Option Writing Strategy based on Volatility Regime Back Testing Karl Gauvin, M.Sc. Paul Turcotte, Ph.D. 423 - 300 St-Sacrement Montreal (Quebec) H2Y 1X4 www.openmindcapital.com 514-824-4778 [email protected]CONFIDENTIAL OpenMind Capital - February 2015 Financial Innovation
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Quality Volatility Regime (Option Strategy - Feb 05 2015)
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Option Writing Strategy based on Volatility Regime
OpenMind Capital is not registered as an investment advisor under NI 31-103 in Canada, the Investment Advisor Act of 1940 in the U.S.A. or in any others legislations. OpenMind Capital is a research and development company. This presentation outlines the result of a series of back-testing using a financial and mathematical model developed by the firm. The information contained in this presentation is confidential and proprietary to OpenMind Capital Inc. It is provided for informational purposes only. Under no circumstances does the information in this report represent a recommendation to buy or sell investment instruments. Information in this presentation is not complete and does not contain certain material information about making investments including important disclosures and risk factors.
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Presentation Content
o Elevator pitch o Volatility in the financial (capital) equity market o Volatility Index – VI
o Realized performance and volatility during High and Low Quality regime
o S&P 500 High Quality Index and S&P 500 Low Quality Index o Back Test: S&P 500 Index Option Writing Strategies
o Conclusion
o Appendix: Test Model's Robustness
o Appendix: The Team
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o Volatility of Volatility (V2) explain why sharp market drawdowns occur more frequently than what is assume under the Modern Portfolio Theory (MPT) assumption.
o Using volatility regime methodology, we have developed a financial model which enable us to identify Low Quality (Bad Volatility) and High Quality (Good Volatility) regime in equity markets.
o Equity markets delivers very good risk-adjusted performance during high quality regime but failed to do it during low quality regime.
o Volatility regime in equity market can be used to build investment strategies without relying on economics and/or market forecasts.
o Assessing Volatility Regime can’t be achieved with traditional risk measurement such as historical standard deviation, VIX, or even the most sophisticated Garch equation. o The objective is to allocate more money in risky asset during High Quality Regime (Good Volatility) and less during Low Quality Regime (Bad Volatility).
o Rather than hedging (Target Volatility Strategy) the underlying risk pertaining to change in volatility, we prefer to capitalize on it and generate added value.
Quality Volatility Regime - Elevator Pitch
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o There is an inverse relationship between significant change in equity index returns and change in its underlying volatility. Behavioral finance provides explanations for this relationship.
o Volatility of equity returns follows a mean reverting process and empirical studies have demonstrated it can be inferred with statistical tools. However, contrary to popular belief, this relationship is not easily exploitable.
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S&P TSX Composite Index Level and Annualized Volatility of Returns Jan. 1979 to 2014
Volatility in the Financial Equity Market
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o Proprietary Index to assess the level of volatility in equity market. o React more quickly to change in volatility than traditional risk measurement tools.
o The level approach quantifies the magnitude of volatility (HIGH versus LOW).
o The index allow us to discriminate between Good (High Quality) and Bad Volatility (Low Quality) regime.
o Can be used in several equity markets around the globe: S&P TSX Composite, S&P 500, MSCI EAFE, CAC 40, DAX, Nikkei 225, etc.
Volatility Index
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Volatility Index – S&P TSX Composite Index Jan. 1985 to Dec. 2014
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We use Volatility Regime Switching Models to assess the current market volatility regime (Low or High) and the probability of being in this regime.
Volatility Index
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S&P TSX Composite Index – Daily Return Distribution Jan. 1985 to Dec. 2014
S&P TSX Composite Index – Daily Return Distribution Jan. 1985 to Dec. 2014
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S&P TSX Realized Performance and Volatility - High and Low Quality Regime
From 1985 to Dec 2014, the S&P TSX Index achieved an annualized performance of 15.2% during high quality regime versus -2.9% during low quality regime.
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S&P 500 Realized Performance and Volatility - High and Low Quality Regime
From 1970 to Dec 2014, the S&P 500 Index achieved an annualized performance of 15.6% during high quality (low volatility) regime versus 1.2% during low quality (high volatility) regime.
