The post Is There a Less Expensive Hedge Than a Protective Put ? appeared first on Relative Value Arbitrage.
]]>With the cost of insurance so high, is there a less expensive way for investors to hedge their portfolios?
One might think immediately of cross-asset hedging. However, if we use other underlying to hedge, we will then take on correlation (basis) risk and if not managed correctly, it can add risks to the portfolio instead of protecting it.
In this post we examine different hedging strategies using instruments on the same underlying. Our goal is to investigate the cost, risk/reward characteristics of each hedging strategy. Knowing the risk/reward profiles will allow us to design a cost-effective portfolio-protection scheme.
We will use Monte Carlo (MC) simulation to accomplish our goal [1]. The parameters and assumptions of the MC simulation are as follows:
Parameter | Value |
Initial stock price | 100 |
Volatility | 20% |
Risk-free rate | 0.02 |
Drift | 0.07 |
Days in simulation | 252 |
Time step (day) | 1 |
Number of paths | 10000 |
Model | GBM [2] |
The hedging strategies we’re investigating are:
1-NO HEDGE: no hedging is performed. The asset is allowed to evolve freely in a risky world. This would correspond to the portfolio of a Buy and Hold investor.
2-PPUT: protective put. We buy an at the money (ATM) put in order to hedge the downside. This strategy is the most common type of portfolio insurance.
3-GAMMA: convexity hedge. We buy an ATM put, but we then dynamically hedge it. This means that we flatten out the delta at the end of every day.
The GAMMA hedging strategy is not used frequently in the industry. The rationale for introducing it here is that given a high price of a put option, we will try to partially recoup its cost by actively scalping gamma, while we still benefit from the positive convexity of the option. This means that in case of a market correction, the gamma will manufacture negative delta so that the hedging position can offset some of the loss in the equity portfolio.
We use 10000 paths in our MC simulation. At the end of 1 year, we calculate the returns (using a Reg-T account) and determine its mean and variance. We also calculate the Value at Risk at 95% confidence interval. The graph below shows the histogram of the returns for the NO HEDGE strategy,
Table below presents the expected returns, standard deviations and Value at Risks for the hedging strategies.
Strategies | Expected return | Standard Deviation | Value at Risk |
NO HEDGE | 0.075 | 0.048 | 0.318 |
PPUT | 0.052 | 0.024 | 0.118 |
GAMMA | 0.066 | 0.029 | 0.248 |
As it is observed from the table, hedging with a protective put (PPUT) reduces the risks. Standard deviation and VaR are reduced from 0.048 and 0.318 to 0.024 to 0.118 respectively. However, the expected return is also reduced, from 0.075 to 0.052. This reduction is the cost of the insurance.
Interestingly, hedging using gamma convexity (GAMMA strategy) provides some reduction in risks (Standard deviation of 0.029, and VaR of 0.248), while not diminishing the returns greatly (expected return of 0.066).
In summary, GAMMA hedging is a strategy that is worth considering when designing a portfolio insurance scheme. It’s a good alternative to the often used protective (and expensive) put strategy.
Footnotes
[1] We note that the simulations were performed under idealistic assumptions, some are advantageous, and some are disadvantageous compared to a real life situation. However, results and the conclusion are consistent with our real world experience.
[2] GBM stands for Geometric Brownian Motion.
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]]>The post Forward Volatility and VIX Futures appeared first on Relative Value Arbitrage.
]]>As briefly mentioned in the footnotes of the blog post entitled “A Volatility Term Structure Based Trading Strategy”, VIX futures represent the (risk neutral) expectation values of the forward implied volatilities and not the spot VIX. The forward volatility is calculated as follows,
Using the above equation, and using the VIX index for σ_{0,t} , VXV for σ_{0,T}, we obtain the 1M-3M forward volatility as shown below.
Graph below shows the prices of VXX (green and red bars) and VIX April future (yellow line) for approximately the same period. Notice that their prices have increased since mid February, along with the forward volatility, while the spot VIX (not shown) has been more or less flat.
If you define the basis as VIX futures price-spot VIX, then you will observe that last week this basis widened despite the fact that time to maturity shortened.
