Update ... June 2012.

Over the last couple of years I have had several people contact me about this research. I have also had a lot of spam. If you are reading and would like to discuss further please do contact me. Also, if I don't respond immediately, please give me the benefit of doubt that your email got caught by a spam filter and try again.

I can be reached at john DOT parker AT relevanteconomics DOT com.

Presentation, the decline of the blog (and the end of this one)

As promised, the research paper and presentation that summarize my research are posted here.

An appropriate article, reproduced below, from the newspaper this morning charts the rise and fall of blogs. I started this one just before the peak last year and today I’m adding it to the estimated 200 million that are in the discarded pile.

An estimated 200 million blogs have been started and then abandoned, writes Tony Allen-Mills in The Sunday Times of London. Research by the Gartner Group, a U.S. technology firm, finds that the blogging phenomenon may have peaked last October, when 100,000 new blogs were being created every day. As well as personal diaries these included corporate, professional, celebrity and other specialist blogs. Gartner concludes that the trend will level off this year, with perhaps 100 million people still blogging worldwide. Other analysts predict that number will fall to 30 million. "A lot of people have been in and out of [blogging]," said Gartner's Daryl Plummer. "Everyone thinks they have something to say until they're put on stage and asked to say it."

Source: Social studies - A daily miscellany of information by Michael Kesterton from The Globe and Mail Thursday, April 05, 2007

I started this blog with two goals in mind. First it was a place to organize my research results in small chunks as I worked through the hypotheses I wanted to test. Second, I hoped to start a dialogue with others interested in the area.

The first goal was met and a research paper and presentation are posted above as the organized version of the results published here. The second goal was not met. While hundreds of people visited, the dialogue did not really get going.

So, until I get a sponsor to undertake more research, here it is, blog number 200,000,001.

The picture? Taken 90 years ago almost to the day (April 1917) by my grandfather, a steamship engineer in the RNR, in northern Russia. The caption reads "Arctic Price after striking a mine which killed 4 men on April 14th 1917". More of my granddad's pictures can be found here.

Almost the end - research paper

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

My research on the impact of news on financial markets is coming to an end.

After a presentation this week I am not intending to do any new research in this area.

My interest does continue however. I would like to talk to others interested in this topic and encourage any who stumble across this site to contact me.

As my work winds down I have decided to provide a link here to a draft research paper. While I provide this research free, please do give me credit for it if you use it.

If you would like me to expand this research in any way please contact me.

For more on my work see this post, or this list. Some of my energy economics related work is listed here. For more about me see my profile or this.

Correlation of Currencies - Normal and Stressed

Correlations can be useful tools.

FX Traders often use them to confirm movements following an announcement. For example, if a trader is expecting the EUR to appreciate following a retail sales announcement and the EUR has a negative correlation with the CHF and a positive relationship with the GBP then these currencies might be tracked to make sure the movement in the EUR is one to take advantage of.

Correlations are useful but can change in stressful times. In times of extreme financial stress correlations head toward 1 and -1. This is the contagion effect, when safe harbours disappear.

When economic news affects financial markets correlations strengthen as volatilities rise. It is important then that market participants use the right correlation for the right situation.

A while ago I posted some correlations that looked at FX rates a few minutes after major announcements. Here is the link to these posts.

Tables at mataf.net (Currencies Price Provided by the Swiss broker RealtimeForex) give correlation of currencies in more normal times. According to the website:

• If the correlation is high (above 0.8) and positive then the currencies move in the same way.
• If the correlation is high (above 0.8) and negative then the currencies move in the opposite way.
• If the correlation is low (below 0.6) then the currencies don't move in the same way.

These correlations for 5, 20 and 100 day periods will tend to average out the extremes that are experienced during announcement days and so will tend to be lower.

So correlations tend to be higher at times of stress and following economic announcements. Here is the proof.

