Dispersion option strategy,stock market volatility in indian stock exchanges

Dispersion option strategy,stock market volatility in indian stock exchangesDispersion option strategy, stock market volatility in indian stock exchanges.

15-Nov-2015 18:25 by Administrator

This papers studies an options trading strategy known as dispersion strategy to investigate the apparent risk premium for bearing correlation risk in the But funds of option strategies means replicating hedge automobile industry stock. Traded index option strategies; variance swaps; dispersion scandal has evolved significantly since. What is dispersion trading? • Dispersion trading refers to trades in which one-- sells index options and buys options on the index components, or

Trading strategy dispersion

Trading strategy dispersionDISPERSION TRADING - Advanced Volatility Dispersion System

Volatility dispersion trading is a popular hedged strategy designed to take advantage of relative value differences in implied volatilities between an index and a basket of component stocks, looking for a high degree of dispersion. This strategy typically involves short option positions on an index, against which long option positions are taken on a set of components of the index. It is common to see a short straddle or near-ATM strangle on the index and long similar straddles or strangles on 30% to 40% of the stocks that make up the index. If maximum dispersion is realized, the strategy will make money on the long options on the individual stocks and will lose very little on the short option position on the index, since the latter would have moved very little. The strategy is typically a very low-premium strategy, with very low initial Delta and typically a small net long Vega.

The success of the strategy lies in determining which component stocks to pick. At the simplest level they should account for a large part of the index to keep the net risk low, but at the same time it is critical to make sure you are buying "cheap" volatility as well as candidates that are likely to "disperse."

DISPERSION TRADING provides volatility dispersion traders with current and historical measures on stock indices to determine the best time to engage in a dispersion strategy. It also provides several measures to help choose the components of the basket and create options portfolios on indexes and component stocks based on the trader's chosen strategy. Measures include implied correlation, Equivalent Index IV, Stock Specific Variance, contribution in Index IV and ratios of index volatilities calculated from the components vs. actual index vol.

Dispersion Trading is now Web-based and adds many new features such as:

Additional statistical measures on the indices such as correlation weighted implied and historical volatilities and their comparison to current volatility

Introduction of correlations of implied volatilities in the calculations

"Filters" to create user defined criteria when creating your portfolio

Portfolio statistics like implied volatility and implied correlation of the chosen basket

Ability to save portfolio to Excel

Key Features

Now that Dispersion Trading is completely available on the web, users do not have to worry about installation, feeds, firewall issues, etc.

IVolatility database

The numbers are calculated using IVolatility database, which is fast becoming an industry standard for equity options derived data.

Institutional

The Advanced Volatility Dispersion System is provided for general informational and educational purposes only and is not intended for trading purposes. The Advanced Volatility Dispersion System is not intended to provide investment advice or recommendations to purchase or sell securities.

Advanced Volatility Dispersion System

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Correlation trading strategies–opportunities and limitations

Correlation trading strategies–opportunities and limitationsSimilar Publications

Correlation Trading Strategies – Opportunities and Limitations

Gunter Meissner1

Key words: Correlation Trading, Pairs Trading, Multi-asset options, Dispersion Trading, Variance

dispersion trading

JEL Classification: G11

Abstract: This paper gives an overview and analyzes the most popular correlation trading

strategies in financial practice. Six correlation strategies are discussed: 1) Empirical Correlation

Trading, 2) Pairs Trading, 3) Multi-asset Options, 4) Structured Products, 5) Correlation Swaps,

and 6) Dispersion trading. This paper focuses on trading correlation, however, briefly in point 7,

the risk managing properties of correlation products are outlined.

1) Empirical Correlation Trading

Empirical Correlation Trading attempts to exploit historically significant correlations within

or between financial markets. Numerous financial correlations can be investigated. One area of

interest is the autocorrelation between stocks or indices. Figure 1 shows the autocorrelation of

the Dow Jones Industrial Index (Dow) from 1920 to 2014:

1 Gunter Meissner is President of Derivatives Software, dersoft, CEO of Cassandra Capital Management,

cassandracm and Adjunct Professor of MathFinance at NYU-Courant. He can be reached at

meissnerhawaii. edu

Figure 1: One-day autocorrelation of the Dow Jones Industrial Index (Dow). A positive

autocorrelation means that an up-day is followed by an up-day or a down-day is followed by a

down-day. A negative autocorrelation means that an up-day is followed by a down-day, or a

down-day is followed by an up-day. Figure 1 shows the one-year moving autocorrelation average.