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Return to Risk Ratio - S&P 500 Index Tot Ret. Jan 1970 to Dec 2014
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S&P 500 Index – High and Low Quality Regime From Jan. 1970 to Dec. 2014
S&P 500 Index and Volatility Regime
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S&P 500 Index Realized Volatility and Volatility Regime
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S&P 500 Index Returns – High and Low Quality Regime From Jan. 1970 to Dec. 2014
S&P 500 Index Realized Return and Volatility Regime
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S&P 500 – High and Low Quality Index From Jan. 1970 to Dec. 2014
S&P 500 High Quality and S&P 500 Low Quality Index
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Strategy Index
S&P 500 – Defensive Strategy based on Volatility Regime From Jan. 1970 to Dec. 2014
S&P 500 Defensive Strategy based on Volatility Regime
Strategy Index Exc. Ret.
Return 11.9% 10.4% 1.4%
S. Dev. 10.3% 17.0% 13.6%
Ret./Risk 1.16 0.61 0.11
Annualized Performance (Jan 1970 to Dec 2014)
The performance information presented represents back-tested performance for the period date shown. Back-tested performance is hypothetical and is provided for information purposes only to show historical performance had the strategy been managed over the relevant period. We refer to this hypothetical data as simulated experience. Back-tested performance results have certain inherent limitations. Such results do not necessarily represent the impact of material market conditions such as liquidity and intra-day volatility. The results reflect performance of a strategy not historically offered to investors and do NOT represent returns that any investor actually attained. Past performance may not be repeated.
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Financial Innovation
Option Writing Strategy Based on Volatility Regime
The myth about option writing strategies and diversification Contrary to popular belief, the best way to make money with options is to be on the short side of the market. Writing Put and Call options delivers excellent returns over long time but expose investors to significant performance drawdown's (similar to a long equity Index Strategy). If someone would have invested in the S&P 500 index, with no leverage (from 1990 to 2014), the investor would have experienced market drawdown's up to 55%. When the market collapse, the correlation among stocks increase. Diversification is definitely not as powerful as most peoples think. When you need it, it doesn’t performed as much as expected. Investing in a systematic put writing strategy (short term options and slightly in-the-money) with a notional leverage of 1.5 would have generated market drawdown's up to 58%. This strategy will have generated an excess return (versus the S&P 500 Index) of 0.60% on an annualized basis. Not so bad for a passive strategy.
Financial Innovation
Back Test: Systematic S&P 500 Index Option Writing Strategy Jan 1990 to Dec 2014
The performance information presented represents back-tested performance for the period date shown. Back-tested performance is hypothetical and is provided for information purposes only to show historical performance had the strategy been managed over the relevant period. We refer to this hypothetical data as simulated experience. Back-tested performance results have certain inherent limitations. Such results do not necessarily represent the impact of material market conditions such as liquidity and intra-day volatility. The results reflect performance of a strategy not historically offered to investors and do NOT represent returns that any investor actually attained. Past performance may not be repeated.
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Annual Return – Syst. S&P 500 Index Option Writing Strategy (Lev. of 1.5x)
Systematic S&P 500 Index Option Writing Strategy (Notional Leverage of 1.5x) – Jan. 1990 to Dec. 2014
Systematic S&P 500 Index Option Writing Strategy (Notional Leverage of 1.5x) – Jan. 1990 to Dec. 2014
Back Test Strategy: S&P 500 Index Option Writing Strategy
o Dynamic option writing strategy based on volatility regime o Generate better Sharp Ratio than a systematic put writing strategy (previous slide)
o Limit potential drawdown compare to systematic put writing strategy
o Better return adjusted for risk entitle more leverage
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Volatilty
Regime
GOOD (High quality) SHORT OTM SHORT ITM
BAD (Low quality)Reduce SHORT OTM or
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CALL PUT
Financial Innovation
Back Test Strategy: S&P 500 Index Option Writing Strategy
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S&P 500 High Quality Index – Jan. 1970 to Dec. 2014
S&P 500 Low Quality Index – Jan. 1970 to Dec. 2014
We sell (write) put option during high quality regime.
We sell OTM call with inner strike
We reduce short put or do not sell (write) put option during low
quality regime.
We reduce short OTM call with outer strike or do not sell call
option. 25
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Financial Innovation
Back Test Strategy: Absolute Return S&P 500 Index Option Writing Strategy Jan 1990 to Dec 2014
The performance information presented represents back-tested performance for the period date shown. Back-tested performance is hypothetical and is provided for information purposes only to show historical performance had the strategy been managed over the relevant period. We refer to this hypothetical data as simulated experience. Back-tested performance results have certain inherent limitations. Such results do not necessarily represent the impact of material market conditions such as liquidity and intra-day volatility. The results reflect performance of a strategy not historically offered to investors and do NOT represent returns that any investor actually attained. Past performance may not be repeated.