I have taken the average correlations (currency pairs of currencies shown below with the USD vs. EUR/USD) following four major U.S. economic announcements: CPI, initial jobless claims, nonfarm payrolls, and retail sales. The correlations are plotted for data 1, 5, 10, 20, and 30 minutes following these announcements. Using the mataf.net data I also plot the 5, 20, and 100 day correlations alongside:The correlations on the right hand side of the chart are for more "normal" times.

The correlations on the left hand side of the chart are for more "stressful" times.

Notice how currencies that tend not to move together at the daily frequency do move together after annoucements. Also there is a trend towards greater positive or negative correlation the closer one gets to the announcement.

If anyone would like a spreadsheet of my calculation of the 1, 5, 10, 20 and 30 minute return correlation matrices for the U.S. announcements of nonfarm payrolls, initial jobless claims, retail sales, and CPI, please send me an email to john.parker at relevanteconomics.com.

Thursday, April 5, 2007

TABE Luncheon, Thursday, April 5, 2007

The Impact of Economic News on Financial Markets

John Parker, Principal, Relevant Economics

It is not news that news moves financial markets. In the past, research into the impact of economic announcements on financial markets has used surveys of economists to gauge market sentiment. The extent to which the actual number differs from the survey is taken as the news component that drives markets following the announcement. Recently two developments have occurred that have allowed us to quantify how much, when, and in what direction financial markets move in response to news. Firstly, economic derivatives, auctions of derivatives on economic releases allow us to get a much better read on market sentiment than comes from surveys. Secondly real-time financial markets data has allowed the effect of the announcement to be isolated and separated from other influences.

John Parker will present his research on the impact of economic news on financial markets using economic derivatives and real-time financial data. He will answer questions such as: which economic announcements move markets most; which markets respond most quickly; do good news and bad news have asymmetric effects; and, do data revisions matter?

John has an M.Phil. from Oxford, a B.A. from UWO and 25 years experience as an economist, in the fields of energy markets, financial markets, and business forecasting. His experience in financial markets includes working at the central bank and a risk management software company.

Location: BMO Boardroom, 21^st Floor, 1 First Canadian Place, Toronto

Date: Thursday, April 5th, 2007

Time: 12 noon to 2 p.m.

Cost: TABE and CABE members in good standing $25, Others $45. A light lunch will be available.

PLEASE USE THE WEB SITE FOR REGISTRATIONS www.cabe.ca/chapters/TABE . Only if absolutely necessary, to register, you can call 905-845-3102 or E-Mail tabe@cabe.ca. Registration closes 2 p.m. Tuesday, April 3rd.

Members (only) may pay cash or cheque at the door, non-members and credit card payments (VISA, Diners/EnRoute, MasterCard, or American Express) are to be prepaid VIA THE WEB SITE. No-shows will be invoiced. Charges include GST. (TABE GST number: R124389990)

Meeting arranged by Paul Ferley

Charts for Survey vs. Derivatives Based Forecast Comparisons

In the last few posts I showed the statistics that show that the economic derivatives forecast outperforms the consensus for the nonfarm payrolls announcement. Here are the charts that go with the stats. First, here is the chart of Actual (U.S. nonfarm payrolls or NFP) versus the economic derivatives or market-based forecast for the last 54 months (2002:01 - 2007:03). Here is the same chart for the Consensus or survey-based forecast: And side-by-side:And here are all three as a time series plot: Here are the forecast errors for the economic derivative or auction market forecasts compared with a fitted normal distribution (you'd hope that the errors were random and reasonably normal in their distribution): And here are the Consensus or survey-based forecast errors: Conclusion? You can't see much from the charts as the two forecast series both track the actual quite well and eye-balling the charts does not suggest one is better than another. However, the statistics of MAE, RMSE, correlation and especially the horse-race regression confirm that the economic derivative or auction market-based forecast outperforms the survey or Consensus forecast and that the latter adds nothing once you have the former.

NFP: Superior Derivatives-Based Forecasts - Confirmed

Refet S. Gürkaynak and Justin Wolfers compared the Consensus of Economists or survey-based forecasts with the economic derivatives or market-based forecast using data from Oct. 2002 to Jul. 2005 (33 NFP observations). The results shown are shown below (in the GW columns).

