Financial markets and Real economy interoperate to create prosperity in the world. Financial economy acts as a barometer to provide instant feel of the state of the economy. Ted spread is an indicator of stress in the financial economy. High Ted spread suggests markets are experiencing deep stress and low ted spread will tell us converse is true. Real economy indicators like ISM report, Housing sector indicators gives us an indication of actual picture of what is happening in the real economy. Ultimately growth rate, unemployment and inflation are three indicators that will form the base of the economy and base of the financial and real economy pyramid.
Humanity around the world is always in need of goods and services for its survival. In ancient times people lived in isolated corners of the world by living with whatever is available in their surroundings. Once humans evolved from hunting societies to civilized societies where food and other goods are secured tug of war has took place between different societies. In the words of Darwin fittest society survived. Today, we witness similar fight for survival among various countries happening all around us. Countries are scouring across the world for energy resources, cheaper services and production of goods. Technological advancements have helped accelerate this process that started to make the world look more tightly integrated and known as globalization.
Although societies across the world today have global bodies like UN, IMF and other associations to resolve and safeguard rights we still witness extreme poverty, rise and fall of societies. This can be attributed to fact that individual societies operate on policies that can make or break them. For instance, African countries were given aid by western countries are not seeing the light of prosperity mainly because of lack of proper governance policy. These things have main implications for differential growth rates, inflation and unemployment levels in these countries. This differential will become base for the FX market, interest rates and commodity markets. Together these markets will become base for the equity markets locally.
Macro Trading is identifying opportunities in FX, IR and commodity sectors that are arising due to dislocations caused by governance policy. These policies can be like, Quantitative easing policy followed by central banks to fight credit crisis, Chinese and Japanese purchasing of US treasury bonds to keep recycling their dollars obtained by selling their goods to world, famous Mugabe’s printing money to buy grocery items. These brute force acts comes with lot of side effects and macro traders are after these kind of opportunities. Along with macro trading, systematic trading can be pursued where markets follow certain rules.
I call trading style that includes Macro themes with systematic trading strategies macro-Algo trading and if this style is based on mathematical themes, quant-macro-algo trading. Futures, futures options and spot contracts provide suitable vehicles to monetize the themes.
Sunday, May 31, 2009
Saturday, May 30, 2009
Fisher says economy has long way to go to recover
Review of comments from Dallas Fed:
A very slow recovery
Economy has a long way to go before a clear recovery starts kicking.
Fisher says “A sudden new set of circumstances, easy money seemingly heaven-sent and the short-sighted suspension of time-tested, prudent financial practice led us on the road, not to salvation, but to economic perdition.” To aid this argument fable about a man visiting Episcopal church after getting aid from a presbyterian pastor describes the current economic situation very aptly.
Geo Political factors and Technological factors
End of cold war and commercial reorientation of Asian and eastern European countries has unleashed enormous new capacity for production of goods and services and capped the costs. Technological advancements have created new forces that connected remote corners of the world to trading centers and became source for new place to invest and speculate.
Fed is trying to prevent past errors and of course it acted as pastor earlier and has not stopped from being so.
The Green Shoots (Financial Economy)
Revival of Commercial paper Market
Decline of Mortgage rates significantly
Robust corporate bond issuance market
Invest grade corps premium over treasury has fallen
All measures compared to last fall have improved.
The Green Shoots (Main Street)
New orders index shows rise
Moderations in decline of activity (Fed survey)
Job losses are reduced
Monetary policy initiatives have given rise to these green shoots. Side effects are inflation. But fed is very aware of these facts and taking every measure not unleash the ugly monster of inflation.
So far financial markets have taken these green shoots and have risen from last week. From here where are we headed next week? Next we have GM bankruptcy filing and unemployment report.
A very slow recovery
Economy has a long way to go before a clear recovery starts kicking.
Fisher says “A sudden new set of circumstances, easy money seemingly heaven-sent and the short-sighted suspension of time-tested, prudent financial practice led us on the road, not to salvation, but to economic perdition.” To aid this argument fable about a man visiting Episcopal church after getting aid from a presbyterian pastor describes the current economic situation very aptly.
Geo Political factors and Technological factors
End of cold war and commercial reorientation of Asian and eastern European countries has unleashed enormous new capacity for production of goods and services and capped the costs. Technological advancements have created new forces that connected remote corners of the world to trading centers and became source for new place to invest and speculate.
