aether gaviota options vii strategy

8/16/2019 Aether Gaviota Options VII Strategy

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Æther Analytics

Gaviota VII Strategy

Quantitative • Long Short •Equity Options


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Æther Analytics • Gaviota VII Options Strategy

Gaviota VII Options Strategy

Abstract: A strategy of systematically selling rich implie volatility through the use of high pro!a!ilityquantitative price inflecting points an equity options vertical cre it sprea s"

Investors shoul !e a#are that tra ing options involves su!stantial ris$ of loss an is not suita!le for all investors" %ast performance is not necessarily in icative of future results"%ast performance is not necessarily in icative of future results" &here is unlimite ris$ of loss in selling options"

Characteristic Description

Approach 're it Sprea Option (riting

%rocess Quantitative ) Systematic

Source of *eturns Implie Volatility 'ollapse Events

Signal &ype +igh *an$ E,treme +urst E,tension Signals

-a, &ra e Length ./)0/ ays

&ra e *is$ 'appe *is$

&his investment strategy is uncorrelate to most other investment strategies" Volatility is a form of ris$ premium1higher levels of implie volatility enhance the potential returns" 2y com!ining a high pro!a!ility price 3EL&A quantitative signals #ith options volatility mo eling 4 ata mining1 the strategy is a!le to enhance returnscompare to in epen ently tra ing equity option cre it sprea s"

&he overall portfolio positioning varies from 5/6 long7 5/6 short1 to 8/670/6 or 0/678/6 ma,imum" &he goal of$eeping the portfolio slightly long !ias is congruent #ith historical mar$et ata1 #e can remove the9 irectional ris$: of having to !e correct in forecasting the overall stoc$s mar$ets imme iate tren " *ather !yonly focusing on high pro!a!ility ./)0/ ay premium rich option events #here the main alpha is through amean reversion of option implie volatility not pre icting price irection1 #e can see$ !etter ris$ a ;ustereturns in a alternative space not #i ely saturate !y other managers or fun s"


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Strategy Science &he primary signaling event comes from searching over one thousan of the most liqui optiona!le stoc$s in

the 1 #hile e,tremes of mean reversion are evi ence of negativefee !ac$" E,tremes of !oth tren persistency an mean reversion are quantifie via multiple measurementsof the +urst e,ponent" %aralla, foun that +urst e,tremes couple #ith log perio ic cycling of price1 are highpro!a!ility signals of the !eginning or en of mar$et tren s"

E,treme+urst signals are fully characteri?e !y the presence of iscrete scale invariance1 accelerating price1log perio ic cycles1 an volume anomalies" Signals have !een foun to persist for up to 0/ time units from the‐

?ero point" E,treme+urst e,ploits the science of non linear ynamics an chaos theory to i entify unique an‐pre ictive signals occurring in freely tra ing auction mar$ets" &here are t#o types of E,treme+urst signals that#e call 9E,tensions: an 9'ompressions:" &hese correspon to the e,treme high an lo# en s of the +urste,ponent istri!ution"



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Implie! Volatility

Options are often a li$ene to a 0) imensional chess game" &he three imensions are price1 time1 an volatility" &hemost misun erstoo an neglecte imension1 an often the last thing a most tra ers are a#are of is volatility" &heessence of volatility !ase tra ing1 is !uying options #hen they are cheap an selling options #hen they are

e,pensive" In a ition to the measurement of ho# 9e,pensive: options are price #e also !elieve that implievolatility is stochastic process an is mean reverting" &he foun ations of our strategy are strongly supporte !ymassive amounts of empirical evi ence an research from the aca emic an private sector"

Implie volatility reflect the mar$ets e,pectations of #hat the future volatility of an asset #ill !e1 #hile the reali?evolatility is #hat the actual volatility of the asset turne out to !e" 3ue to emotionally riven !ehavior !et#een optionmar$et participants there are constant opportunities to e,ploit these mar$et pricing inefficiency occurrences #henthe sprea !et#een IV an +V e,pan " 9Sell fear1 !uy hope:

&he results of most research on this su!;ect confirm our in ication that the con itional variance1 log)variance1 anstan ar eviation of option volatility prices are mean revering in the long run an prove ro!ustness of these fin ingsacross ifferent time perio s1 atasets an istri!utional assumptions"

-ar$et over price theun erlying options"9E,cess %remium:



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Extreme H rst an! Implie! Volatility "# antitative $ sion o$ option pricing an! stoc% price ine$$iciency&

&hrough very large =hun re s of thousan s> sample si?e ata mining of ho# volatility !ehaves at an afterE,treme+urst inflection points1 #e have foun that implie volatility rops on average 0/)@/6" &his unique facet ofour primary strategy signal provi es us the a!ility to first esta!lishing a positive pro!a!ility e ge1 or !etter than5/75/1 that the a e,pecte price movement #ill unfol in a irection that #ill !enefit our tra e" &hen secon ly ouroption price mo eling ena!les us to further screen for high pro!a!ly option tra es to compoun our e,pectepro!a!ility of profit on each tra e" &he en result is that #ith the fusion of price an implie volatility mo eling1every tra e #e employ is positively s$e#e in our favor" An creates a opportunity that has a higher pro!a!ility ofprofit versus compare to in epen ently tra ing the un erlying stoc$ or implie volatility mispricing in isolation



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O t o$ Sample Sim lation 'esting) Strategy *is$ 'ali!ration Aggressive) Starting BAV CD1///1///"/


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'opyright =c> ./D Aether Analytics1 LL'"aetheranalytics"comAle, 2ernal1 '-&1 ' &eale,Jaetheranalytics"comSanta 2ar!ara1 'A

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aether gaviota options vii strategy

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