I have replicated their study using more, and overlapping, data from Oct. 2002 to Mar. 2007 (64 observations). My results are shown in the table by the JCP columns.

The conclusion? Again the economic derivatives or auction market-based forecast dominates the Economist survey or Consensus forecast.

Details are found in the table below which looks at measures of forecast accuracy, the mean absolute error (MAE) and the root mean squared error (RMSE). There is also a correlation of each forecast with the actual (NFP release) and a regression-based test of the information content of each forecast using the Fair and Shiller method.

As with the smaller sample in GW, the MAE and RMSE are lower for the economic derivatives forecast. The correlation with the actuals is also higher than the Consensus-based forecast.

The coefficient in the regression should be unity for a good forecast. For the Derivatives or auction market-based forecast the test of the coefficient being equal 1 could not be rejected by GW. The evidence is not as strong now, as the test statistic is: F(1, 51) = 0.235477, with p-value = 0.62957.

The test that the Consensus or survey-based forecast is zero (that is that this forecast adds nothing to explanatory power of the other forecast, or conditioning on the market-based forecast renders the survey forecast uninformative) is: Test statistic: F(1, 51) = 5.62732, with p-value = 0.0214933. So the Consensus adds no information beyond the economic derivative forecast.

Not only that but the perverse negative coefficient found by GW persists with the longer data set.

Again, it seems “likely that the improved performance is due to the market effectively weighting a greater number of opinions, or more effective information aggregation as market participants are likely more careful when putting their money where their mouth is.”

	JCP	JCP	GW	GW
	Consensus	Economic Derivatives	Consensus	Economic Derivatives
Mean Absolute Error (MAE)	0.812	0.809	0.743	0.723
Root Mean Squared Error (RMSE)	1.036	1.023	0.929	0.907
Correlation of Forecast with Actual	0.7025	0.7026	0.677	0.700
Horse Race Regression (Fair-Shiller)	-0.42	1.26	-0.14	1.06
standard error	0.56	0.53	0.89	0.78
t-statistics	-0.75	2.38	-0.16	1.36
significant at 10% (*), 5% (**), or 1% (***) level		**
R2		0.50		0.46
obs.		54		33
range of data	Oct. 2002 - Mar. 2007		Oct. 2002 - Jul. 2005

Forecast errors normalized by historical (Oct. 2002 to Mar. 2007) standard deviation of survey-based forecasts of 90.31.

Fair-Shiller - Fair, Ray C. and Robert J. Shiller (1990), “Comparing Information in Forecasts from Econometric Models,” American Economic Review, 80(3), 375-89.

GW - Refet S. Gürkaynak and Justin Wolfers (2005), "Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty, and Risk"

A Consensus of Economists

How well do economists predict the U.S. nonfarm payrolls? I picked up the consensus forecast for the NFP from the NASDAQ website (from Econoday). The latest report is here.

There are 63 monthly reports back to January 2002 (there may be more but this is all I pulled for analysis), all have actual and the consensus forecast. The most recent 4o reports have the range of the forecasts as well.

The average error was 24,000.

This appears to be a biased forecast (on average the error should be zero. With a standard error or 10,000, this is significantly different from zero at the 95% level). So despite studying the dismal science, economists are overall an optimistic bunch. In fact they are overly optimistic.

The biggest oopsie was 328,000 (in March 2003 when the actual was -308,000 and the expected was 20,000).

In the last 40 months the actual fell within the range of the expectations 20 times. So 50% of the time the actual was inside the range. That is not a great record.

Next, I'll look at the economic derivatives forecast performance and provide some comparisons.

Nonfarm payroll employment - whose better?

