Vomma: Difference between revisions

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The names used in [[risk]] assessment, [[risk measures]], and collateral parameters are taken from the Greek language. In finance, Greeks use variables that affect the underlying parameters, which are the value of financial instruments. Greeks ensure that the portfolio is properly balanced and the risk is low. They examine even the smallest change in the underlying portfolio.  
The names used in [[risk]] assessment, [[risk measures]], and collateral parameters are taken from the Greek language. In finance, Greeks use variables that affect the underlying parameters, which are the value of financial instruments. Greeks ensure that the portfolio is properly balanced and the risk is low. They examine even the smallest change in the underlying portfolio.  


Each model serves to simplify and clarify changes. '''The Black-Scholes model''' is the simplest and most commonly used model to calculate price changes, time changes, general changes and collateral, using delta, theta and vega measures.
Each model serves to simplify and clarify changes. '''The Black-Scholes model''' is the simplest and most commonly used model to calculate price changes, time changes, general changes and collateral, using delta, theta and vega measures.  


Greek derivatives are divided into three ordered instruments<ref>Ursone P. (2015)</ref><ref>Allen S. L. (2013)</ref>.  
Greek derivatives are divided into three ordered instruments<ref>Ursone P. (2015)</ref><ref>Allen S. L. (2013)</ref>.  


A Vomma as a second-order derivative will indicate vega changes when interpreting a variable of the underlying instrument. A positive value means an increase in value, a negative value means a decrease. The convexity of the '''Vomma''' indicates that the increase in a [[percentage point]] will also be reflected in the increase in the value of the options, and what will be visible in the vega because the convexity will also appear. They are mutually correlated.  
A Vomma as a second-order derivative will indicate vega changes when interpreting a variable of the underlying instrument. A positive value means an increase in value, a negative value means a decrease. The convexity of the '''Vomma''' indicates that the increase in a [[percentage point]] will also be reflected in the increase in the value of the options, and what will be visible in the vega because the convexity will also appear. They are mutually correlated.


To make profitable options trades, you [[need]] to examine the factors that will help you understand what will happen - '''Vomma and Vega'''. They are inseparable and to understand one thing you need to know the other<ref>Ursone P. (2015)</ref><ref>Allen S. L. (2013)</ref>.  
To make profitable options trades, you [[need]] to examine the factors that will help you understand what will happen - '''Vomma and Vega'''. They are inseparable and to understand one thing you need to know the other<ref>Ursone P. (2015)</ref><ref>Allen S. L. (2013)</ref>.


'''Vega''' as integers in the range from -20 to 20 - usually allow you to determine the change based on the measured case when its value changes by 1%. For example, when the [[project]] we sell and its value drops by 10 vegs per 1000 euro, in such a case it means a [[profit]]/loss of 10 euro for each percentage decrease/loss increase in value<ref>Ursone P. (2015)</ref><ref>Allen S. L. (2013)</ref>.
'''Vega''' as integers in the range from - 20 to 20 - usually allow you to determine the change based on the measured case when its value changes by 1%. For example, when the [[project]] we sell and its value drops by 10 vegs per 1000 euro, in such a case it means a [[profit]]/loss of 10 euro for each percentage decrease/loss increase in value<ref>Ursone P. (2015)</ref><ref>Allen S. L. (2013)</ref>.  


What should the investor do? It all depends on what options he has<ref>Ursone P. (2015)</ref>:  
What should the investor do? It all depends on what options he has<ref>Ursone P. (2015)</ref>:
* If he is the owner or manager of short-term options, negative vommy values will be more advantageous for him.  
* If he is the owner or manager of short-term options, negative vommy values will be more advantageous for him.
* Long-term options should give investors a better return with a positive exchange value.  
* Long-term options should give investors a better return with a positive exchange value.


We calculate it with the formula:
We calculate it with the formula:
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: '''Vomma = (∂*v)/(∂*σ) = (∂2*V)/(∂*σ2)'''
: '''Vomma = (∂*v)/(∂*σ) = (∂2*V)/(∂*σ2)'''


The most commonly used model for trading options is the '''Black-Scholes pricing model'''. Vega and Vomma have a key role to play in this model to interpret and make the right decision<ref>Allen S. L. (2013)</ref><ref>Ursone P. (2015)</ref>.  
The most commonly used model for trading options is the '''Black-Scholes pricing model'''. Vega and Vomma have a key role to play in this model to interpret and make the right decision<ref>Allen S. L. (2013)</ref><ref>Ursone P. (2015)</ref>.


==Examples of Vomma==
==Examples of Vomma==
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* Gamma - Gamma measures the rate of change in an option's delta relative to a change in the underlying asset's price. It measures the sensitivity of an option's delta to changes in the price of the underlying asset.
* Gamma - Gamma measures the rate of change in an option's delta relative to a change in the underlying asset's price. It measures the sensitivity of an option's delta to changes in the price of the underlying asset.