The polynomial trendline is of order 5.

From Figure 1 we observe that autocorrelation since the start of World War II in 1939

until the mid-1970’s was mostly positive. However, since the mid 1970’s autocorrelation has

been declining and has mostly been in range with a mean of zero until 2014. An exception was

the global financial crisis, in which numerous stocks in the Dow declined, resulting in a positive

autocorrelation. Altogether Figure 1 verifies that the Dow is trending less in recent times. This

can be interpreted as an increase in the efficiency of the Dow and a demise of technical analysis

trend-following strategies.

A further interesting association is the correlation between international equity markets.

Numerous studies on this topic exist such as Hilliard (1979), Ibbotson (1982), Schollhammer and

Sand (1985), Eun and Shim (1989), Koch (1991), Martens and Poon (2001), Johnson and Soenen

(2009), and Vega and Smolarski (2012). Most studies find a positive correlation between

international equity markets. This is confirmed by Meissner and Villarreal (2003), whose results

are displayed in Table 1:

From table 1 we observe that the US market follows the European market quite closely.

For example, if the European market was up or down more than 2%, the US market had the same

directional change in 76.08% of all cases the following day. The degree of the change was 0.91%

on average.

We also observe from Table 1 that except for one case (the European market following

the US market if the US market has changed by more than 2%), all dependencies are higher than

50%. This confirms the high interdependences between international equity markets.

A word of caution: The Pearson correlation model, which underlies empirical trading

strategies suffers from a variety of limitations. Most critically, the Pearson model only measures

linear associations. As a consequence, the Pearson outcomes can only be meaningfully

interpreted if the joint distribution of the variables is elliptical, which comprises the Normal,

Student-t, Laplace, Cauchy and the Logistic Distribution. In addition, the correlation coefficient is

notoriously volatile, i. e. different time frames can result in very different correlation parameters,

Lagging Market

SuccessChangeSuccessChange Success Change

Volatility dispersion trading

Volatility dispersion tradingSimilar Publications

Electronic copy available at: ssrn/abstract=1156620

Volatility Dispersion Trading

QIAN DENG?

January 2008

This papers studies an options trading strategy known as dispersion strategy

to investigate the apparent risk premium for bearing correlation risk in the op -

tions market. Previous studies have attributed the profits to dispersion trading to

the correlation risk premium embedded in index options. The natural alternative

hypothesis argues that the profitability results from option market inefficiency.

Institutional changes in the options market in late 1999 and 2000 provide a nat -

ural experiment to distinguish between these hypotheses. This provides evidence

supporting the market inefficiency hypothesis and against the risk-based hypoth -

esis since a fundamental market risk premium should not change as the market

structure changes.

?University of Illinois at Urbana-Champaign (email: qiandenguiuc. edu). I thank Tim Johnson,

Neil Pearson, Allen Poteshman, Joshua White and seminar participants at the University of Illinois for

Electronic copy available at: ssrn/abstract=1156620

I. Introduction

There is growing empirical evidence that index options, especially index puts, appear to

be more expensive than their theoretical Black-Scholes prices (Black and Scholes (1973)

and Merton (1973)), while individual stock options do not appear to be too expensive

(see for instance Bakshi and Kapadia (2003), Bakshi, Kapadia, and Madan (2003),

Bollen and Whaley (2004), among others.1). An options trading strategy known as

dispersion trading is designed to capitalize on this overpricing of index options relative

to individual options and has become very popular. Two hypotheses have been put

forward in the literature to explain the source of the profitability of dispersion strategy.