Nonfarm payrolls for February 2007 (released today, Friday March 9th at 8:30am) continued to trend up (+97,000). Details are here. Yesterday I noted that as of yesterday, the Economists' consensus was 100,000 and the CME Auction Market participants' consensus was 82,500. So who is the better forecaster? In this instance the Economists were better (error of 3,000 versus the derivatives auction results error of 14,500). For the Nonfarm payrolls (NFP) there are several forecasts that come from the CME auctions There was one more auction and therefore one more forecast before the release at 8:30am this morning. This auction gave an implied market forecast that was even more off the mark (75,500). I have maintained that, based on my research, over time the CME Auction Market participants' consensus outperforms the Economists' consensus. Gürkaynak and Wolfers (2006) conclude that “The evidence presented … shows that economic derivatives option prices are accurate and efficient predictors of the densities of underlying events” (p. 29). Seems it is time to test that hypothesis again. Here's what I'd like to do. Test:

• Which forecast best predicts the actual outcome, CME economic derivatives auctions or the Economists' consensus?
• Since, for the NFP there are several forecasts from economic derivatives auctions, do auctions closer to the release perform better than forecasts that are more stale?
• Does averaging the auction results produce a superior forecast?

There are a couple of definitions of a better forecast that may be pertinent here. One might be, as implied above, that is which forecast predicts the outcome better. However this assumes that the variable you are interested in is in fact the economic release (such as the NFP). But who really cares about the NFP? What most people care about is what it means to them. These economic statistics are indicators. Most investors care about how these indicators affect their portfolio of holdings. So one definition of a better forecast of NFP might be one that more accurately explains (or forecasts) movements in the financial variable of interest. I'm not sure the data will allow for a definitive test on all of these points, but this is my goal.

1. Gürkaynak, Refet S., Wolfers, Justin, (2006) “Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty and Risk” NBER Working Paper Series, NBER Working Paper 11929, January 2006.

What Can You Expect from the March Nonfarm Payroll Release?

As I have discussed before in this blog, the best information about an upcoming nonfarm payrolls release comes from the economic derivatives auctions held by the Chicago Mercantile Exchange, Longitude, and ICAP. From the CME: March Nonfarm Payroll Release - Friday, March 9 at 8:30 a.m. EST

Economists' consensus - 100,000

CME Auction Market participants' consensus - 82,500

Based on previous auctions that occurred Tuesday, March 6, Wednesday, March 7 and earlier this morning, CME Auction Market participants predict U.S. employers added 82,500 jobs last month, which is significantly lower than economists' forecasts of 100,000 jobs.

Chart from this morning's CME Auction (March 8, 7:30 a.m. - 8:15 a.m. EST)

Chart from yesterday's CME Auction (March 7, 7:30 a.m. - 8:15 a.m. EST)

Chart from Tuesday's CME Auction (March 6, 7:30 a.m. - 8:15 a.m. EST)

It's All About Me

Time for some self-promotion.

I have written a fair bit about announcements, market expectations, and using scenarios for risk management (as well as ranking mutual funds). Here are some currently active links to my work.

• Credit Downgrade/Fat Tails/Mixture of Normals - It is February 17, 2000, and Moody’s Investors Services has placed on review, for possible downgrade, the Aa1-rated yen-dominated domestic securities of the Japanese government. Moody’s called the review because Japan’s Ministry of Finance forecast its national debt to exceed 129% of its GDP at the end of fiscal year 2000. Moody’s last downgraded Japan’s debt because the ratio had reached 100%. Japan’s current debt-to-GDP ratio is the highest among all industrialized nations. Apart from extraordinary events such as wars, this level of debt is unusual. A story about how risk managers at a fictitious US bank with a large position in Japanese government bonds deal with the implications of the downgrade for their portfolio.

• Interest Rate Forecasts as Scenarios - A risk manager at a fictitious US bank is asked by a board member who had seen a survey of forecasts for interest rates at the end of the year asked, “What might the banks’ losses be if these forecasts represented the mainstream opinion, but everyone was wrong?” This story investigates ways in which forecasts can be used to create scenarios for risk analysis.

• Economic Announcements and Risk Measures - This story investigates whether knowledge of an impending policy announcement by the Federal Reserve Board (Fed) is useful in estimating market volatility, and therefore risk. Risk measures based on a variance-covariance matrix derived from recent historical data assume that history is stable and will repeat itself. Information about an impending change in the target federal funds rate can be incorporated into a risk management framework through scenarios. Scenario-based risk measures capture the announcement effect, the higher volatility observed following a change in the federal funds rate.