==Footnotes==
==Footnotes==


<references />
<references />  


{{infobox5|list1={{i5link|a=[[Downside deviation]]}} &mdash; {{i5link|a=[[Zomma]]}} &mdash; {{i5link|a=[[Overbought oversold indicator]]}} &mdash; {{i5link|a=[[Capped Index]]}} &mdash; {{i5link|a=[[Implementation Shortfall]]}} &mdash; {{i5link|a=[[Running yield]]}} &mdash; {{i5link|a=[[Gamma hedging]]}} &mdash; {{i5link|a=[[Float time]]}} &mdash; {{i5link|a=[[Selling Into Strength]]}} }}
{{infobox5|list1={{i5link|a=[[Downside deviation]]}} &mdash; {{i5link|a=[[Zomma]]}} &mdash; {{i5link|a=[[Overbought oversold indicator]]}} &mdash; {{i5link|a=[[Capped Index]]}} &mdash; {{i5link|a=[[Implementation Shortfall]]}} &mdash; {{i5link|a=[[Running yield]]}} &mdash; {{i5link|a=[[Gamma hedging]]}} &mdash; {{i5link|a=[[Float time]]}} &mdash; {{i5link|a=[[Selling Into Strength]]}} }}


==References==
==References==
* Allen S. L. (2013)., [https://books.google.pl/booksid=G8smxk_cp7AC&pg=PA330&dq=vomma&hl=pl&sa=X&ved=0ahUKEwj7lKHFh9nlAhW5AxAIHcVuBvU4ChDoAQgwMAE#v=onepage&q=vomma&f=false '' Financial Risk Management''], John WIley & Sons, USA  
* Allen S. L. (2013)., [https://books.google.pl/booksid=G8smxk_cp7AC&pg=PA330&dq=vomma&hl=pl&sa=X&ved=0ahUKEwj7lKHFh9nlAhW5AxAIHcVuBvU4ChDoAQgwMAE#v=onepage&q=vomma&f=false '' Financial Risk Management''], John WIley & Sons, USA  
* Freedman J. (2007).,[https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3387810 ''Understanding Autocalls: Real Time Vega map''] , Oxford University Press
* Freedman J. (2007).,[https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3387810 ''Understanding Autocalls: Real Time Vega map''] , Oxford University Press
* Lynn B. W., Coffey B.J. (2009).,[https://indico.cern.ch/event/65872/attachments/1015731/1445605/FinancialDerivatives_CERNAcademicTraining_28Oct09.pdf ''Risk Management and Trading Strategies for Financial Derivatives: Foreign Exchange (FX) & Interest Rates (IR)''], CERN Academic [[Training]]  
* Lynn B. W., Coffey B.J. (2009).,[https://indico.cern.ch/event/65872/attachments/1015731/1445605/FinancialDerivatives_CERNAcademicTraining_28Oct09.pdf ''Risk Management and Trading Strategies for Financial Derivatives: Foreign Exchange (FX) & Interest Rates (IR)''], CERN Academic [[Training]]  
* Roring J. (2017).,[http://www.diva-portal.se/smash/get/diva2:1107407/FULLTEXT01.pdf ''Volatility and variance swaps''], Umea  
* Roring J. (2017).,[http://www.diva-portal.se/smash/get/diva2:1107407/FULLTEXT01.pdf ''Volatility and variance swaps''], Umea  
* Ursone P. (2015)., [https://books.google.pl/booksid=xSWsBwAAQBAJ&pg=PA197&dq=vomma&hl=pl&sa=X&ved=0ahUKEwja9vSUiNnlAhXloosKHaSLAFQQ6AEIKTAA#v=onepage&q=vomma&f=false ''How to Calculate Options Prices and Their Greeks: Exploring the Black Scholes Model from Delta to Vega''], John Wiley & Sons, Padstow  
* Ursone P. (2015)., [https://books.google.pl/booksid=xSWsBwAAQBAJ&pg=PA197&dq=vomma&hl=pl&sa=X&ved=0ahUKEwja9vSUiNnlAhXloosKHaSLAFQQ6AEIKTAA#v=onepage&q=vomma&f=false ''How to Calculate Options Prices and Their Greeks: Exploring the Black Scholes Model from Delta to Vega''], John Wiley & Sons, Padstow  
{{a|Dawid Kuczowicz}}  
{{a|Dawid Kuczowicz}}  
[[Category:Risk_management]]
[[Category:Risk_management]]

Latest revision as of 07:46, 18 November 2023

Vomma is a measure of the rate at which vega changes when market conditions change. It is found in the 'Greeks' group of measures that are used to price options. This group includes other measures such as delta, gamma, and vega. Vomma in its model contains characteristic changes concerning vegas, which during interpretation help to understand what will happen to the value of the option[1].

Greeks in finance

The names used in risk assessment, risk measures, and collateral parameters are taken from the Greek language. In finance, Greeks use variables that affect the underlying parameters, which are the value of financial instruments. Greeks ensure that the portfolio is properly balanced and the risk is low. They examine even the smallest change in the underlying portfolio.

Each model serves to simplify and clarify changes. The Black-Scholes model is the simplest and most commonly used model to calculate price changes, time changes, general changes and collateral, using delta, theta and vega measures.

Greek derivatives are divided into three ordered instruments[2][3].

A Vomma as a second-order derivative will indicate vega changes when interpreting a variable of the underlying instrument. A positive value means an increase in value, a negative value means a decrease. The convexity of the Vomma indicates that the increase in a percentage point will also be reflected in the increase in the value of the options, and what will be visible in the vega because the convexity will also appear. They are mutually correlated.

To make profitable options trades, you need to examine the factors that will help you understand what will happen - Vomma and Vega. They are inseparable and to understand one thing you need to know the other[4][5].

Vega as integers in the range from - 20 to 20 - usually allow you to determine the change based on the measured case when its value changes by 1%. For example, when the project we sell and its value drops by 10 vegs per 1000 euro, in such a case it means a profit/loss of 10 euro for each percentage decrease/loss increase in value[6][7].

What should the investor do? It all depends on what options he has[8]:

  • If he is the owner or manager of short-term options, negative vommy values will be more advantageous for him.
  • Long-term options should give investors a better return with a positive exchange value.

We calculate it with the formula:

Vomma = (∂*v)/(∂*σ) = (∂2*V)/(∂*σ2)

The most commonly used model for trading options is the Black-Scholes pricing model. Vega and Vomma have a key role to play in this model to interpret and make the right decision[9][10].

Examples of Vomma

  • Vomma is used to measure the sensitivity of an option's price to changes in implied volatility (IV). This is important for traders who want to hedge against changes in IV or speculate on the direction of IV. For example, a trader may buy an option with a high vomma to take advantage of an expected increase in IV.
  • Another example is when an option trader wants to adjust their portfolio to reflect a change in the underlying stock's implied volatility. By measuring the vomma, they can determine which option positions should be bought or sold to get the desired exposure to the change in IV.
  • Vomma can also be used to measure the risk of an option position. For example, if a trader is long an option with a high vomma, then the trader is exposed to a large amount of risk due to changes in the underlying's implied volatility.

Advantages of Vomma

Vomma is a useful tool for pricing options, and it offers a number of advantages:

  • Vomma can help traders better predict the effects of changing market conditions on the value of their options. By monitoring vomma, traders can get an idea of how much their options will gain or lose in value if the market moves in a certain direction.
  • Vomma can be used to gauge the sensitivity of options to changes in implied volatility. This information can be used to make more informed trading decisions, such as when to buy or sell an option.
  • Vomma can be used to identify areas of high and low risk. By understanding how much risk is associated with an option, traders can make informed decisions about which options to trade.
  • Vomma can be used to assess the effectiveness of hedging strategies. By understanding how much an option's value is affected by changing market conditions, traders can evaluate the effectiveness of different hedging strategies.

Limitations of Vomma

Vomma is a useful tool in pricing options, but it has its limitations. These include:

  • It does not take into account any other factors that may influence the price of an option, such as the underlying asset's volatility, time to expiration, or the type of option.
  • Vomma does not account for the actual movement of the underlying asset, so it cannot be used to accurately predict the direction of the asset's price.
  • Vomma is only an estimate of how the vega of an option will change, so it can be misleading if the market conditions change significantly.
  • Vomma does not take into account any unusual events or changes in the market that might affect the option's vega.
  • Vomma is also not very useful in determining the long-term effects of price changes on the option's value.

Other approaches related to Vomma

Other approaches related to Vomma include:

  • Theta - Theta is a measure of the rate of change in an option's value relative to the passage of time. It measures the rate of decay of an option's value as the expiration date approaches.
  • Rho - Rho measures the sensitivity of an option's price to a change in the risk-free interest rate. It is used to determine how much the price of an option will change if the risk-free rate changes.
  • Lambda - Lambda measures the rate of change in an option's value relative to a change in the underlying asset's price. It measures the sensitivity of an option's price to changes in the price of the underlying asset.
  • Charm - Charm measures the rate of change in an option's delta relative to a change in the underlying asset's price. It is used to measure the rate at which an option's delta will change as the price of the underlying asset changes.
  • Gamma - Gamma measures the rate of change in an option's delta relative to a change in the underlying asset's price. It measures the sensitivity of an option's delta to changes in the price of the underlying asset.

Footnotes

  1. ↑ Allen S. L. (2013)
  2. ↑ Ursone P. (2015)
  3. ↑ Allen S. L. (2013)
  4. ↑ Ursone P. (2015)
  5. ↑ Allen S. L. (2013)
  6. ↑ Ursone P. (2015)
  7. ↑ Allen S. L. (2013)
  8. ↑ Ursone P. (2015)
  9. ↑ Allen S. L. (2013)
  10. ↑ Ursone P. (2015)


Vomma — recommended articles
Downside deviation — Zomma — Overbought oversold indicator — Capped Index — Implementation Shortfall — Running yield — Gamma hedging — Float time — Selling Into Strength

References

Author: Dawid Kuczowicz

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