In summary, VIX futures and ETF traders should pay attention to forward volatilities, in addition to the spot VIX. Forward and spot volatilities often move together, but they diverge from time to time. The divergence is a source of risk as well as opportunity.
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]]>The post A Volatility Skew Based Trading Strategy appeared first on Relative Value Arbitrage.
]]>1-Implied/realized volatilities (IV/RV)
2-Term structure
3-Skew
A summary of the systems developed based on the first 2 risk premia was published in this post.
In this article, we will attempt to build a trading system based on the third type of risk premium: volatility skew. As a measure of the volatility skew, we use the CBOE SKEW index.
According to the CBOE website, the SKEW index is calculated as follows,
The CBOE SKEW Index (“SKEW”) is an index derived from the price of S&P 500 tail risk. Similar to VIX®, the price of S&P 500 tail risk is calculated from the prices of S&P 500 out-of-the-money options. SKEW typically ranges from 100 to 150. A SKEW value of 100 means that the perceived distribution of S&P 500 log-returns is normal, and the probability of outlier returns is therefore negligible. As SKEW rises above 100, the left tail of the S&P 500 distribution acquires more weight, and the probabilities of outlier returns become more significant. One can estimate these probabilities from the value of SKEW. Since an increase in perceived tail risk increases the relative demand for low strike puts, increases in SKEW also correspond to an overall steepening of the curve of implied volatilities, familiar to option traders as the “skew”.
Our system’s rules are as follows:
Buy (or Cover) VXX if SKEW >= 10D average of SKEW
Sell (or Short) VXX if SKEW < 10D average of SKEW
The table below summarizes important statistics of the trading system
Initial capital | 10000 |
Ending capital | 40877.91 |
Net Profit | 30877.91 |
Net Profit % | 308.78% |
Exposure % | 99.50% |
Net Risk Adjusted Return % | 310.34% |
Annual Return % | 19.56% |
Risk Adjusted Return % | 19.66% |
Max. system % drawdown | -76.00% |
Number of trades | 538 |
Winners | 285 (52.97 %) |
The graph below shows the equity line from February 2009 to December 2016
We observe that this system does not perform well as the other 2 systems [1]. A possible explanation for the weak performance is that VXX and other similar ETFs’ prices are affected more directly by the IV/RV relationship and the term structure than by the volatility skew. Hence using the volatility skew as a timing mechanism is not as accurate as other volatility indices.
In summary, the system based on the CBOE SKEW is not as robust as the VRP and RY systems. Therefore we will not add it to our existing portfolio of trading strategies.
Footnotes
[1] We also tested various combinations of this system and results lead to the same conclusion.
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]]>The post Relationship Between the VIX and SP500 Revisited appeared first on Relative Value Arbitrage.
]]>While the S&P 500 Index rose to an all-time high for a second day, the advance was accompanied by a gain in an options-derived gauge of trader stress that usually moves in the opposite direction
The article refers to a well-known phenomenon that under normal market conditions, the VIX and SP500 indices are negatively correlated, i.e. they tend to move in the opposite direction. However, when the market is nervous or in a panic mode, the VIX/SP500 relationship can break down, and the indices start to move out of whack.
In this post we revisit the relationship between the SP500 and VIX indices and attempt to quantify their dislocation. Knowing the SP500/VIX relationship and the frequency of dislocation will help options traders to better hedge their portfolios and ES/VX futures arbitrageurs to spot opportunities.
We first investigate the correlation between the SP500 daily returns and change in the VIX index [1]. The graph below depicts the daily changes in VIX as a function of SP500 daily returns from 1990 to 2016.
We observe that there is a high degree of correlation between the daily SP500 returns and daily changes in the VIX. We calculated the correlation and it is -0.79 [2].
We next attempt to quantify the SP500/VIX dislocation. To do so, we calculated the residuals. The graph below shows the residuals from January to December 2016.
Under normal market conditions, the residuals are small, reflecting the fact that SPX and VIX are highly (and negatively) correlated, and they often move in lockstep. However, under a market stress or nervous condition, SPX and VIX can get out of line and the residuals become large.
We counted the percentage of occurrences where the absolute values of the residuals exceed 1% and 2% respectively. Table below summarizes the results
Threshold | Percentage of Occurrences |
1% | 17.6% |
2% | 3.9% |
We observe that the absolute values of SP500/VIX residuals exceed 1% about 17.6% of the time. This means that a delta-neutral options portfolio will experience a daily PnL fluctuation in the order of magnitude of 1 vega about 17% of the time, i.e. about 14 times per year. The dislocation occurs not infrequently.
The Table also shows that divergence greater than 2% occurs less frequently, about 3.8% of the time. This year, 2% dislocation happened during the January selloff, Brexit and the US presidential election.
Most of the time this kind of divergence is unpredictable. It can lead to a marked-to-market loss which can force the trader out of his position and realize the loss. So the key in managing an options portfolio is to construct positions such that if a divergence occurs, then the loss is limited and within the allowable limit.
Footnotes:
[1] We note that under different contexts, the percentage change in VIX can be used in a correlation study. In this post, however, we choose to use the change in the VIX as measured by daily point difference. We do so because the change in VIX can be related directly to Vega PnL of an options portfolio.
[2] The scope of this post is not to study the predictability of the linear regression model, but to estimate the frequency of SP500/VIX divergence. Therefore, we applied linear regression to the whole data set from 1990 to 2016. For more accurate hedges, traders should use shorter time periods with frequent recalibration.
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]]>The post Volatility Trading Strategies, a Comparison of Volatility Risk Premium and Roll Yield Strategies appeared first on Relative Value Arbitrage.
]]>In previous posts, we presented 2 volatility trading strategies: one strategy is based on the volatility risk premium (VRP) and the other on the volatility term structure, or roll yield (RY). In this post we present a detailed comparison of these 2 strategies and analyze their recent performance.
The first strategy (VRP) is based on the volatility risk premium. The trading rules are as follows [1]:
Buy (or Cover) VXX if VIX index <= 5D average of 10D HV of SP500
Sell (or Short) VXX if VIX index > 5D average of 10D HV of SP500
The second strategy (RY) is based on the contango/backwardation state of the volatility term structure. The trading rules are as follows:
Buy VXX if 5-Day Moving Average of VIX/VXV >=1 (i.e. backwardation)
Sell VXX if 5-Day Moving Average of VIX/VXV < 1 (i.e. contango)
Table below presents the backtested results from January 2009 to December 2016. The starting capital is $10000 and is fully invested in each trade (different position sizing scheme will yield different ending values for the portfolios. But the percentage return of each trade remains the same)
RY | VRP | |
Initial capital | 10000 | 10000 |
Ending capital | 179297.46 | 323309.02 |
Net Profit | 169297.46 | 313309.02 |
Net Profit % | 1692.97% | 3133.09% |
Exposure % | 99.47% | 99.19% |
Net Risk Adjusted Return % | 1702.07% | 3158.54% |
Annual Return % | 44.22% | 55.43% |
Risk Adjusted Return % | 44.46% | 55.88% |
Max. system % drawdown | -50.07% | -79.47% |
Number of trades trades | 32 | 55 |
Winners | 15 (46.88 %) | 38 (69.09 %) |
We observe that RY produced less trades, has a lower annualized return, but less drawdown than VRP. The graph below depicts the portfolio equities for the 2 strategies.
It is seen from the graph that VRP suffered a big loss during the selloff of Aug 2015, while RY performed much better. In the next section we will investigate the reasons behind the drawdown.
The graph below depicts the 10-day HV of SP500 (blue solid line), its 5-day moving average (blue dashed line), the VIX index (red solid line) and its 5-day moving average (red dashed line) during July and August 2015. As we can see, an entry signal to go short was generated on July 21 (red arrow). The trade stayed short until an exit signal was triggered on Aug 31 (blue arrow). The system exited the trade with a large loss.
The reason why the system stayed in the trade while SP500 was going down is that during that period, the VIX was always higher than 5D MA of 10D HV. This means that 10D HV was not a good approximate for the actual volatility during this highly volatile period. Recall that the expectation value of the future realized volatility is not observable. This drawdown provides a clear example that estimating actual volatility is not a trivial task.
By contrast, the RY strategy was more responsive to the change in market condition. It went long during the Aug selloff (blue arrow in the graph below) and exited the trade with a gain. The responsiveness is due to the fact that both VIX and VXV used to generate trading signals are observable. The graph below shows VIX/VXV ratio (black line) and its 5D moving average (red line).
In summary, we prefer the RY strategy because of its responsiveness and lower drawdown. Both variables used in this strategy are observable. The VRP, despite being based on a good ground, suffers from a drawback that one of its variables is not observable. To improve it, one should come up with a better estimate for the expectation value of the future realized volatility. This task is, however, not trivial.
References
[1] T Cooper, Easy Volatility Investing, SSRN, 2013
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]]>The post October was Favorable for Short Volatility Strategies appeared first on Relative Value Arbitrage.
]]>First, the main PnL driver of a delta neutral, short gamma and short vega strategy is the spread between the implied volatility (IV) and the subsequently realized volatility (RV) of returns. Trading strategies such as long butterfly is profitable when, during the life of the position, RV is low compared to IV. The graph below shows the difference between IV and RV for SP500 during the last 5 months. (Note that RV is shifted by 1 month, so that IV-RV presents accurately the spread between the implied volatility and the volatility realized during the following month). As we can see from the graph, IV-RV was high, around 4%-7%, during October (the area around the “10/16” mark). Hence short volatility strategies were generally profitable during October.
The second reason for the profitability is more subtle. The graph below shows the IV-RV spreads in function of monthly returns. As we can see, there is a high degree of correlation between IV-RV and the monthly returns. In fact, we calculated the correlation for the last 10 years and it is 0.69
This means that when IV-RV is high, SP500 usually trends up. This was the case, for example, during the month after Brexit (see the area around the “07/16” mark on the first graph). However, when the market trends, the cost of hedging in order to keep the position delta neutral is high. By contrast, even though IV-RV was high in October, the market moved in a range, thus helping us to minimize our hedging costs. This factor therefore contributed to the profitability of short volatility strategies.
In summary, October was favorable for short volatility strategies due to the high IV-RV spread and the range bound nature of the market.
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]]>The post A Volatility Term Structure Based Trading Strategy appeared first on Relative Value Arbitrage.
]]>In this article, we explore another volatility trading strategy, also discussed in Ref [1]. This strategy is based on the volatility term structure [2].
It is well known that volatilities exhibit a term structure which is similar to the yield curve in the interest rate market. The picture below depicts the volatility term structure for SP500 as at August 31 2016 [3].
Most of the time the term structure is in contango. This means that the back months have higher implied volatilities than the front months. However, during a market stress, the volatility term structure curve usually inverts. In this case we say that the volatility term structure curve is in backwardation (a similar phenomenon exists in the interest rate market which is called inversion of the yield curve).
The basic idea of the trading strategy is to use the state (contango/backwardation) of the volatility term structure as a timing mechanism. Specifically, we go long if the volatility term structure is in backwardation and go short otherwise. To measure the slope of the term structure, we use the VIX and VXV volatility indices which represent the 1M and 3M implied volatilities of SP500 respectively.
The trading rules are as follows,
Buy VXX if 5-Day Moving Average of VIX/VXV >=1 (i.e. backwardation)
Sell VXX if 5-Day Moving Average of VIX/VXV < 1 (i.e. contango)
The Table below presents the results
All trades | Long trades | Short trades | |
Initial capital | 10000 | 10000 | 10000 |
Ending capital | 177387.15 | 19232.01 | 168155.15 |
Net Profit | 167387.15 | 9232.01 | 158155.15 |
Net Profit % | 1673.87% | 92.32% | 1581.55% |
Exposure % | 99.44% | 6.64% | 92.80% |
Net Risk Adjusted Return % | 1683.22% | 1390.38% | 1704.17% |
Annual Return % | 46.07% | 9.00% | 45.05% |
Risk Adjusted Return % | 46.33% | 135.54% | 48.54% |
All trades | 30 | 15 (50.00 %) | 15 (50.00 %) |
Avg. Profit/Loss | 5579.57 | 615.47 | 10543.68 |
Avg. Profit/Loss % | 13.29% | 2.29% | 24.28% |
Avg. Bars Held | 64.53 | 9.8 | 119.27 |
Winners | 14 (46.67 %) | 4 (13.33 %) | 10 (33.33 %) |
Total Profit | 208153.11 | 36602.85 | 171550.26 |
Avg. Profit | 14868.08 | 9150.71 | 17155.03 |
Avg. Profit % | 36.58% | 25.17% | 41.14% |
Avg. Bars Held | 129.64 | 14.25 | 175.8 |
Max. Consecutive | 3 | 1 | 4 |
Largest win | 71040.59 | 18703.17 | 71040.59 |
# bars in largest win | 157 | 35 | 157 |
Losers | 16 (53.33 %) | 11 (36.67 %) | 5 (16.67 %) |
Total Loss | -40765.96 | -27370.84 | -13395.12 |
Avg. Loss | -2547.87 | -2488.26 | -2679.02 |
Avg. Loss % | -7.09% | -6.02% | -9.45% |
Avg. Bars Held | 7.56 | 8.18 | 6.2 |
Max. Consecutive | 5 | 6 | 2 |
Largest loss | -9062.89 | -8222.29 | -9062.89 |
# bars in largest loss | 6 | 8 | 6 |
Max. trade drawdown | -28211.89 | -15668.21 | -28211.89 |
Max. trade % drawdown | -23.97% | -18.20% | -23.97% |
Max. system drawdown | -32794.28 | -26555.13 | -37915.18 |
Max. system % drawdown | -50.07% | -90.85% | -34.31% |
Recovery Factor | 5.1 | 0.35 | 4.17 |
CAR/MaxDD | 0.92 | 0.1 | 1.31 |
RAR/MaxDD | 0.93 | 1.49 | 1.41 |
Profit Factor | 5.11 | 1.34 | 12.81 |
Payoff Ratio | 5.84 | 3.68 | 6.4 |
Standard Error | 12109.91 | 6401.13 | 12526.9 |
Risk-Reward Ratio | 1.49 | 0.15 | 1.36 |
Ulcer Index | 11.25 | 42.48 | 8.24 |
Ulcer Performance Index | 3.62 | 0.08 | 4.81 |
Sharpe Ratio of trades | 0.8 | 0.53 | 0.97 |
K-Ratio | 0.0745 | 0.0073 | 0.0683 |
The graph below shows the portfolio equity from 2009 up to August 2016.
The annual rerun is 46% and the drawdown is 50%. There are 2 interesting observations
In the next installment we will compare the 2 strategies, volatility risk premium and roll yield, in details.
References
[1] T Cooper, Easy Volatility Investing, SSRN, 2013
[2] Note that there is a so-called term structure risk premium in the options market that is not often discussed in the literature. The strategy discussed in this post, however, is not meant to exploit the term structure risk premium. It uses the term structure as a timing mechanism.
[3] The volatility term structure presented here is calculated based on VIX futures, which are the expectation values of 30-day forward implied volatility. Therefore, it is theoretically different from the term structure of spot volatilities which are calculated from SP500 options. Practically speaking, the 2 volatility term structures are highly correlated, and we use the futures curve in this article for illustration purposes.
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]]>The post Volatility Trading Strategy, a System Based on Volatility Risk Premium appeared first on Relative Value Arbitrage.
]]>Buy (or Cover) VXX if VIX index <= 5D average of 10D HV of SP500
Sell (or Short) VXX if VIX index > 5D average of 10D HV of SP500
The strategy performed well in backtest. In this follow-up post, we look at how it has performed since last year. The Table below summarizes the results
All trades | Long trades | Short trades | |
Initial capital | 10000 | 10000 | 10000 |
Ending capital | 3870.55 | 9095.02 | 4775.53 |
Net Profit | -6129.45 | -904.98 | -5224.47 |
Net Profit % | -61.29% | -9.05% | -52.24% |
Exposure % | 100.00% | 14.67% | 85.33% |
Net Risk Adjusted Return % | -61.29% | -61.68% | -61.23% |
Annual Return % | -60.50% | -8.86% | -51.48% |
Risk Adjusted Return % | -60.50% | -60.42% | -60.33% |
All trades | 11 | 5 (45.45 %) | 6 (54.55 %) |
Avg. Profit/Loss | -557.22 | -181 | -870.74 |
Avg. Profit/Loss % | -3.35% | -4.40% | -2.47% |
Avg. Bars Held | 24.55 | 8.6 | 37.83 |
Winners | 6 (54.55 %) | 2 (18.18 %) | 4 (36.36 %) |
Total Profit | 2366.97 | 365.38 | 2001.59 |
Avg. Profit | 394.49 | 182.69 | 500.4 |
Avg. Profit % | 13.75% | 6.46% | 17.40% |
Avg. Bars Held | 32.5 | 7.5 | 45 |
Max. Consecutive | 2 | 2 | 2 |
Largest win | 1308.08 | 239.26 | 1308.08 |
# bars in largest win | 102 | 8 | 102 |
Losers | 5 (45.45 %) | 3 (27.27 %) | 2 (18.18 %) |
Total Loss | -8496.42 | -1270.36 | -7226.06 |
Avg. Loss | -1699.28 | -423.45 | -3613.03 |
Avg. Loss % | -23.87% | -11.64% | -42.21% |
Avg. Bars Held | 15 | 9.33 | 23.5 |
Max. Consecutive | 2 | 2 | 1 |
Largest loss | -6656.33 | -625.51 | -6656.33 |
# bars in largest loss | 29 | 11 | 29 |
The strategy produced 11 trades with 6 trades (55%) being winners. However, it suffered a big loss during August. The graph below shows the portfolio equity since last August.
Large losses are typical of short volatility strategies. An interesting observation is that after the large drawdown, the strategy has recovered, as depicted by the upward trending equity line after August. This is usually the case for short volatility strategies.
Despite the big loss, the overall return (not shown) is still positive. This means that the strategy has a positive expectancy. Drawdown can be minimized by using a correct position size, stop losses, and a good portfolio allocation scheme. Another solution is to construct limited-loss positions using VXX options.
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]]>The post Volatility Trading through VIX ETFs appeared first on Relative Value Arbitrage.
]]>where E_{Q} denotes the expectation value of X, a stochastic variable, in the risk-neutral world, and E_{P} denotes the expectation value of X in the real world. X can be, for example, commodity prices, FX rates, etc. See reference 1 for a thorough discussion of various risk premia in financial markets.
Of our particular interest is the case where X is the realized volatility (RV) of a stock or stock index. The risk premium in this case is often called volatility (or variance) risk premium (VRP). If we substitute X in the above equation with the RV of SP500, then E_{Q} (RV) becomes the VIX index, and the VRP becomes VIX- E_{P} (RV).
Traders often try to exploit the VRP by trading listed options or OTC variance swaps. Another way to harvest the VRP is through trading VIX -based Exchange Trade Funds such as VXX. In this post we explore the latter possibility.
The most difficult problem when designing a VRP-based trading strategy is that E_{P}(RV), which is needed in order to calculate the VRP, is not observable. The best we can do is to use a quantitative method to estimate it.
Reference 2 tested various forms of E_{P}(RV) estimate: GARCH, historical volatilities (HV). The author found that 10-day HV is the most effective. They then further smoothed out the HV by using a 5-day moving average in order to avoid whipsaws. The trading rules are as follows:
Buy (or Cover) VXX if VIX index <= 5D average of 10D HV of SP500
Sell (or Short) VXX if VIX index > 5D average of 10D HV of SP500
The Table below summarizes the trading strategy’s statistics. The starting capital is $10000. It is fully invested in each trade
All trades | Long trades | Short trades | |