The risk-based hypothesis argues that the index options are more expensive relative

to individual stock options because they bear some risk premium that is absent from

individual stock options. An alternative hypothesis is market inefficiency, which argues

that options market demand and supply drive option premiums to deviate from their

theoretical values. The options market structural changes during late 1999 and 2000

provides a “natural experiment” to distinguish between these two hypotheses. If the

profitability comes from some risk factors priced in index options but not in individual

equity options, then there should be no change in the profitability following the change

in market structure. Our paper investigates the performance of dispersion trading from

1996 to 2005 and finds that the strategy is quite profitable through the year 2000, after

which the profitability disappears. These findings provide evidence in support of the

market inefficiency hypothesis and against the risk-based explanation.

Dispersion trading is a popular options trading strategy that involves selling options

on an index and buying options on individual stocks that comprise the index. As noted

sentially a hedged strategy designed to take advantage of relative value differences in

implied volatilities between an index and a basket of component stocks. It typically in -

volves short option positions on an index, against which long option positions are taken

on a set of components of the index. It is common to see a short position of a straddle or

near-ATM strangle on the index and long positions of straddles or strangles on 30% to

40% of the stocks that make up the index.” The exposure to volatility risk from the long

leg of the strategy on individual stock options tends to be canceled by that of the short

leg in index options. In addition, at-the-money straddle or out-of-the-money strangle

positions have delta exposures very close to zero. Therefore, by construction, a disper -

sion strategy that buys index straddles/strangles and sells straddle/strangle positions

on individual components is hedged against large market movement and has low volatil -

ity risk, which makes it an ideal candidate to bet on the differences between implied

volatilities of index and individual options.

One strand of literature has argued that the differences in the pricing of index and

individual equity options evidence that various risks, such as volatility risks and correla -

tions risks, are priced differently in index options and individual stock options. Bakshi,

Kapadia and Madan (2003) relate the differential pricing of index and individual options

to the difference in the risk-neutral skewness of their underlying distributions. Moreover,

in Bakshi and Kapadia (2003), they show that individual stocks’ risk-neutral distribu -

that correlation risk premium is negative and index options, especially index puts, are

more expensive because they hedge correlation risk.

Another avenue of investigation attributes the puzzle of differential pricing between

index and equity options to the limitations or constraints of the market participants

in trading options. According to Bollen and Whaley (2004), the net buying pressure

present in the index options market drives the index options prices to be higher. Under

ideal dynamic replication, an option’s price and implied volatility should be unaffected no

matter how large the demand is. In reality, due to limits of arbitrage (Shleifer and Vishny

(1997), Liu and Longstaff (2000)), a market maker will not sell an unlimited amount

of certain option contracts at a given option premium. As he builds up his position in

a particular option, his hedging costs and volatility-risk exposure also increase, and he

is forced to charge a higher price. Bollen and Whaley show that changes in the level

of an option’s implied volatility are positively related to variation in demand for the

option, and then argue that demand for out-of-the-money puts to hedge against stock

market declines pushes up implied volatilities on low strike options in the stock index

options market. G? arleanu, Pedersen and Poteshman (2006) complement Bollen and

Whaley ’s hypothesis by modelling option equilibrium prices as a function of demand

pressure. Their model shows that demand pressure in a particular option raises its

prices as well as the prices of other options on the same underlying. Empirically, it is

documented that the demand pattern for single-stock options is very different from that

settlement, and a marked reduction in bid-offer spreads, provide a natural experiment

that allows one to distinguish between these hypotheses. Specifically, these changes in

the market environment reduced the costs of arbitraging any differential pricing of indi -

vidual equity and index options via dispersion trading. If the profitability of dispersion

trading is due to miss-pricing of index options relative to individual equity options, one

would expect the profitability of dispersion trading to be much reduced after 2000. In

contrast, if the profitability of dispersion trading is compensation for a fundamental risk

factor, the change in the option market structure should not affect the profitability of

this strategy.

In this paper, we investigate the performance of dispersion trading from 1996 to

2005 and examine whether the profits to dispersion strategy decreased after 2000. We

find that dispersion trading is quite profitable through the year 2000, after which the

profitability disappears.

