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Managing an Option Portfolio and how Automated Trading makes it easier

The document discusses the management of options portfolios, outlining fundamental concepts, strategies, and risk management techniques. It explains various types of options, their characteristics, and methods for pricing and evaluating risk through different parameters. Additionally, it highlights advanced trading strategies and the role of automation in simplifying complex position management for options trading.

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Managing an Option Portfolio Rajib Ranjan Borah 3-August-2013 - and how automated trading makes it easier
2 Topics Fundamentals Strategies Managing positions Sophistication through automation © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
3 • Understanding basics of options Topics Fundamentals and derivatives Strategies Managing positions Sophistication through automation © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
4 • A Few Simple Option Trading Strategies Topics Fundamentals Strategies Managing positions Sophistication through automation © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
5 • Initiating and Managing Option Positions • Handling risks – different risk management parameters Topics Fundamentals Strategies Managing positions Sophistication through automation © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
6 Topics • Advanced Trading Strategies • Complex Position Management using Automation Fundamentals Strategies Managing positions Sophistication through automation © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
7 Fundamentals Strategies Position Management Automation & Sophistication • Definitions: • Derivative is a financial instrument whose price is derived from the price of some other financial instrument. • Option is a special type of derivative instrument - the buyer of the option has the option (i.e. right but not the obligation) to buy/sell a specified amount of underlying asset at a specified price on or before a specified date • Other types of common derivatives: Future: the owner of such a derivative is obligated to buy/sell a specified amount of underlying asset at a specified price on a specified date Forward: Same as futures, but traded OTC instead of in the exchange © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
8 Fundamentals Strategies Position Management Automation & Sophistication • Two types of options • Call Options: Buyer has the option (but not the obligation) to buy the underlying • Put Options: Buyer has the option (but not the obligation) to sell the underlying • Defining Characteristics of Option Instruments • Strike: Price of the underlying at which the option can be exercised • Expiry Date: The date at which the option can be exercised • Premium: Upfront payment made by the buyer of the option (to the seller) • Option Styles • European: can be exercised only on expiry date • American: can be exercised anytime prior to expiry date • Exotic – Bermudan, Asian, Binary, Barrier, etc © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
9 Fundamentals Strategies Position Management Automation & Sophistication Option Types elaborated CALL OPTION PUT OPTION B U Y E R S E L L E R The right (but not the obligation) to buy The right (but not the obligation) to sell The potential obligation to buy The potential obligation to sell © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
10 Fundamentals Strategies Position Management Automation & Sophistication Payoff from holding a call option instrument Consider a Call Option on XYZ stock with strike = 30, expiry date = 30th August 201X At Expiry the payoff of the call option for the buyer is as shown below: Strike at 30 Will only exercise the call option if underlying at expiry is more than the strike 14 12 10 8 6 4 2 0 20 22 24 26 28 30 32 34 36 38 40 42 Underlying price at expiry © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited For e.g., if the underlying price is at 36, the buyer of the option can buy the underlying at 30. Hence, a payoff of 6.
11 Fundamentals Strategies Position Management Automation & Sophistication Payoff from holding a put option instrument Consider a Put Option on XYZ stock with strike = 30, expiry date = 30th August 201X. At Expiry the payoff of the put option for the buyer is as shown below: 12 10 8 6 4 2 0 Will only exercise the call option if underlying at expiry is less than the strike Strike at 30 For e.g., if the underlying price is at 23, the buyer of the option can sell the underlying at 30. Hence, a payoff of 7. 20 22 24 26 28 30 32 34 36 38 40 42 © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
12 Fundamentals Strategies Position Management Automation & Sophistication Payoff - Buy Call Option 4 2 0 -2 -4 -6 -8 -10 Payoff - Sell Call Option Payoff - Buy Put Option 4 2 0 -2 -4 -6 -8 Premium Strike Payoff - Sell Put Option © Copyright 2010-2014 QuantInsti 12 Quantitative Learning Private Limited 12 10 8 6 4 2 0 -2 -4 20 22 24 26 28 30 32 34 36 38 40 42 Underlying price at expiry Premium Breakeven Point -12 20 22 24 26 28 30 32 34 36 38 40 42 Underlying price at expiry 10 8 6 4 2 0 -2 -4 20 22 24 26 28 30 32 34 36 38 40 42 Underlying Price at Expiry -10 20 22 24 26 28 30 32 34 36 38 40 42
13 Fundamentals Strategies Position Management Automation & Sophistication Terminology – Moneyness Terminology Call Option Put Option In the Money (ITM) Underlying Price > Strike Underlying Price < Strike At the Money (ATM) Underlying Price = Strike Underlying Price = Strike Out the Money (OTM) Underlying Price < Strike Underlying Price > Strike © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
14 Fundamentals Strategies Position Management Automation & Sophistication Option Premium (price) components: • Option price = Intrinsic Value + Time Value • Intrinsic Value: Immediate value of the option given the current relationship between the price of underlying and the price of the option For call options, intrinsic value = underlying – option strike For put options, intrinsic value = option strike - underlying • Time Value: Because the buyer of the option has the upside benefits but not downside obligations, therefore future price movements in the underlying can benefit (but not harm) the buyer – therefore there is an additional value for the option instruments for the extra benefits future price movements might bring An option with no intrinsic value is an Out of The Money Option © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