Initial capital | 10000 | 10000 | 10000 |
Ending capital | 727774.91 | -74688.89 | 812463.8 |
Net Profit | 717774.91 | -84688.89 | 802463.8 |
Net Profit % | 7177.75% | -846.89% | 8024.64% |
Exposure % | 99.02% | 6.58% | 92.44% |
Net Risk Adjusted Return % | 7248.42% | -12868.01% | 8680.57% |
Annual Return % | 93.38% | N/A | 96.68% |
Risk Adjusted Return % | 94.29% | N/A | 104.58% |
All trades | 43 | 21 (48.84 %) | 22 (51.16 %) |
Avg. Profit/Loss | 16692.44 | -4032.8 | 36475.63 |
Avg. Profit/Loss % | 12.04% | 2.34% | 21.29% |
Avg. Bars Held | 38.79 | 6.14 | 69.95 |
Winners | 32 (74.42 %) | 12 (27.91 %) | 20 (46.51 %) |
Total Profit | 918340.82 | 94671.52 | 823669.31 |
Avg. Profit | 28698.15 | 7889.29 | 41183.47 |
Avg. Profit % | 18.13% | 8.45% | 23.93% |
Avg. Bars Held | 47.47 | 5.92 | 72.4 |
Max. Consecutive | 9 | 5 | 8 |
Largest win | 198505.33 | 36704.45 | 198505.33 |
# bars in largest win | 78 | 7 | 78 |
Losers | 11 (25.58 %) | 9 (20.93 %) | 2 (4.65 %) |
Total Loss | -200565.92 | -179360.41 | -21205.5 |
Avg. Loss | -18233.27 | -19928.93 | -10602.75 |
Avg. Loss % | -5.69% | -5.82% | -5.10% |
Avg. Bars Held | 13.55 | 6.44 | 45.5 |
Max. Consecutive | 3 | 4 | 1 |
Largest loss | -38701.76 | -38701.76 | -16516.3 |
# bars in largest loss | 4 | 4 | 41 |
Max. trade drawdown | -300537.33 | -38701.76 | -300537.33 |
Max. trade % drawdown | -46.12% | -12.49% | -46.12% |
Max. system drawdown | -331363.27 | -164250.75 | -300537.33 |
Max. system % drawdown | -52.21% | -95.16% | -64.93% |
Recovery Factor | 2.17 | -0.52 | 2.67 |
CAR/MaxDD | 1.79 | N/A | 1.49 |
RAR/MaxDD | 1.81 | N/A | 1.61 |
Profit Factor | 4.58 | 0.53 | 38.84 |
Payoff Ratio | 1.57 | 0.4 | 3.88 |
Standard Error | 56932.62 | 32093.21 | 72795.73 |
Risk-Reward Ratio | 1.67 | -0.46 | 1.51 |
Ulcer Index | 12.97 | 111.97 | 11.45 |
Ulcer Performance Index | 6.78 | N/A | 7.97 |
Sharpe Ratio of trades | 1.46 | 1.36 | 1.71 |
K-Ratio | 0.0775 | -0.0214 | 0.07 |
We observe that the short trades are profitable while the long ones lost money. The CARG is 93.4%, which is high, but so is the drawdown of -52%. The graph below shows the portfolio equity
In summary, the VRP can be harvested through VIX ETF. However the drawdown is high. This strategy is viable if it is part of an asset allocation scheme. It would enhance the portfolio risk-adjusted return if we allocate, for example, 10% of our portfolio to this strategy, and the rest to equity and fixed income investments.
References
[1 ] A. Ilmanen, Expected Returns: An Investor’s Guide to Harvesting Market Rewards, John Wiley & Sons , 2011
[2] T Cooper, Easy Volatility Investing, SSRN, 2013
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]]>The post Pricing Convertible Bonds-An Example appeared first on Relative Value Arbitrage.
]]>The specifics of the hypothetical convertible bond are as follows,
INPUTS | |
Stock price | 100 |
Volatility | 0.25 |
Risk Free Rate | 0.02 |
Risky Rate (risk free+credit spread) | 0.08 |
Coupon | 0.06 |
Maturity (in years) | 5 |
Conversion ratio | 10 |
Using the formula provided in the previous post, we calculated the up and down moves, and probability of the up move. The results are
MODEL PARAMETERS | |
dt | 1.00 |
u | 1.28 |
d | 0.78 |
Probability Up | 0.48 |
Once the tree parameters are calculated, we next build the tree, and then work backward from the end nodes in order to obtain the convertible bond’s price at time zero. As the final result, the bond price is $1319 (per $1000 notional)
To see the details of the calculations of the tree, click on the link below to download an Excel spreadsheet.
Note that this is a simplified example. In real life, convertible bonds are usually more complex. They often include features such as call, put, contingency conversion options. The call and put options can be implemented using the formula given in the previous post.
I hope that this and previous posts demystified a little bit the complexities of convertible bonds. Let us know if you have any questions.
The post Pricing Convertible Bonds-An Example appeared first on Relative Value Arbitrage.
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