jbankfin.2013.02.036|s2cid=154138333 }}</ref>


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.
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==
==Regulatory views==
The Basel Committee set revised minimum capital requirements for market risk in January 2016.<ref>{{Cite journal | url=http://www.bis.org/bcbs/publ/d352.htm | title=Minimum capital requirements for market risk| date=2016-01-14}}</ref> These revisions address deficiencies relating to:
The [[Basel Committee]] set revised minimum [[capital requirements]] for market risk in January 2016.<ref>{{Cite journal | url=http://www.bis.org/bcbs/publ/d352.htm | title=Minimum capital requirements for market risk| date=2016-01-14}}</ref>
These revisions, the [[FRTB|"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:
* Boundary between the [[trading book]] and [[banking book]]
* Boundary between the "[[Trading book]]" and the "[[Banking book]]"
* Internal models approach for market risk
* The standardised approach for market risk
* Use of [[value at risk]] vs. [[expected shortfall]] to measure of risk under stress
* Use of [[value at risk]] vs. [[expected shortfall]] to measure of risk under stress
* The risk of [[Quick ratio|market illiquidity]]
* The risk of [[liquidity risk|market illiquidity]]


==Use in annual reports of U.S. corporations==
==Use in annual reports of U.S. corporations==


In the [[United States]], a section on market risk is mandated by the [[United States Securities and Exchange Commission|SEC]]<ref>FAQ on the United States [https://www.sec.gov/divisions/corpfin/guidance/derivfaq.htm SEC Market Disclosure Rules]</ref> 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.
In the [[United States]], a section on market risk is mandated by the [[United States Securities and Exchange Commission|SEC]]<ref>FAQ on the United States [https://www.sec.gov/divisions/corpfin/guidance/derivfaq.htm SEC Market Disclosure Rules]</ref> 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.

== Market risk for physical investments ==

Physical investments face market risks as well, for example [[real capital]] such as real estate can lose market value and cost components such as fuel costs can fluctuate with market prices. On the other hand, some investments in physical capital can reduce risk and the value of the risk reduction can be estimated with financial calculation methods, just as market risk in financial markets is estimated. For example [[Fuel efficiency|energy efficiency]] investments, in addition to reducing fuel costs, reduce exposure fuel price risk. As less fuel is consumed, a smaller cost component is susceptible to fluctuations in fuel prices. The value of this risk reduction can be calculated using the Tuominen-Seppänen method<ref>B Baatz, J Barrett, B Stickles: [https://aceee.org/research-report/u1803 Estimating the Value of Energy Efficiency to Reduce Wholesale Energy Price Volatility]. [[ACEEE]], Washington D.C., 2018.</ref> and its value has been shown to be approximately 10% compared to direct cost savings for a typical energy efficient building.<ref>Tuominen, P., Seppänen, T. (2017): [http://www.mdpi.com/1996-1073/10/10/1545 Estimating the Value of Price Risk Reduction in Energy Efficiency Investments in Buildings]. Energies. Vol. 10, p. 1545.</ref>


==See also==
==See also==
Line 43: Line 48:
* [[Cost risk]]
* [[Cost risk]]
* [[Demand risk]]
* [[Demand risk]]
* [[Valuation risk]]
* [[Risk modeling]]
* [[Risk modeling]]
* [[Risk attitude]]
* [[Risk attitude]]
* [[Modern portfolio theory]]
* [[Modern portfolio theory]]
* [[Risk return ratio]]
* [[Risk return ratio]]
* {{slink|Financial risk management#Banking}}
* [[FRTB|Fundamental Review of the Trading Book (FRTB)]]
** [[Internal models approach (market risk)]]
** [[Standardized approach (market risk)]]


==References==
==References==
{{reflist}}
{{Reflist}}
* {{cite book | author=Dorfman, Mark S. | title=Introduction to Risk Management and Insurance (6th ed.) | publisher=Prentice Hall | year=1997 | isbn=0-13-752106-5}}
* {{cite book | author=Dorfman, Mark S. | title=Introduction to Risk Management and Insurance (6th ed.) | publisher=Prentice Hall | year=1997 | isbn=0-13-752106-5}}


==External links==
==External links==
* [https://www.springer.com/gp/book/9783030428655 Bank Management and Control], Springer Nature – Management for Professionals, 2020
* [http://www.coopext.colostate.edu/abm/abmmanagingmarketrisk.pdf Managing market risks by forwarding pricing]
* [https://web.archive.org/web/20160304085319/http://www.coopext.colostate.edu/abm/abmmanagingmarketrisk.pdf Managing market risks by forwarding pricing]
* [https://www.springer.com/business+%26+management/finance/book/978-3-642-40373-6 Bank Management and Control], Springer - Management for Professionals, 2014
* [https://web.archive.org/web/20061116070235/http://www.uiowa.edu/ifdebook/faq/Hedge.shtml How hedge funds limit exposure to market risk]
* [https://web.archive.org/web/20061116070235/http://www.uiowa.edu/ifdebook/faq/Hedge.shtml How hedge funds limit exposure to market risk]
{{Financial risk}}
{{Financial risk}}
{{Authority control}}


[[Category:Market risk]]
[[Category:Market risk| ]]
[[Category: Pricing]]
[[Category:Pricing]]
[[Category:Statistical deviation and dispersion]]
[[Category:Statistical deviation and dispersion]]
[[Category: Market failure]]
[[Category:Market failure]]

Revision as of 08:37, 12 May 2024

Market risk is the risk of losses in positions arising from movements in market variables like prices and volatility.[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 then the study of how to control risks and balance the possibility of gains. For a discussion of the practice of (market) risk management in banks, investment firms, and corporates more generally see Financial risk management § Application.

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.

Market risk for physical investments

Physical investments face market risks as well, for example real capital such as real estate can lose market value and cost components such as fuel costs can fluctuate with market prices. On the other hand, some investments in physical capital can reduce risk and the value of the risk reduction can be estimated with financial calculation methods, just as market risk in financial markets is estimated. For example energy efficiency investments, in addition to reducing fuel costs, reduce exposure fuel price risk. As less fuel is consumed, a smaller cost component is susceptible to fluctuations in fuel prices. The value of this risk reduction can be calculated using the Tuominen-Seppänen method[9] and its value has been shown to be approximately 10% compared to direct cost savings for a typical energy efficient building.[10]

See also

References

  1. ^ Bank for International Settlements: A glossary of terms used in payments and settlement systems [1]
  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. S2CID 6770585.
  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: 49–72. 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. S2CID 154138333.
  7. ^ "Minimum capital requirements for market risk". 2016-01-14. {{cite journal}}: Cite journal requires |journal= (help)
  8. ^ FAQ on the United States SEC Market Disclosure Rules
  9. ^ B Baatz, J Barrett, B Stickles: Estimating the Value of Energy Efficiency to Reduce Wholesale Energy Price Volatility. ACEEE, Washington D.C., 2018.
  10. ^ Tuominen, P., Seppänen, T. (2017): Estimating the Value of Price Risk Reduction in Energy Efficiency Investments in Buildings. Energies. Vol. 10, p. 1545.
  • Dorfman, Mark S. (1997). Introduction to Risk Management and Insurance (6th ed.). Prentice Hall. ISBN 0-13-752106-5.

External links