Fed is trying to prevent past errors and of course it acted as pastor earlier and has not stopped from being so.
The Green Shoots (Financial Economy)
Revival of Commercial paper Market
Decline of Mortgage rates significantly
Robust corporate bond issuance market
Invest grade corps premium over treasury has fallen
All measures compared to last fall have improved.
The Green Shoots (Main Street)
New orders index shows rise
Moderations in decline of activity (Fed survey)
Job losses are reduced
Monetary policy initiatives have given rise to these green shoots. Side effects are inflation. But fed is very aware of these facts and taking every measure not unleash the ugly monster of inflation.
So far financial markets have taken these green shoots and have risen from last week. From here where are we headed next week? Next we have GM bankruptcy filing and unemployment report.
Sunday, May 24, 2009
WU long straddle
Exploiting volatility spread between Implied volatility and historical volatility. Western union Implied vols falling down once the rally picked up in the equity market. Current implied vols of 40 for aug 09 contracts is at its historical lows. This gives an opportunity to buy some volatility at this level.
WU aug 09 strike: 17, underlying: 16.51, straddle costing 3.1 debit. trade date: 5/24/09. for 10 contracts, net debit will be 3100.
this position will loose if underlying moves nowhere. Time decay will be eating into the premium. Best case for this trade is underlying moving above 20 or below 14. rising volatility will add some more to our profits.
Since this is a long position, this will needs no initial margin requirements.
best trading
regards
Chandra khandrika
WU aug 09 strike: 17, underlying: 16.51, straddle costing 3.1 debit. trade date: 5/24/09. for 10 contracts, net debit will be 3100.
this position will loose if underlying moves nowhere. Time decay will be eating into the premium. Best case for this trade is underlying moving above 20 or below 14. rising volatility will add some more to our profits.
Since this is a long position, this will needs no initial margin requirements.
best trading
regards
Chandra khandrika
Thursday, May 7, 2009
IBM condor Option Trade
On april 21st i have decided to play a iron condor option on IBM. I have observed few things. IBM is trading at 100. 10 IBM May options 90/95/105/110 iron condor with a premium intake of 2400 and a risk of loss of 2600 if IBM is above 110 or below 90. So far IBM never breached these levels. looks like a decent trade with minimum risk.
Only one factor creating itch in my back is the trending market. Since March markets entered into a Bull market. No one is able to believe in this market. But market is rallying. Market has moved from the bottoms of 6500 to 8500 today.
Some observations on the trade. This trade is a 5 point Bear call spread and 5 point Bull put spread. This has a width of 10 point range. Implied volatility fell down and benefited the trade.
Now i am on to next trade.
Bye for now
Chandra Khandrika
Option Trader.
Only one factor creating itch in my back is the trending market. Since March markets entered into a Bull market. No one is able to believe in this market. But market is rallying. Market has moved from the bottoms of 6500 to 8500 today.
Some observations on the trade. This trade is a 5 point Bear call spread and 5 point Bull put spread. This has a width of 10 point range. Implied volatility fell down and benefited the trade.
Now i am on to next trade.
Bye for now
Chandra Khandrika
Option Trader.
Thursday, March 12, 2009
Forecasting markets-perspective
Whenever I see a fit personality, I conclude that person should be exercising well. In the same manner if I see a person doing exercise and his schedule then I should be able to tell how fit and unfit his body turns out to be. This needs keen observation and sense of understanding of exercise and human body. Also how these relations existed historically.
How this relates to economy and trading. To undertake trading as a serious business it needs understanding of how various movements can happen. What scenarios can prevail? Strong economies are symbols for stronger factors leading to growth. So what factors contribute towards growth can be inferred by looking at economic indicators. If that is the case then why we make so many poor judgments with regards to understanding economy. Markets (people) are collectively blind and individually wise. They swing from one side to other when cool breeze pass by them. In other words as Benjamin Graham said in the short term markets are like voting machine, but over the long term they are weighing machine.
When looking at individual economic indicators we can tell what is happening in that sector. But we fail to predict what will happen to economy as a whole. Economic indicators are a snapshot of the economy at a particular point of time. Extrapolating the economic potential from scattered economic indicators with some empirical reasoning might be akin to predicting stock markets with number of sun spots. No doubt, economic indicators play a role in measuring the economy. There is one aspect which we might be missing. People adjust their activities according to market environment. A classic example is cut back (adjusting) in personal consumption with a gloomy outlook of economy. Sophisticated predicting models may be adaptive and also they can factor most of the changes and kinks in economic growth but they cannot assimilate the human nature. Human nature can be detected by human alone. There are plenty of human actions that can be automated but to predict a human you need a human. Similarly to predict markets you need humans. Therefore trading remained as a human activity since the dawn of civilization. To predict the markets we need, flair to understand and relate the past. A good understanding of economic indicators and their functional relations to traded instruments. On top of this it requires a human touch of understanding human behavior. By blending all these insights properly we can come up with a good forecast. So for instance to predict gold prices, we need to understand the nature of this commodity, its historical performance under various environments (inflation-deflation). From here we need to understand forces that will drive the price of gold. Towards the end market madness need to be included to come up with a genuine forecast.
This tells me clearly to trade an instrument, I need to know its past, present and market madness.
How this relates to economy and trading. To undertake trading as a serious business it needs understanding of how various movements can happen. What scenarios can prevail? Strong economies are symbols for stronger factors leading to growth. So what factors contribute towards growth can be inferred by looking at economic indicators. If that is the case then why we make so many poor judgments with regards to understanding economy. Markets (people) are collectively blind and individually wise. They swing from one side to other when cool breeze pass by them. In other words as Benjamin Graham said in the short term markets are like voting machine, but over the long term they are weighing machine.
When looking at individual economic indicators we can tell what is happening in that sector. But we fail to predict what will happen to economy as a whole. Economic indicators are a snapshot of the economy at a particular point of time. Extrapolating the economic potential from scattered economic indicators with some empirical reasoning might be akin to predicting stock markets with number of sun spots. No doubt, economic indicators play a role in measuring the economy. There is one aspect which we might be missing. People adjust their activities according to market environment. A classic example is cut back (adjusting) in personal consumption with a gloomy outlook of economy. Sophisticated predicting models may be adaptive and also they can factor most of the changes and kinks in economic growth but they cannot assimilate the human nature. Human nature can be detected by human alone. There are plenty of human actions that can be automated but to predict a human you need a human. Similarly to predict markets you need humans. Therefore trading remained as a human activity since the dawn of civilization. To predict the markets we need, flair to understand and relate the past. A good understanding of economic indicators and their functional relations to traded instruments. On top of this it requires a human touch of understanding human behavior. By blending all these insights properly we can come up with a good forecast. So for instance to predict gold prices, we need to understand the nature of this commodity, its historical performance under various environments (inflation-deflation). From here we need to understand forces that will drive the price of gold. Towards the end market madness need to be included to come up with a genuine forecast.
This tells me clearly to trade an instrument, I need to know its past, present and market madness.
Wednesday, March 11, 2009
Generic Execution Algo-definitions
Generic algorithms: Systematic execution of securities in the market can be performed in various ways. Each of these ways will serve one particular purpose. These Algorithms can belong to categories of Iceberg, TWAP, VWAP, Participation and Seek and Destroy.
Each algorithm has certain basic features that it shares commonly with all other algorithms. Limit price, Maximum or minimum price at which the algorithm will send orders to the market. Order quantity, the amount of quantity to trade.
ICE Berg: This algorithm sends out limited quantities of order at certain fixed price and continues to do so till the order is completed. Good thing about this algorithm is it will execute trades at desired price but on negative side some times order gets unfilled.
TWAP: Time weighted average Price algorithm is used to trade a fixed quantity in set time period. Order is broken down into discrete time intervals (waves) with an equal quantity to be traded in each wave.
VWAP: Volume weighted average price algorithm executes orders proportional to average historical market volume over the same period.
Participation (% Volume): This algorithm is used to trade up to the order quantity using a rate of execution that is proportional to the actual volume trading in the market.
Seek and destroy: This algorithm is designed to hide on the passive side of the order book until there is sufficient liquidity available on the aggressive side.
Limit on Close: The limit on close algorithm aims to trade the target quantity during the closing auction of the exchange. If no limit price is set it will trade at a limit of 2.5% of the last traded price before the auction.
Each algorithm has certain basic features that it shares commonly with all other algorithms. Limit price, Maximum or minimum price at which the algorithm will send orders to the market. Order quantity, the amount of quantity to trade.
ICE Berg: This algorithm sends out limited quantities of order at certain fixed price and continues to do so till the order is completed. Good thing about this algorithm is it will execute trades at desired price but on negative side some times order gets unfilled.
TWAP: Time weighted average Price algorithm is used to trade a fixed quantity in set time period. Order is broken down into discrete time intervals (waves) with an equal quantity to be traded in each wave.
VWAP: Volume weighted average price algorithm executes orders proportional to average historical market volume over the same period.
Participation (% Volume): This algorithm is used to trade up to the order quantity using a rate of execution that is proportional to the actual volume trading in the market.
Seek and destroy: This algorithm is designed to hide on the passive side of the order book until there is sufficient liquidity available on the aggressive side.
Limit on Close: The limit on close algorithm aims to trade the target quantity during the closing auction of the exchange. If no limit price is set it will trade at a limit of 2.5% of the last traded price before the auction.
Tuesday, March 10, 2009
option trading -Basics
In this article, short summary of Black, Scholes and Merton model, ways to measure and forecast volatility and dynamics of implied volatility surface are discussed.
BSM Model: Delta hedged portfolio consists of a call option and ∆ units of underlying short stock or future. With passage of time this portfolio will be rebalanced to make it delta neutral.
Option prices change due to passage of time, change in underlying and change in volatility. Of three components, change in underlying impacts the option price most.
From Taylor’s rule, we get a relation between volatility, theta and gamma.
Option trading using quoted prices and estimate of option volatility can have P/L effects in the following fashion.
GAMMA profits:
(½)*S2*Γ*(σ2- σ2implied)
Vega Profits:
Vega*(σ- σimplied)
Assumptions:
1) Underlying is a trade able asset
2) Underlying pays no dividends
3) Can short the underlying in any size
4) Interest rates are constant
5) Volatility is constant
6) Underlying changes continuously
Defining and measuring volatility
Volatility is defined as square root of variance. Variance is measured as
Unbiased estimate
To get unbiased estimate of volatility needs correction factor. This correction factor depends on the assumption of the underlying process follows particular distribution (Normal distribution).
There are other estimators that can be used to measure volatility. But due to simplicity and well understood sampling properties make this estimator most desirable.
Forecasting volatility:
Volatility is a mean reverting process
Volatility of volatility is positively related to level.
In making an estimate of volatility, we need to understand primarily what events are being included and what are getting excluded. Exponentially weighted moving average model does a good job in giving higher weights to recent events and lower weighting to past events.
In the above equation most recent return values are given weightings. Λ values used generally range between 0.9 and 0.99. One draw back of exponential moving averages is that they do not address the mean reversion nature of volatility. High volatility regimes follow calm and low volatility ranges.
GARCH (generalized auto-regressive conditional heteroskedasticity) models address the above issue of mean reversion. These models are developed by Engle-Bollerslev. But this model is not Holy Grail for forecasting volatility.
This equation is specification for GARCH (1,1) Model.
This model does good job in some situations. One undesirable feature for this model is that it needs estimation of calibration parameters. These estimates are not persistent and some times they tend to be highly unstable.
Volatility cones: Forecasting volatility involves in coming up with point estimates for the volatility. When we need a range of volatilities then volatility cones will come in aid for such analysis. This analysis was initially developed by Bughardt. In this analysis, what we try to find is chart a series of 10, 20, 40, 100, 300 days volatility for securities for non-overlapping periods. This will give a context to today’s volatility.
Implied volatility dynamics: Implied volatilities for a particular stock at different strikes and maturities form a 3D surface. This volatility surface will have different shapes and contours. From time to time market changes cause the shape to undergo changes. PCA (principal component analysis) when applied to yield curve data it provides insights to level, slope and curvature changes in the yield curve. Similarly when applied to implied volatilities as a deviation from ATM (at the money volatilities) we get similar factors that explain variation.
Level Dynamics: VIX Index published by CBOE.
Level of VIX has 3 regimes, less than 20, above 20 and below 40 and above 40.
Volatility of the index is positively related to the level.
There are more large ups compared to downs.
It is mean reverting and settles with new level at each regime.
ATM volatility level for contracts maturing from front month to last month will have embedded event volatility. In other words contract maturing after event will see a sharp drop in volatility.
Smile dynamics: Good understanding of volatility smile will give us a handle to spot best strike to trade. Volatility smile is a phenomena where OTM/ITM strikes trade at different volatilities compared to ATM. These volatilities can be higher or lower compared to ATM vol subjected to market conditions.
1) retail investors buy OTM strike options (akin to lottery ticket)
2) Large funds who buy downside protection and writing covered calls
Skewness and kurtosis are 3rd and 4th moments for a distribution. These two elements additionally required to specify a particular distribution. Jarrow and Rudd (1982) have made first attempt to include these two elements into option pricing. Corrado and Su (1996) have provided a better solution to estimate these parameters.
The European call price is given by
This equation will be solved for ATMVOL, SKEW and Kurtosis.
BSM Model: Delta hedged portfolio consists of a call option and ∆ units of underlying short stock or future. With passage of time this portfolio will be rebalanced to make it delta neutral.
Option prices change due to passage of time, change in underlying and change in volatility. Of three components, change in underlying impacts the option price most.
From Taylor’s rule, we get a relation between volatility, theta and gamma.
Option trading using quoted prices and estimate of option volatility can have P/L effects in the following fashion.
GAMMA profits:
(½)*S2*Γ*(σ2- σ2implied)
Vega Profits:
Vega*(σ- σimplied)
Assumptions:
1) Underlying is a trade able asset
2) Underlying pays no dividends
3) Can short the underlying in any size
4) Interest rates are constant
5) Volatility is constant
6) Underlying changes continuously
Defining and measuring volatility
Volatility is defined as square root of variance. Variance is measured as
Unbiased estimate
To get unbiased estimate of volatility needs correction factor. This correction factor depends on the assumption of the underlying process follows particular distribution (Normal distribution).
There are other estimators that can be used to measure volatility. But due to simplicity and well understood sampling properties make this estimator most desirable.
Forecasting volatility:
Volatility is a mean reverting process
Volatility of volatility is positively related to level.
In making an estimate of volatility, we need to understand primarily what events are being included and what are getting excluded. Exponentially weighted moving average model does a good job in giving higher weights to recent events and lower weighting to past events.
In the above equation most recent return values are given weightings. Λ values used generally range between 0.9 and 0.99. One draw back of exponential moving averages is that they do not address the mean reversion nature of volatility. High volatility regimes follow calm and low volatility ranges.
GARCH (generalized auto-regressive conditional heteroskedasticity) models address the above issue of mean reversion. These models are developed by Engle-Bollerslev. But this model is not Holy Grail for forecasting volatility.
This equation is specification for GARCH (1,1) Model.
This model does good job in some situations. One undesirable feature for this model is that it needs estimation of calibration parameters. These estimates are not persistent and some times they tend to be highly unstable.
Volatility cones: Forecasting volatility involves in coming up with point estimates for the volatility. When we need a range of volatilities then volatility cones will come in aid for such analysis. This analysis was initially developed by Bughardt. In this analysis, what we try to find is chart a series of 10, 20, 40, 100, 300 days volatility for securities for non-overlapping periods. This will give a context to today’s volatility.
Implied volatility dynamics: Implied volatilities for a particular stock at different strikes and maturities form a 3D surface. This volatility surface will have different shapes and contours. From time to time market changes cause the shape to undergo changes. PCA (principal component analysis) when applied to yield curve data it provides insights to level, slope and curvature changes in the yield curve. Similarly when applied to implied volatilities as a deviation from ATM (at the money volatilities) we get similar factors that explain variation.
Level Dynamics: VIX Index published by CBOE.
Level of VIX has 3 regimes, less than 20, above 20 and below 40 and above 40.
Volatility of the index is positively related to the level.
There are more large ups compared to downs.
It is mean reverting and settles with new level at each regime.
ATM volatility level for contracts maturing from front month to last month will have embedded event volatility. In other words contract maturing after event will see a sharp drop in volatility.
Smile dynamics: Good understanding of volatility smile will give us a handle to spot best strike to trade. Volatility smile is a phenomena where OTM/ITM strikes trade at different volatilities compared to ATM. These volatilities can be higher or lower compared to ATM vol subjected to market conditions.
1) retail investors buy OTM strike options (akin to lottery ticket)
2) Large funds who buy downside protection and writing covered calls
Skewness and kurtosis are 3rd and 4th moments for a distribution. These two elements additionally required to specify a particular distribution. Jarrow and Rudd (1982) have made first attempt to include these two elements into option pricing. Corrado and Su (1996) have provided a better solution to estimate these parameters.
The European call price is given by
This equation will be solved for ATMVOL, SKEW and Kurtosis.
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