We initially examine the risk/return profile of a simple dispersion trading strategy

that writes the at-the-money (ATM) straddles of SP 500 and buys the ATM straddles

of SP 500 components. We find the average monthly return decreases from 24% over

1996 to 2000 to ?0.03% over 2001 to 2005. Moreover, the Sharpe ratio also decreased

from 1.2 to ?0.17, and Jensen’s alpha decreases from 0.29 to ?0.04. A test of structural

change supports the changing profitability hypothesis as well. These results suggests that

the differential pricing of index versus individual stock option must have been caused at

Dispersion trading

Dispersion tradingSimilar Publications

Electronic copy available at: ssrn/abstract=1889147

Dispersion Trading

A thesis submitted in partial fulfillment

of the requirements for the degree of

Diplom-Finanz? okonom math.

(Master of Mathematical Finance)

1 Introduction

In the early 1990s some sophisticated equity derivatives traders started to make

profits from price differences in volatility markets by selling options on an index and

simultaneously buying options on the constituent stocks. Thereby, they took advan -

tage of the differences between index volatility and average component’s volatility.

Such a strategy bets on the degree to which constituent stocks disperse, leading

to the name dispersion trading. Since such strategies were difficult to hedge on one

hand and costly to implement on the other, only few traders performed these trades.

Due to increased liquidity in option markets and the development of volatil -

ity derivatives, the access to dispersion trading is more commonly available today.

Hedge funds and investment bank’s proprietary trading desks are nowadays the most

active participants in this specialized market. To satisfy the institutional and private

investors’ demand for alternative investments, asset management companies have

also set up related quantitative products. This gives investors access to a further

dimension of investment strategies hopefully increasing the benefits of diversifica -

tion. Among these products are funds employing strategies denoted market-neutral

or relative-value since they do not replicate a certain equity market but are aimed

at taking advantage of its inefficiencies. Dispersion trading, formerly also known as

index-option arbitrage, is one such strategy and thereby received more and more

attention.

What is dispersion trading exactly? The term dispersion trading comprises a

multitude of different trading strategies and there is no clear-cut definition of a

dispersion trade since the way of implementing such a strategy has evolved over

time and the products utilized have changed. However, the strategies have in com -

mon that they trade index volatility against the volatility of the index constituents

with the objective of exploiting price differences in index and single-stock volatility

markets. Historically, a long dispersion position, i. e. selling quite expensive index

options and buying a basket of individual options (the offsetting dispersion basket),

has been highly profitable. With volatility becoming a common investment vehicle,

various products for trading index and single-stock volatilities are established today.

As for dispersion trading variance swaps are the most common and gamma swaps

1 INTRODUCTION

The main goal of this thesis is to rationalize why dispersion trading is a worth -

while strategy. Therefore, definitions of volatility and correlation are presented and

their modeling and predictability are discussed extensively. In particular, we rig -

orously investigate different measures of average correlation of an index. Thereby

we set the foundations for academically discussing dispersion trading. Using the

concept of average correlation we rationalize that the potential profit obtained in

a dispersion trade can be attributed to particular properties of the index volatility

skew and to a negative premium for correlation risk. We confirm these results and

recent empirical findings by examining the average correlation of the Dow Jones

Euro Stoxx 50.

Another major goal is the comparison and evaluation of different dispersion trad -

ing strategies. We characterize various ways to set up dispersion trades and discuss

their properties and their practicability. To evaluate the theoretical findings, we test

several strategies empirically and evaluate their performance in stress scenarios. Fi -

nally, the use of dispersion trading in a portfolio context is discussed.

