7. OPTIONS
“A financial derivative that represents a contract sold
by one party to another party.The contract offers the
buyer the right, but not the obligation, to buy (call
option) or sell (put option) the underlying stock at an
agreed-upon price (the strike price) during a certain
period of time or on a specific date (exercise date).”
18. VOLATILITY CONES
In practice, it helps us determine if an IV
is cheap by comparing it to the range of
historical volatility experience over the
same trading horizon . Since IV is the
expected volatility in the remaining life
19. OPTION PRICING MODELS
• Black-Scholes
• StochasticVolatility
• StochasticVolatility with Jump Diffusion
• Monte Carlo
• Binomial Models
• …
However, option prices are rapidly
changing and to make sense of these
highly leveraged, nonlinear, time-
dependent derivatives we need a
method to convert their prices into a
20. QUANTLIB
• Only free/open-source C++ Library for quants and developers.
• Cutting edge pricing and hedging tools.
• Robust, efficient and effective implementations.
• Exported to C#, Java, Perl, Python, GNU R..etc
• Well documented
22. HISTORICAL OPTIONS DATA
• EOD Options Data including for 4000 U.S Stocks
• Data includes Bid,Ask,Volume, OI etc..
• 1 Billion records
• CSV files
So how did we do it?
23. CONVENTIONALVS MODERN
DB
It was expected that the 20-year
headstart of the RDBMS databases and
the fact that every record in the data
conforms to the same schema would
make the RDBMS the superior
24. INFRASTRUCTURE
• Developer productivity
• Document <- -> Python
• Fast out of the box
• Low latency
• High throughput
• Predictable performance
• Sharding/replication for growth and scale out
• Free
• Great support
• Most widely used NoSQL DB
25. INFRASTRUCTURE
The decision was made for a hybrid
system where zC+ was chosen to do
the heavy lifting and to pre-process the
Implied Volatility and volatil- ity cones
using a separate C++ application.
26. PRE-PROCESSING DATA FILES
• Pre-process filter
• Top 150 S&P 500
• HighVolume and High OI (i.e liquid
options)
• Matching put or call
• options in close proximity (similiar
options)
• Reduced to 86 Million options
30. BLACK-SCHOLES
The BS makes some very simplifying
assumptions about the world. Still, the
greatest strength of using the BS is that
its weakness is well under- stood and
that the market thinks in these terms.
31. BLACK-SCHOLES
• Assumes stock price
normally distributed
• Assumes underlying
volatility constant
The BS makes some very simplifying
assumptions about the world.
Stock price is normally distributed.
34. MEASURINGVOLATILITY
•Measuring volatility in financial data problematic
•More data points = better estimate
•but volatility is a changing process.
• too much data = outdated information and irrelevant
•too little data = noise and unacceptable sampling errors.
•Need efficient time series model for estimating historical volatility
38. HISTORICALYANG-ZHANG
VOLATILITY
• up to 14 times more efficient than the
close-to-close estimator
• minimum estimation error
• accounts for drifts and opening jumps
• but degrades to that of the close-to-
close when the process is heavily
dominated by opening jumps.
39. VOLATILITY CONE STATISTICS
• The Statistics should be calculated for
• each symbol
• eachTrading Day
• Historical 10day, 20day, 30day volatility .. etc
40. HODGES-TOMPKINS
ADJUSTMENT FACTOR
h = sample length
n =T − h + 1 is the number of
distinct subseries for a total
number of observations,T
• Corrects for correlation bias in overlapping
data
42. Backtesting?
Data Flow
• Layered approach
• Data checked at
every step
• Data pre-processed
for efficient backtest
Everything ready
now…
43. BACKTESTING
• Demo = simplistic
• Considers only 800,000 shortlisted (new) options
• For each trading day
• Buys 1 = lowest percentile/SD straddle [undervalued]
• Sells 1 = highest percentile/SD straddle [overvalued]
• Holds to expiry.. never sells during life
44. ENHANCEMENTS
• Include options during their life (backtest on 86 million options)
• Rank by profitability?
• by average profitability of similar trades in the past
• overcome problem of volatility of volatility
• quantify edge in entering straddle
• TrackVIX (Volatility Index)
45. FUTURE WORK
• Production volatility trading system needs to..
• Dynamically hedge [reduce risk]
• maintain delta-neutral straddles
• because want to profit or loss from only forecast of volatility not price
• Trade sizing
• More edge = trade bigger
• More uncertainty = trade smaller
• Trade evaluation for strategy comparison
• Risk-adjusted performance measures
• maximum drawdown
• % win + win:lose size
• %