• Earnings Announcements, Volatility Spikes and Risk Measures - It’s October 12, 2000 and there is a strong possibility that Microsoft’s release of its quarterly earnings on October 18, 2000 might miss Wall Street’s earnings expectations of $0.413. A broker whose largest portfolio consists of Microsoft stock, wants to ensure that its equity trading desk is accurately measuring risk. Its risk managers are aware that markets seem to show irregularities during a two-day period each quarter. If this phenomenon coincides with a downturn in Microsoft’s earnings, volatility might spike. Future volatility estimates based on this transient spike will be biased. This story examines the question: Is there a way to account for these shocks so they do not corrupt forecasts of risk measures for months to come?

• Scenario Banks and The Implications of Bank Takeovers on their Stock Price - On January 26, 2001, The Royal Bank of Canada (RBC) announces a takeover of Centura Banks Inc. in the United States. How can history be used to create scenarios to model RBC’s share price following the announcement of the takeover that will help an investor understand the implications of this news?

• How Bad is Bad News; How Good is Good News? - The stock market is driven by news. Good news lifts the market. Bad news dampens growth. Good news does not lift the market as much as bad news depresses it. Also, bad news during a bear market has a bigger negative impact than bad news during a bull market. To illustrate these two asymmetries in stock market, GARCH volatility models are estimated. Because volatility is unobserved, models for volatility are particularly difficult to validate. Our models are re-cast in terms of how they react to news. By applying news scenarios, the adequacy of the models can be assessed.

• Mutual Fund Ranking System – Developed for www.globefund.com, Globefund 5-Star Ratings service, a simple rating for most mutual funds in Canada. Globefund 5-Star Ratings help investors understand how well each fund has been doing relative to similar funds. Funds are ranked from one to five stars, with the top ranked funds getting five stars and the lowest ranked funds getting one star. While past performance does not guarantee future performance, our historical testing of this rating system has shown that on average, top-rated funds have tended to outperform their peers over a six-month to two-year horizon. Press release link.

• Stress test proves blue chips tops for returns, safety – (The Globe and Mail Print Edition 20/01/01 Page N2, Rob Carrick) To gain some insight into how the a couple of stock-picking strategies hold up in market declines, I looked at what would happen to both the TSE 300 and a couple of portfolios if the average price-earnings ratio for TSE stocks were to fall by three different amounts. I did some further testing on the two portfolios by measuring its performance on days when the Nasdaq Stock Market plunges.

Don't forget thatI will be giving a presentation on the impact of news on financial markets to the Toronto Association for Business and Economics (TABE).

• Location: Bank of Montreal - BMO Boardroom, 21st Floor, 1 First Canadian Place, King St. West and Bay Street, Toronto (map)
• Date: Thursday April 5, 2007
• Time: 12 noon to 2 p.m.
• Cost: TABE and CABE members in good standing around $25-30, others $45-50. A light lunch will be available.
• Registration: Closer to the date you will be able to book on-line at: www.cabe.ca/chapters/TABE. Or you can call 905-845-3102 or E-Mail tabe@cabe.ca.
• TABE Members (only) may pay cash or cheque at the door, non-members and credit card payments (VISA, Diners/EnRoute, MasterCard, or American Express) are to be prepaid via the web site. No-shows will be invoiced. Charges include GST. (TABE GST number: R124389990).

Consensus Forecasts and Herd Mentality

This is the third and final note about a post by the FX guru of Nova Scotia, Tom Yeomans, entitled “Is news trading dead?”

Tom talks about how FX trading around economic announcements has changed over the last few years. He then outlines a view of the future of FX news trading that involves forecasting the announcement.

Since it is news (that is difference between the actual release and the market’s expectation just before the release) that moves the market you need not just a forecast but also a read on what the market thinks.