15 Fundamentals Strategies Position Management Automation & Sophistication Option Pricing Methodology • Pricing depends on key characteristics of instrument • Option Strike • Option Expiry date • Current Underlying Price • Characteristics of price change in underlying (volatility, price jumps, etc) • Interest Rate & Stock Borrowing Rates • Dividends • Option type (Call/Put) • Option Style (American/European) © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
16 Fundamentals Strategies Position Management Automation & Sophistication Option Pricing Methodology • Common Pricing Formulations Underlying Characteristic Pricing Methodology Constant Volatility Black Scholes Constant Volatility with Dividends Black Scholes Merton Constant Volatility with Poisson Jumps Merton Jump Diffusion Volatility as a function of Underlying price CEV Volatility as a function of Underlying price & time to Derman Kani expiry Volatility is volatile Heston American type expiry Barone Adesi Whaley, Bjerksund Stensland Currency Options Garman Kohlhagen © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
17 Fundamentals Strategies Position Management Automation & Sophistication Simple Option Trading Strategies • Option Trading can be used to express following types of views • view on the price of the underlying in the future • view on the volatility of the price movements of the underlying • view on interest rates • combined view on more than one of the above factors © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
18 Fundamentals Strategies Position Management Automation & Sophistication Expressing views on the price(/direction of price change) of the underlying • Naked Call/Put option • Call Bull Spread / Put Bull Spread • Call Bear Spread / Put Bear Spread • Combo/Risk Reversal • Ratio Spread • Ladder • Call / Put Strip • Synthetic underlying – conversal / reversal • Diagonal Calendar Spread © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
19 Fundamentals Strategies Position Management Automation & Sophistication Expressing views on the volatility (of price changes) of the underlying • Straddle • Strangle • Guts • Butterfly • Condor • Iron Butterfly • Iron Condor • Call / Put Strip • Calendar Spread • Diagonal Calendar Spread © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
20 Fundamentals Strategies Position Management Automation & Sophistication Expressing views on interest rates • Jelly Rolls • Box © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
21 Fundamentals Strategies Position Management Automation & Sophistication Risk Evaluations • First order risks: • Delta – i.e. change in option price with change in underlying price • Vega – i.e. change in option price with change in underlying volatility • Theta – i.e. change in option price as time to expiry reduces • Rho – i.e. change in option price with change in interest rates • Second Order risks • Gamma ( change of Delta with change in Underlying price) • Vanna ( change of Delta with change in Volatility) • Charm ( change of Delta with change in Time) • Vomma ( change of Vega with change in Volatility) • Veta ( change of Vega with change in Time) • Change of Vega with change in Underlying price • Vera (change of Rho with change in Volatility) © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
22 Fundamentals Strategies Position Management Automation & Sophistication Risk Evaluations • Third order risks: • Color ( change of Gamma with change in Time) • Speed ( change of Gamma with change in Underlying Price) • Zomma ( change of Gamma with change in Volatility) • Ultima ( change of Vomma with change in Volatility) • Other risks • Rega - Volatility curve skew • Sega - Volatility curve wings • Forward Volatility (Volatility between two expiry periods) • Skewed gamma (change in Gamma with change in volatility curve skew) • Skewed delta (change in Delta with change in volatility curve skew) © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
23 Fundamentals Strategies Position Management Automation & Sophistication Risk Evaluations (examples) • Delta with changing underlying price • Delta with changing volatility (Vanna) • Delta with changing time (Charm) • Gamma with changing underlying price © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
24 Fundamentals Strategies Position Management Automation & Sophistication Risk Evaluations (examples) • Gamma with changing time (Color) • Gamma with changing volatility (Zomma) • Option price at different volatility levels • Vega at different underlying levels © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
25 Fundamentals Strategies Position Management Automation & Sophistication Risk Evaluations (examples) • Vega with changing time (Veta) • Vega with changing volatility (Vomma) • Theta with changing time • Rho at different levels of underlying Price © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
26 Fundamentals Strategies Position Management Automation & Sophistication An example of a somewhat sophisticated strategy which benefits from automation • Index constituted of a basket of stocks (e.g. NIFTY50 consists of 50 stocks) • If all stocks (in the index) go up by 5%, then index also goes up by 5% • However, if the volatility of all stocks go up by 5%, can we infer the change in volatility of the index ? Not a simple answer. Depends upon correlation amongst stocks • Volatility used to price index options = function (volatility used to price stock options, correlation level amongst stocks) • However, the known values are (i) volatility used to price index options and (ii) volatility used to price stock options we can determine the implied average correlation amongst stocks we can then trade our view on this implied average correlation amongst stocks © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
27 Fundamentals Strategies Position Management Automation & Sophistication Complexities in position management of aforesaid strategy • Approximation of index basket • maintaining position in stocks in proportion to their weightage in the index • Position spread across index options and stock options • Calculating total portfolio greeks by combining greeks for individual stocks & index • for e.g.: portfolio vega = function ( vega position in index options + vega position in stock1 options + ... + vega position in stockn options ) • As the market moves, the position has to be rolled from one set of option strikes to another • Delta to be hedge periodically © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited
28 Fundamentals Strategies Position Management Automation & Sophistication Any questions ? Thank You Merci Danke Gracias Arigato Asante Grazi Shukriya See you in the QI program – EPAT (Executive Program on Algorithmic Trading) rajib.borah@quantinsti.com © Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited