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Volatility swap

From Wikipedia, the free encyclopedia
Financial derivative instrument

Infinance, avolatility swap is aforward contract on the future realisedvolatility of a given underlying asset. Volatility swaps allow investors to trade the volatility of an asset directly, much as they would trade a price index. Its payoff at expiration is equal to

(σrealisedKvol)Nvol{\displaystyle (\sigma _{\text{realised}}-K_{\text{vol}})N_{\text{vol}}}

where:

that is, the holder of a volatility swap receivesNvol{\displaystyle N_{\text{vol}}} for every point by which the underlying's annualised realised volatilityσrealised{\displaystyle \sigma _{\text{realised}}} exceeded the delivery price ofKvol{\displaystyle K_{\text{vol}}}, and conversely, paysNvol{\displaystyle N_{\text{vol}}} for every point the realised volatility falls short of the strike.[1]

The underlying is usually a financial instrument with an active orliquidoptions market, such asforeign exchange, stock indices, or single stocks. Unlike an investment in options, whose volatility exposure is contaminated by its price dependence, these swaps provide pure exposure to volatility alone. This is truly the case only forforward starting volatility swaps. However, once the swap has its asset fixings itsmark-to-market value also depends on the current asset price. One can use these instruments to speculate on future volatility levels, to trade the spread between realized and implied volatility, or to hedge the volatility exposure of other positions or businesses.

Volatility swaps are more commonly quoted and traded than the very similar but simplervariance swaps, which can be replicated with a linear combination of options and a dynamic position in futures. The difference between the two is convexity: The payoff of a variance swap is linear with variance but convex with volatility.[1]That means, inevitably, a static replication (a buy-and-hold strategy) of a volatility swap is impossible. However, using the variance swap (ΣT2{\displaystyle \Sigma _{T}^{2}}) as a hedging instrument and targeting volatility (ΣT{\displaystyle \Sigma _{T}}), volatility can be written as a function of variance:

ΣT=aΣT2+b{\displaystyle \Sigma _{T}=a\Sigma _{T}^{2}+b}

anda{\displaystyle a} andb{\displaystyle b} chosen to minimise the expect expected squared deviation of the two sides:

minE[(ΣTaΣT2b)2]{\displaystyle {\text{min}}E[(\Sigma _{T}-a\Sigma _{T}^{2}-b)^{2}]}

then, if the probability of negative realised volatilities is negligible, future volatilities could be assumed to be normal with meanΣ¯{\displaystyle {\bar {\Sigma }}} and standard deviationσΣ{\displaystyle \sigma _{\Sigma }}:

ΣTN(Σ¯,σΣ){\displaystyle \Sigma _{T}\sim N({\bar {\Sigma }},\sigma _{\Sigma })}

then the hedging coefficients are:

a=12Σ¯+σΣ2Σ¯{\displaystyle a={\frac {1}{2{\bar {\Sigma }}+{\frac {\sigma _{\Sigma }^{2}}{\bar {\Sigma }}}}}}
b=Σ¯2+σΣ2Σ¯2{\displaystyle b={\frac {\bar {\Sigma }}{2+{\frac {\sigma _{\Sigma }^{2}}{{\bar {\Sigma }}^{2}}}}}}

Definition of the realized volatility

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Definition of the annualized realized volatility depends on traders viewpoint on the underlying price observation, which could be either discretely or continuously in time. For the former one, with the analogous construction to that of thevariance swap, if there aren+1{\displaystyle n+1} sampling points of the observed underlying prices, says,St0,St1,...,Stn{\displaystyle S_{t_{0}},S_{t_{1}},...,S_{t_{n}}}where0ti1<tiT{\displaystyle 0\leq t_{i-1}<t_{i}\leq T}fori=1{\displaystyle i=1} ton{\displaystyle n}. DefineRi=ln(Sti/Sti1),{\displaystyle R_{i}=\ln(S_{t_{i}}/S_{t_{i-1}}),} the natural log returns.Then the discrete-sampling annualized realized volatility is defined by

which basically is the square root of annualizedrealized variance. Here,A{\displaystyle A} denotes an annualized factor which commonly selected to be the number of the observed price in a year i.e.A=252{\displaystyle A=252} if the price is monitored daily orA=52{\displaystyle A=52} if it is done weekly.T{\displaystyle T} is the expiry date of the volatility swap defined byn/A{\displaystyle n/A}.

The continuous version of the annualized realized volatility is defined by means of the square root of quadratic variation of the underlying price log-return:

whereσ(s){\displaystyle \sigma (s)} is the instantaneous volatility of the underlying asset. Once the number of price's observation increase to infinity, one can find thatσrealised{\displaystyle \sigma _{\text{realised}}} converges in probability toσ~realized{\displaystyle {\tilde {\sigma }}_{\text{realized}}}[2] i.e.

limnAni=1nRi2=1T0Tσ2(s)ds,{\displaystyle \lim _{n\to \infty }{\sqrt {{\frac {A}{n}}\sum _{i=1}^{n}R_{i}^{2}}}={\sqrt {{\frac {1}{T}}\int _{0}^{T}\sigma ^{2}(s)ds}},}

representing the interconnection and consistency between the two approaches.

Pricing and valuation

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In general, for a specified underlying asset, the main aim of pricing swaps is to find a fair strike price since there is no cost to enter the contract. One of the most popular approaches to such fairness is exploiting theMartingale pricing method, which is the method to find the expected present value of given derivative security with respect to some risk-neutral probability measure (or Martingale measure). And how such a measure is chosen depends on the model used to describe the price evolution.

Mathematically speaking, if we suppose that the price processS=(St)0tT{\displaystyle S=(S_{t})_{0\leq t\leq T}} follows theBlack-Scholes model under the martingale measureQ{\displaystyle \mathbb {Q} }, then it solves the following SDE:

dStSt=r(t)dt+σ(t)dWt,S0>0{\displaystyle {\frac {dS_{t}}{S_{t}}}=r(t)dt+\sigma (t)dW_{t},\;\;S_{0}>0}

where:

Since we know that(σrealisedKvol)×Nvol{\displaystyle (\sigma _{\text{realised}}-K_{\text{vol}})\times N_{\text{vol}}} is the volatility swap payoff at expiry in the discretely sampled case (which is switched toσ~realized{\displaystyle {\tilde {\sigma }}_{\text{realized}}} for the continuous case), then its expected value at timet0{\displaystyle t_{0}}, denoted byVt0{\displaystyle V_{t_{0}}} is

Vt0=et0Tr(s)dsEQ[σrealisedKvol|Ft0]×Nvol,{\displaystyle V_{t_{0}}=e^{\int _{t_{0}}^{T}r(s)ds}\mathbb {E} ^{\mathbb {Q} }[\sigma _{\text{realised}}-K_{\text{vol}}|{\mathcal {F}}_{t_{0}}]\times N_{\text{vol}},}

which gives

Kvol=EQ[σrealised|Ft0]{\displaystyle K_{\text{vol}}=\mathbb {E} ^{\mathbb {Q} }[\sigma _{\text{realised}}|{\mathcal {F}}_{t_{0}}]}

due to the zero price of the swap, defining the value of a fair volatility strike. The solution can be discovered in various ways. For instance, we obtain the closed-form pricing formula once theprobability distribution function ofσrealized{\displaystyle \sigma }_{\text{realized}} orσ~realized{\displaystyle {\tilde {\sigma }}_{\text{realized}}} is known, or compute it numerically by means of theMonte Carlo method. Alternatively, Upon certain restrictions, one can utilize the value of the European options to approximate the solution.[3]

Pricing volatility swap with continuous-sampling

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Regarding the argument of Carr and Lee (2009),[3] in the case of the continuous- sampling realized volatility if we assumes that the contract begins at timet0=0{\displaystyle t_{0}=0},r(t){\displaystyle r(t)} is deterministic andσ(t){\displaystyle \sigma (t)} is arbitrary (deterministic or a stochastic process) but independent of the price's movement i.e. there is no correlation betweenσ(t){\displaystyle \sigma (t)} andSt{\displaystyle S_{t}}, and denotes byCt(K,T){\displaystyle C_{t}(K,T)} theBlack-Scholes formula for European call option written onSt{\displaystyle S_{t}} with the strike priceK{\displaystyle K} at timet,0tT{\displaystyle t,\;0\leq t\leq T} with expiry dateT{\displaystyle T}, then by the auxilarity of the call option chosen to be at-the-money i.e.K=S0{\displaystyle K=S_{0}}, the volatility strikeKvol{\displaystyle K_{\text{vol}}} can be approximated by the function

Kvol=EQ[σ~realised|Ft0]2πTC0(S0,T)S02r(T){\displaystyle K_{\text{vol}}=\mathbb {E} ^{\mathbb {Q} }[{\tilde {\sigma }}_{\text{realised}}|{\mathcal {F}}_{t_{0}}]\approx {\sqrt {\frac {2\pi }{T}}}{\frac {C_{0}(S_{0},T)}{S_{0}}}-2r(T)}

which is resulted from applyingTaylor's series on the normal distribution parts of theBlack-Scholes formula.

See also

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References

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  1. ^abDerman, Emanuel; Dmeterfi, Kresimir; Kamal, Michael; Zou, Joseph."More Than You Ever Wanted To Know About Volatility Swaps"(PDF).Quantitative Strategis Research Notes. Goldman Sachs. Retrieved16 December 2019.
  2. ^Barndorff-Nielsen, Ole E.;Shephard, Neil (May 2002)."Econometric analysis of realised volatility and its use in estimating stochastic volatility models".Journal of the Royal Statistical Society, Series B.64 (2):253–280.doi:10.1111/1467-9868.00336.S2CID 122716443.
  3. ^abCarr, Peter; Lee, Roger (2009-12-05). "Volatility Derivatives".Annual Review of Financial Economics.1 (1): 319-339.doi:10.1146/annurev.financial.050808.114304.

External links

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Modelling volatility
Trading volatility
Options
Terms
Vanillas
Exotics
Strategies
Valuation
Swaps
Exotic derivatives
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