Tom discusses the use of consensus forecasts. He notes that “Usually they had 18-21 people making guesses. That always seemed a little suspect to me …”.

I have noted in another post about the problems with consensus forecasts.

However consensus forecasts can be made useful. In a paper on how forecasts can be used by financial institutions for risk management purposes I noted that surveys of forecasts can be used to develop scenarios for risk management and how …

“This allows risk managers to understand their potential losses conditional on a range of forecasts. The average forecast and the dispersion in forecasts can be used to build a model of the distribution of market participants' expectations. The fitted model can then be used to extrapolate to large moves and thereby address the problems of sparse and clustered data. Finally, conditional scenarios can be used to mitigate the lack of coverage in forecast surveys.”

Note that scenarios, based on forecasts, can also be used for speculating as well as risk management since the focus is on the entire set of possible outcomes and so scenarios paint a picture of the return and the associated risk.

Of course, for certain announcements getting a read on the market is easy since a full distribution of expectations can be had from auctions of economic derivatives.

Trading Scenarios

I’ve been thinking some more about a post by the FX guru of Nova Scotia, Tom Yeomans, entitled “Is news trading dead?” In a tremendously interesting and informative post, after chronicling the history, Tom outlines a view of the future of FX news trading.

He says that it seems now that he can’t straddle the market and he wants to avoid the chaos of the first few minutes after an announcement. As a result he says it is inevitable that he will have to make predictions of what the official numbers will be several hours prior to an important economic report. He suggests that a forecast within the framework of a money management system would permit the pyramiding trades early in the morning based on the assumption that we “know” what the economic report numbers will be. He goes on to suggest that this would more or less emulate the way a large brokerage or investment house would do it.

I agree. I think the most promising approach is to use range of outcomes (scenarios) conditional on a range of forecasts. This gives the likely return and the attendant risk associated with a particular position.

What would this look like? Well, right before certain big U.S. economic announcements we have the results of derivatives auctions that give us a very good view of the market’s expectation. The modeling that I have done links this expectation with market responses. For someone that is interested in the EURUSD following a non-farm payroll announcement, they might be interested the chart of the distribution of market expectations (link to example charts) and what the resulting return chart is for the EURUSD (example charts, and some more).

A Short History of News Trading

I’ve been thinking about a post by the FX guru Tom Yeomans entitled “Is news trading dead?” In a tremendously interesting and informative post Tom documents his experience in news trading as:

• 2003-4 Straddling – Traders would place an order 15 pips above and below current price 30 seconds prior to the release of an important economic report. No matter where price went, the trader would profit. Straddling worked well and brokers guaranteed fulfillment of the order.
• Whiplashing the Straddlers - My understanding is that in response to straddling, brokers increased spreads and took the waiting orders, then decreased the spread and filled the waiting orders, triggering stops all over the place seconds prior to the report being released. That move (called a whiplash) ended straddling.
• Tick trading - This trading exploited the delays in updating currency pairs.
• Delayed correlation signals – using the correlation of the USDCAD against the EURUSD to give entry and exit signals. Tom still uses the correlations but not the same way since the correlations are not as useful.
• News trading – Quote from “Is news trading dead?”: “When I first began trading economic numbers, I did it with B’berg on the web and constantly refreshing my browser or searching around for a feed that gave me them fairly quickly. I usually had a few minutes to place my trade, have it accepted, and then a few sips of coffee before it began to move. That was on the EURUSD in 2006. It often took up to 20 minutes before the UK based reports moved the market. I am dead serious.”
• News Arbitrage - Summer 2006 - thousands of people, faster entry, special brokers, and software began to scalp the market immediately after a report. As a result the spreads have widened prior to big reports. Mr. Yeomans might be somewhat responsible for this as he has been successfully training people to trade the news for some time.
• Wait and see – Tom now sets his triggers higher knowing that noise created by people jumping in and out during the first minute creates chaos. These days, Tom waits to see confirmation of the move in a clear, sustained direction. By waiting an extra minute or so to see the move going in the correct direction means that he can execute his trade when the spreads have dropped back to normal. The idea is to still base the trade on the economic numbers and results, but to increase the lot size of the trade while forfeiting the early gains of the move. By placing larger bets he hopes to get the same payoff as before news trading became so popular.