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Annual Return – Abs. Ret. S&P 500 Index Option Writing Strategy (Lev. of 1.75x)
Abs. Ret. S&P 500 Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1990 to Dec. 2014
Abs. Ret. S&P 500 Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1990 to Dec. 2014
Back Test Strategy: Absolute Return S&P 500 Index Option Writing Strategy Jan 1990 to Dec 2014
The performance information presented represents back-tested performance for the period date shown. Back-tested performance is hypothetical and is provided for information purposes only to show historical performance had the strategy been managed over the relevant period. We refer to this hypothetical data as simulated experience. Back-tested performance results have certain inherent limitations. Such results do not necessarily represent the impact of material market conditions such as liquidity and intra-day volatility. The results reflect performance of a strategy not historically offered to investors and do NOT represent returns that any investor actually attained. Past performance may not be repeated.
Annualized 4 Years Returns - S&P 500 Abs. Ret. Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1994 to Dec. 2014
20
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
Dec
-93
Dec
-94
Dec
-95
Dec
-96
Dec
-97
Dec
-98
Dec
-99
Dec
-00
Dec
-01
Dec
-02
Dec
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Dec
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Dec
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Dec
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Dec
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Dec
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Dec
-13
Syst. Put X1 Abs. Return Put Call X1.75
Financial Innovation
Back Test Strategy: Dynamic S&P 500 Index Option Writing Strategy Jan 1990 to Dec 2014
The performance information presented represents back-tested performance for the period date shown. Back-tested performance is hypothetical and is provided for information purposes only to show historical performance had the strategy been managed over the relevant period. We refer to this hypothetical data as simulated experience. Back-tested performance results have certain inherent limitations. Such results do not necessarily represent the impact of material market conditions such as liquidity and intra-day volatility. The results reflect performance of a strategy not historically offered to investors and do NOT represent returns that any investor actually attained. Past performance may not be repeated.
21
Annual Return – Dynamic S&P 500 Index Option Writing Strategy (Lev. of 1.75x)
Dynamic S&P 500 Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1990 to Dec. 2014
Dynamic S&P 500 Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1990 to Dec. 2014
Back Test Strategy: Dynamic S&P 500 Index Option Writing Strategy versus MSCI USA Low Volatility Index - Jan 1990 to Dec 2014
The performance information presented represents back-tested performance for the period date shown. Back-tested performance is hypothetical and is provided for information purposes only to show historical performance had the strategy been managed over the relevant period. We refer to this hypothetical data as simulated experience. Back-tested performance results have certain inherent limitations. Such results do not necessarily represent the impact of material market conditions such as liquidity and intra-day volatility. The results reflect performance of a strategy not historically offered to investors and do NOT represent returns that any investor actually attained. Past performance may not be repeated.
22
Annual Return – Dynamic S&P 500 Index Option Writing Strategy (Lev. of 1.75x)
Dynamic S&P 500 Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1990 to Dec. 2014
Dynamic S&P 500 Index Option Writing Strategy (Notional Leverage of 1.75x) – Jan. 1990 to Dec. 2014
80
800
Dec
-89
Dec
-90
Dec
-91
Dec
-92
Dec
-93
Dec
-94
Dec
-95
Dec
-96
Dec
-97
Dec
-98
Dec
-99
Dec
-00
Dec
-01
Dec
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-14
Dynamic Put Call X1.75 MSCI USA Low Vol
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
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98
19
99
20
00
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01
20
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Dynamic Put Call X1.75 MSCI USA Low Vol
Dynamic Put
Call X1.75
MSCI USA
Low Vol
Dynamic Put
Call X1.75
MSCI USA
Low Vol
1990 3.05% -1.87% 2003 32.62% 19.98%
1991 19.21% 29.06% 2004 11.24% 14.51%
1992 23.71% 6.58% 2005 9.23% 6.62%
1993 18.07% 11.99% 2006 23.45% 14.96%
1994 -0.92% 0.28% 2007 1.05% 4.31%
1995 40.32% 36.79% 2008 -10.35% -25.65%
1996 28.11% 15.16% 2009 32.88% 18.36%
1997 28.48% 30.40% 2010 19.90% 14.70%
1998 10.56% 23.01% 2011 5.91% 12.87%
1999 21.79% 7.80% 2012 13.92% 11.19%
2000 -2.53% 2.83% 2013 14.08% 25.33%
2001 -2.73% -7.82% 2014 13.14% 16.54%
2002 -11.20% -15.31%
Dynamic Put
Call X1.75
MSCI USA
Low Vol
Return 12.91% 9.97%
Standard Dev. 11.32% 11.56%
Risk Free Rate 3.42% 3.42%
Sharp Ratio 0.84 0.57
o On average, there is an inverse relationship between significant change in equity index returns and change in its underlying volatility. Contrary to popular belief, this relationship is not easily exploitable.
