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What the History of the Vega Option Greek Suggests

Article Quant Q&A · Author: Roozbe

Summary

Vega measures how an option’s theoretical value changes with volatility. The discussion asks who introduced the name and why, and it offers several accounts rather than a definitive origin. One respondent cites an options textbook from 1988, which lists vega among several competing names and says that pricing services used the term. Another trader recalls that a floor trader in the early 1980s put vega on options sheets, reportedly choosing it because it begins with the same sound as volatility.

Other suggestions include a possible tribute to seventeenth-century writer Joseph de la Vega and the idea that “vega” sounded Greek enough to stand in for a Greek letter beginning with V. The accounts are personal recollections and speculation, and the discussion does not establish a verified inventor or reason. It also notes that vega is not itself a Greek letter, unlike several other commonly named option sensitivities.

Key ideas

  • Vega is the sensitivity of an option’s value to volatility.
  • A cited 1988 textbook records vega as one of several names then in use.
  • A trader’s recollection links the term’s spread to options sheets in the early 1980s.
  • Proposed explanations include its sound, its link to volatility, and a possible literary namesake.
  • The discussion does not verify a single origin for the term.

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Full text
# How can I drive FPDE of American Option Price from FMLS Model?


# How can I drive FPDE of American Option Price from FMLS Model?












Under the risk neutral measure $\mathbb{Q}$. The FMLS model assumes that the log value of the underlying i.e., $\bar{x}_t=\ln S_t$. with dividend yield $D$ follows a stochastic differential equation of the maximally skewed LS process: $$d\bar{x}_t=(r-D-v)dt+\sigma dL^{\alpha -1}_t$$ where $r$ and $D$ are the risk free intrest and the dividend yield, respectivly. $t$ is the current time. and $v=\sigma ^{\alpha}sec \frac{\alpha \pi}{2}$ is a convexity adjustment. $dL^{\alpha -1}_t$ denotes the maximally skewed log-stable process, which is a special case of the Levy-$\alpha$-stable process $dL^{\alpha ,\beta}_t$ , where $\alpha \in \left( 0,2 \right]$ is the tail index describing the deviation of the LS process from the Brownian motion, and $\beta \in \left[ -1,1 \right]$ is the skew parameter. Remark that in the maximally skewed LS process, the skew parameter $\beta$ βis set to −1, in order to achieve finite moments for index levels and negative skewness in the return density. To ensure that the underlying return has the support on the whole real line, the tail index $\alpha$ needs to be restricted to $(1, 2]$.

let $\bar{V}\left( \bar{x},t;\alpha \right)$ be the price of American puts, with $\bar{x}$ xbeing the log underlying price defined as $\bar{x}=\ln S$ and $\alpha$ being the tail index.

Now how can i show that $\bar{V}\left( \bar{x},t;\alpha \right)$ should be governed by

$$\frac{\partial \bar{V}}{\partial t}+\left( r-D+\frac{1}{2}{{\sigma }^{\alpha }}\sec \frac{a\pi }{2} \right)\frac{\partial \bar{V}}{\partial \bar{x}}-\frac{1}{2}{{\sigma }^{\alpha }}\sec \frac{a\pi }{2}{}_{-\infty }D_{{\bar{x}}}^{\alpha }V\bar{V}-r\bar{V}=0,\,\,\,for\,\,{\bar{x}\in \left( {{x}_{f}},+\infty \right)}$$

where ${x}_{f}$ is the logarithm of the optimal exercise price, i.e., ${{x}_{f}}\left( t;\alpha \right)=\ln {{S}_{f}}\left( t;\alpha \right)$ and ${}_{-\infty }D_{{\bar{x}}}^{\alpha }$ is the one-dimensional Weyl factional operator defined as

$${}_{-\infty }D_{{\bar{x}}}^{\alpha }f\left( x \right)=\frac{1}{\Gamma \left( n-\alpha \right)}\frac{{{\partial }^{n}}}{\partial {{x}^{n}}}\int\limits_{-\infty }^{x}{\frac{f\left( y \right)}{{{\left( x-y \right)}^{n-\alpha -1}}}dy},\,\,\,\,\,\,n-1\le \Re \left( \alpha \right)<n $$

I appreciate any help.

Thanks.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.