Dispersion Trading

The high difference between implied volatility of index options and subsequent realized volatility is a known fact. Trades routinely exploit this difference by selling options with consecutive delta hedging. There is however more elegant way to exploit this risk premium - the dispersion trading. The dispersion trading uses known fact that difference between implied and realized volatility is greater between index options than between individual stock option. Investor therefore could sell options on index and buy individual stocks options. Dispersion trading is a sort of correlation trading as trades are usually profitable in a time when the individual stocks are not strongly correlated and losses money during stress periods when correlation rises. Basic trade could be enhanced by buying options of firms with high belief disagreement (high analysts disagreement about firms earnings).

volatility effect, volatility premium

Simple trading strategy

Other Papers

Driessen, Meanhout, Vilkov: Option-Implied Correlations and the Price of Correlation Risk

Motivated by extensive evidence that stock-return correlations are stochastic, we analyze whether the risk of correlation changes (affecting diversification benefits) may be priced. We propose a direct and intuitive test by comparing option-implied correlations between stock returns (obtained by combining index option prices with prices of options on all index components) with realized correlations. Our parsimonious model shows that the substantial gap between average implied (39.5% for S&P500 and 46.0% for DJ30) and realized correlations (32.5% and 35.5%, respectively) is direct evidence of a large negative correlation risk premium. Empirical implementation of our model also indicates that the index variance risk premium can be attributed to the high price of correlation risk. Finally, we provide evidence that option-implied correlations have remarkable predictive power for future stock market returns, which also stays significant after controlling for a number of fundamental market return predictors.

Lisauskas: Dispersion Trading in German Option Market

There has been an increasing variety of volatility related trading strategies developed since the publication of Black-Scholes-Merton study. In this paper we study one of dispersion trading strategies, which attempts to profit from mispricing of the implied volatility of the index compared to implied volatilities of its individual constituents. Although the primary goal of this study is to find whether there were any profitable trading opportunities from November 3, 2008 through May 10, 2010 in the German option market, it is also interesting to check whether broadly documented stylized fact that implied volatility of the index on average tends to be larger than theoretical volatility of the index calculated using implied volatilities of its components (Driessen, Maenhout and Vilkov (2006) and others) still holds in times of extreme volatility and correlation that we could observe in the study period. Also we touch the issue of what is (or was) causing this discrepancy.

Carrasco: Studying the properties of the correlation trades

This thesis tries to explore the profitability of the dispersion trading strategies. We begin examining the different methods proposed to price variance swaps. We have developed a model that explains why the dispersion trading arises and what the main drivers are. After a description of our model, we implement a dispersion trading in the EuroStoxx 50. We analyze the profile of a systematic short strategy of a variance swap on this index while being long the constituents. We show that there is sense in selling correlation on short-term. We also discuss the timing of the strategy and future developments and improvements.

Choi: Analysis and Development Of Correlation Arbitrage Strategies on Equities

After the two years of studies in the area of mathematical finance at Univ ersity of Paris 1, I had a chance to work with an asset management team as a quantitative analyst at Lyxor Asset Management, Societe Generale in Paris, France. My first task was to develop an analysis of the performances of the funds on hidden assets where the team's main focus was on, such as Volatility Swap, Variance Swap, Correlation Swap, Covariance Swap, Absolute Dispersion, Call on Absolute Dispersion (Palladium). The purpose was to anticipate the profit and to know when and how to reallocate assets according to the market conditions. In particular, I have automated the analysis through VBA in Excel. Secondly, I had a research project on Correlation trades especially involving Correlation Swaps and Dispersion Trades. This report is to summarize the research I have conducted in this subject. Lyxor has been benefiting from taking short positions on Dispersion Trades through variance swaps, thanks to the fact that empirically the index variance trades rich with respect to the variance of the components. However, a short position on a dispersion trade being equivalent to taking a long position in correlation, in case of a market crash (or a volatility spike), we can have a loss in the position. Thus, the goal of the research was to find an effective hedging strategy that can protect the fund under unfavorable market conditions. The main idea was to apply the fact that dispersion trades and correlation swaps are both ways to have exposure on correlation, but with different risk factors. While correlation swap has a pure exposure to correlation, dispersion trade has exposure to the realised volatilities as well as the correlation of the components. Thus, having risk to another factor, the implied correlation of a dispersion trade is above (empirically, 10 points) the strike of the equivalent correlation swap. Thus, taking these two products and taking opposite positions in the two, we try to achieve a hedging effect. Furthermore, I look for the optimal weight of the two products in the strategy which gives us the return of the P&L, volatility of the P&L, and risk-return ratio of our preference. Moreover, I tested how this strategy would have performed in past market conditions (back-test) and under extremely bearish market conditions (stress-test).

Faria, Kosowski: The Correlation Risk Premium: Term Structure and Hedging

As the recent financial crisis has shown, diversification benefits can suddenly evaporate when correlations unexpectedly increase. We analyse alternative measures of correlation risk and their term structure, based on S&P500 correlation swap quotes, synthetic correlation swap rates estimated from option prices and the CBOE Implied Correlation Indices. An analysis of unconditional and conditional correlation hedging strategies shows that only some conditional correlation hedging strategies add value. Among the conditional hedge strategy’s conditioning variables we find that the level of the correlation risk factor and dispersion trade returns deliver the best results, while the CBOE Implied Correlation Indices perform poorly.

Maze: Dispersion Trading in South Africa: An Analysis of Profitability and a Strategy Comparison

A dispersion trade is entered into when a trader believes that the constituents of an index will be more volatile than the index itself. The South African derivatives market is fairly advanced, however it still experiences inefficiencies and dispersion trades have been known to perform well in inefficient markets. This paper tests the South African market for dispersion opportunities and explores various methods of executing these trades. The South African market shows positive results for dispersion trading; namely short-term reverse dispersion trading. Call options and Cross-Sectional Volatility (CSV) swaps are also tested. CSV swaps performed poorly whereas call options experienced annual returns well above the market.

Deng: Volatility Dispersion Trading

Dispersion trading

Dispersion trading is a strategy involving the selling of options on an index against buying a basket of options on individual stocks. Such a strategy is a play on the behaviour of correlations during normal markets and during large market moves. If the individual assets returns are widely dispersed then there may be little movement in the index. but a large movement in the individual assets. This would result in a large payoff on the individual asset options but little to pay back on the short [[index] option.

The volatility on an index. (sigma_I), can be approximated by (sigma_I^2=sqrt ^N sum_ ^N w_iw_jho_ sigma_isigma_j >), where there are (N) constituent stocks, with volatilities (sigma_i), weight (w_i) by value and correlations (ho_ ).

If you know the implied volatilities for the individual stocks and for the index option then you can back out an implied correlation. amounting to an average across all stocks[frac ^N w_i^2 sigma_i^2> ^N sum_ ^N w_iw_jho_ sigma_isigma_j>].

Dispersion trading can be interpreted as a view on this implied correlation versus one's own forecast of where this correlation ought to be, perhaps based on historical analysis.

Correlation trading strategies best binary option brokers

Correlation trading strategies best binary option brokersCorrelation trading strategies Best Binary Option Brokers dentistelasertek

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Correlation trading strategies best binary option brokers

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Correlation trading strategies binary betting revealed

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Currency pair correlations

Currency pair correlationsCurrency Pair Correlations

It is useful to know that some currencies tend to move in the same direction while others move in the opposite direction. For those who want to trade more than one currency pair. this knowledge can be used to test strategies on correlated pairs, to avoid overexposure, to double profitable positions, to diversify risks, and to hedge.

In the financial world, correlation is the statistical measure of the relationship between two securities or assets. The correlation coefficient ranges from -1 to +1, sometimes expressed from -100 to 100.

A correlation of +1 or 100 means two currency pairs will move in the same direction 100% of the time.

A correlation of -1 or -100 means two currency pairs will move in the opposite direction 100% of the time.

A correlation of 0 means no relationship between currency pairs exists.

In between -100 and 100 is different degrees of correlated relationship:

if the correlation is high (above 70) and positive then the currencies move in tandem.

if the correlation is high (above 70) and negative then the currencies move in opposite directions.

if the correlation is low (below 60) then the currencies don't move the same way.

Where can one find information about current currency correlations?

There are a few websites out there that track the currency correlations between different pairs on different time frames (and periods) and present them in an easy to read table.

Forex correlation pdf strategy

Forex correlation pdf strategyforex correlation pdf strategy

Date: March 15, 2013

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Forex correlation heatmap and correlation table

Forex correlation heatmap and correlation tableForex Correlation Heatmap and Correlation Table

What is correlation? Wikipedia Definition:

In probability theory and statistics. correlation . (often measured as a correlation coefficient ), indicates the strength and direction of a linear relationship between two random variables. In general statistical usage, correlation or co-relation refers to the departure of two variables from independence.

Some currency pairs tend to move together in the same direction. Other currency pairs tend to move in opposite directions. Understanding how currency pairs tend to move relative to one another can be used in a number of different ways. It can be used to analyze how diversified your Forex portfolio is and, indirectly, your risk profile. It can also be used to understand how to enter into hedging trades.

Currency correlations measure how closely currency pair prices have (statistically) moved together in the past.

Example of how some currencies are correlated with each other. Positively or Negatively

The darker the color the higher the correlation. In the case of the heatmap above you can plainly see the high correlation between EUR/USD which is the pair we are comparing to XAG/USD. Silver is a commodity that losses value when the USD gains vs the EUR. Given that high correlation it would be highly likely that as the USD apreciates vs. the EUR, it would also appreciate vs Silver. The opposite would also be true.

There is a table view in order to compare the numerical correlation values of the various pairs. A perfect example of direct and inverse correlation is in the following two columns.

A correlation coefficient, a number between -1 and +1, is used to express how closely correlated two pairs are.

EUR/USD is inversely correlated with USD/JPY. In the past week the pairs were inversly correlated at -0.8. A gain by the EUR vs. the USD was correlated to a loss of the USD vs. the JPY. In the next column is the example of the opposite type of correlation. In the past week when EUR gained on the USD there was a correlation of 0.90 between Silver advancing on the USD.

Finally, if two pairs have a correlation coefficient close to 0 then the two pairs tend to move independently of one another.

It is important to note that currency correlations can change over time because of changes in monetary policies or shifts in the eco-political landscape. For example, a new extensive free trade agreement between two countries may mean that their currencies will correlate more strongly in the future. Traders will want to analyze changes in correlations since such changes affect their risk profiles.

The live version of the OANDA FXCorrelation tool is hosted at FXInfoCenter

This article is for general information purposes only. It is not investment advice or a solution to buy or sell securities. Opinions are the authors; not necessarily that of OANDA Corporation or any of its affiliates, subsidiaries, officers or directors. Leveraged trading is high risk and not suitable for all. You could lose all of your deposited funds.

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Using currency correlations to your advantage

Using currency correlations to your advantageUsing Currency Correlations To Your Advantage

To be an effective trader, understanding your entire portfolio's sensitivity to market volatility is important. This is particularly so when trading forex. Because currencies are priced in pairs, no single pair trades completely independent of the others. Once you are aware of these correlations and how they change, you can use them control your overall portfolio's exposure. (For a guide to all things forex, check out our Investopedia Special Feature: Forex .)

Defining Correlation

The upper table above shows that over the month of February (one month) EUR/USD and GBP/USD had a very strong positive correlation of 0.95. This implies that when the EUR/USD rallies, the GBP/USD has also rallied 95% of the time. Over the past 6 months though, the correlation was weaker (0.66) but in the long run (1 year) the two currency pairs still have a strong correlation.

By contrast, the EUR/USD and USD/CHF had a near-perfect negative correlation of -1.00. This implies that 100% of the time, when the EUR/USD rallied, USD/CHF sold off. This relationship even holds true over longer periods as the correlation figures remain relatively stable.

Yet correlations do not always remain stable. Take USD/CAD and USD/CHF, for example. With a coefficient of 0.95, they had a strong positive correlation over the past year, but the relationship deteriorated significantly in February 2010 for a number of reasons, including the rally in oil prices and the hawkishness of the Bank of Canada. (For more, see Using Interest Rate Parity To Trade Forex .)

About currency correlation

About currency correlationAbout Currency Correlation

Some currency pairs are correlated and some are not. Lets try to see what currency correlation is. But before talking about Forex correlation, lets start from the macro level.

Every currency has its own characteristics:

There are safe heaven currencies which attract investments when there are major economic turmoil and risks to global economy is perceived to be high. These currencies are considered to be safe investments when the risk appetite is low.

There are commodity currencies from countries which are major commodity exporters and the health of those economies and hence the health of their currencies depend on the growth of commodity exports.

There are low yield currencies with very low inflation rate and hence very low interest rates and then there are high yield currencies.

There are economies and currencies from nations which are large and net exporters and then there are those who are large and net importers.

The points mentioned above are just on macro level but what these reflect that many of the economies across the globe are correlated. It may be a strong positive correlation or a strong negative correlation or it may be weak ones but if some economies have correlations then it is natural that their currencies would also have correlation. The strength of a currency is nothing but the strength of that economy. So currency correlation basically represents the correlation of those economies.

So when we say that every currency has its own characteristics, we should also say that many of those characteristics are shared with other currencies and are common with some other currencies. This is like families of currencies. The currencies of one family or with similar characteristics would behave similar to each other during various kinds of economic scenarios or market sentiments. For example when there is a perceived risk for global economy, the currencies when may be considered as safe, would become stronger or when there is more risk appetite because of higher confidence in the health of global economy. the currencies with high yield but more risk factor may shine better.

To summarize it, we can say that we do not have to analyze each currency individually but on a macro level we can divided currencies in different groups or families and the initial analysis can be for the individual groups or families and not just the individual currencies. The currencies of on group may tend to behave similarly and move in the same direction normally.

Now to go one step more into detail about currency correlation, lets see what kinds of relationships are possible between currencies:

1) Generally behaving in the similar way i. e. positive correlation.

2) Generally behaving in the opposite way i. e. negatively correlated currency pairs.

3) Not caring about each other or random relationship i. e. no correlation.

The above is the essence of currency correlation. To summarize again and in other words; some currency pairs tend to move in the same direction most of the time, some currency pairs tend to move in the opposite direction most of the time and some currency pairs do not show any relationship and their moves are completely random in relation to each other. These are positive correlation, negative correlation and no correlation respectively. Lets see some of the practical and live examples of currency correlations as follows and we will be talking about the details of Forex correlation on other sections of this site.

Why Currency (Forex) Correlations are Important

Forex correlation need to be understood on macro level and need to be kept in mind, especially when we trade with multiple currency pairs. There is no need to check it on daily basis if the trades are not based on some kind of correlation system. In fact we would always recommend to avoid trading any suggested correlation system. It would simply be too complicated and not worth your time and risks.

We need to be simply aware of the correlation in different Forex pairs so that we avoid cancelling any profits by taking any opposite positions for any Forex pairs which have strong positive correlation or taking the similar positions for two Forex pairs which have strong negative correlation.

Currency pair eur

Currency pair eurCurrency Pair: EUR/USD (Euro/U. S. Dollar)

DEFINITION of 'Currency Pair: EUR/USD (Euro/U. S. Dollar) '

The abbreviation for the euro and U. S. dollar (EUR/USD) pair or cross for the currencies of the European Union (EU) and the United States (USD). The currency pair tells the reader how many U. S. dollars (the quote currency ) are needed to purchase one euro (the base currency).

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BREAKING DOWN 'Currency Pair: EUR/USD (Euro/U. S. Dollar) '

The value of the EUR/USD pair is quoted as 1 euro per x U. S. dollars. For example, if the pair is trading at 1.50 it means that it takes 1.5 U. S. dollars to buy 1 euro.

The EUR/USD tends to have a negative correlation with the USD/CHF and a positive correlation to the GBP/USD currency pairs. This is due to the positive correlation of the euro, the Swiss franc and the British pound.

Live forex currency correlation tables

Live forex currency correlation tablesShare with other Traders

2 Responses to Live Forex Currency Correlation tables

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By tomas January 9, 2013 - 11:29 am

How to use this indicator to set up entry point: currency correlation indicator, relative currency strengh and currency volatility indicator?

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