o Change in volatility is a significant risk for investors. During period of Low Quality (Bad Volatility), equity markets tend to deliver poor sharp ratio and better risk-adjusted returns can be found somewhere else.
o Our back-testing illustrate how an option writing strategy can benefit from an active management decision based on volatility regime.
0%
5%
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30%
1000
10000
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79
19
80
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81
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82
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84
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91
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00
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Volatility SPTX_PI
Volatility Regime Strategy - Conclusion
Financial Innovation
23
1.36
-0.44
0.49
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Low Vol High Vol Index
Return to Risk Ratio - MSCI World Net. Ret. (USD)Jan 1988 to Sept 2014
70.00
700.00
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9
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Systematic Put VRSM Put/Call
Appendix
OpenMind Capital - February 2015
Financial Innovation
Financial Innovation
Test Model's Robustness
To test the model robustness, we built 1000 S&P 500 Index path (from 1970 to 2014) by swapping randomly daily return up to 100 days backward and forward . We then run our model with different parameters on each of these scenarios.
Scenario 1 to 100 (1985 to 1989)
Financial Innovation
Test Model's Robustness
Parameter A
Par
ame
ter
B
Simulation (1000 scenarios) - Average Excess Return – 1970 to 2014
Financial Innovation
Test Model's Robustness
Simulation (1000 scenarios) – Standard Deviation of Excess Return – 1970 to 2014
Parameter A
Par
ame
ter
B
Financial Innovation
Test Model's Robustness
Excess Return Distribution (4,4) – 1969 to 2014
Excess Return Distribution (6,6) – 1969 to 2014
Excess Return Distribution (5,5) – 1969 to 2014
Excess Return Distribution (7,7) – 1969 to 2014
Karl Gauvin, M.sc. Karl Gauvin has twenty-one years experience in the field of investment management (capital markets). He held numerous positions at portfolio management and product development levels in firms like TAL Global Asset Management, Brockhouse Cooper and Montrusco Bolton Investments. In 2007/2008 he took a temporary leave from his area of expertise and invest (angel investor) in 8D Technologies. The company is the developer of the original IT solution that propels the street bike rental system "BiXi" and has been sold to dozens of cities worldwide. During his career, Mr. Gauvin has managed the development of nearly twenty investment products that generated assets under management of more than eight billion Canadian dollars. In particular, he was one of the first to include the use of financial derivatives (options and futures) in the mutual fund industry in Canada and to promote the use of systematic financial risk management technique for traditional investment strategies. Since March 2014, he acts as an independent consultant in the field of asset management. His extensive expertise in this sector gives him an ability to manage projects at all levels of the management chain of financial products (investment, marketing, distribution, compliance and information systems). Mr. Gauvin holds a M.Sc. in Finance from H.E.C. (Université de Montréal) and an undergraduate degree in Business, Finance and Economics.
Paul Turcotte, Ph.D. Paul Turcotte has held multiple positions at Caisse de dépôt et placement du Québec (CDPQ). In 2007, he was responsible for assessing the technical/expertise needs for the establishment of a new quantitative analysis group. From 2002 to 2005, as a quantitative analyst, he developed a real-time analysis system for data gathering, interest rate curve and volatility surface computations. From 1998 to 2002, he developed Sarah, the software used to evaluate financial market risks for Caisse’s global portfolio. In addition to designing the methodology and the architecture of the system, he provided expertise to managers on risk allocation strategies. During this period, Mr. Turcotte presented risk measurement and management methods both internally and in « Become a CFA » sessions as well as in scientific conferences and at Université de Sherbrooke as a guess lecturer. Before working at CDPQ , Mr. Turcotte worked as a post-doctorate researcher in numerical imaging in Montreal and also in fundamental physics in Taiwan. From 2010 to 2013, Mr. Turcotte was Chief Risk Officer for Akira Capital, a Montreal based discretionary commodity Hedge Fund. Mr. Turcotte holds a Ph.D . in physics from Université de Montréal. His thesis was on subatomic particle phenomenology. He also holds a Bachelor in physics degree from Université de Montréal.