Tom has adapted to market conditions and continues to do so. Next I’ll discuss Tom’s view of the future of FX news trading and how I think it can be realized.

See Me Perform - Live and In-Person

This is an early warning that I will be giving a presentation on the impact of news on financial markets to the Toronto Association for Business and Economics (TABE).

• Location: Bank of Montreal - BMO Boardroom, 21st Floor, 1 First Canadian Place, King St. West and Bay Street, Toronto (map)
• Date: Thursday April 5, 2007
• Time: 12 noon to 2 p.m.
• Cost: TABE and CABE members in good standing around $25-30, others $45-50. A light lunch will be available.
• Registration: Closer to the date you will be able to book on-line at: www.cabe.ca/chapters/TABE. Or you can call 905-845-3102 or E-Mail tabe@cabe.ca.
• TABE Members (only) may pay cash or cheque at the door, non-members and credit card payments (VISA, Diners/EnRoute, MasterCard, or American Express) are to be prepaid via the web site. No-shows will be invoiced. Charges include GST. (TABE GST number: R124389990).

NFP FX Correlation Matrix

It is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets.

On Monday, November 06, 2006 in a post entitled “Recent Developments in How Economic Announcements Affect Financial Markets” I noted that the EUR and the CHF move in opposite directions when the U.S. trade news is announced. I expanded on this in the post on Wednesday, December 27, 2006, “Hidden Relationships” noting that the Swiss Franc and the Euro also appear, on average, to move in opposite directions to the U.S. Dollar when non-farm payrolls are announced.

Then on Friday, January 26, 2007 in "More Hidden Treasures" I looked at how the Yen and the Pound appear, on average, to move in opposite directions to the U.S. Dollar when non-farm payrolls ("NFP") are announced.

These NFP relationship can be encapsulated in a correlation matrix for foreign exchange returns right after the news. Here is the NFP return correlation matrix 1 minute after the announcement:

Announcement	NFP
Minute	1
	AUD	CAD	CHF	EUR	GBP	JPY
AUD	1	-0.8692	-0.925	0.9432	0.9136	-0.8767
CAD	-0.8692	1	0.8232	-0.8417	-0.832	0.7753
CHF	-0.925	0.8232	1	-0.9901	-0.9688	0.8832
EUR	0.9432	-0.8417	-0.9901	1	0.9655	-0.889
GBP	0.9136	-0.832	-0.9688	0.9655	1	-0.9016
JPY	-0.8767	0.7753	0.8832	-0.889	-0.9016	1

I have highlighted the correlations previously discussed. But as one can see there are other interesting leverage/hedging opportunities. Note that all of these correlations are significant. The 5% critical value(two-tailed) = 0.2787 for 50 observations (monthly data from Nov 1 2002 i.e. October 2002 release to Dec 8 2006 i.e. November 2006 release). If anyone would like a spreadsheet of my calculation of the 1, 5, 10, 20 and 30 minute return correlation matrices for the U.S. announcements of nonfarm payrolls, initial jobless claims, retail sales, and CPI, please send me an email to john.parkerATrelevanteconomics.com.

Retail Sales Correlations

t is not news that news moves financial markets. This blog will publish research on how, when, why, and which news moves what financial markets. It is important to know how different currency pairs move in relation to each other to manage portfolio exposure. Some currency pairs move together, while others move in opposite directions. Whether you are looking to hedge, diversify your positions, or find alternate pairs to leverage your view, it is important to account for the correlation between various currency pairs. In the last post I suggested that for those looking at economic announcements return correlations using data from announcement days are the most useful. Here is a set of correlations for the Retail Sales (ex-autos) announcement in the U.S. for the most traded currencies.

Calculated:	Feb 6 2007
Data From:	Feb 13 2001 (January 2001 release)
Data to:	Dec 13 2006 (November 2006 release)
Announcement:	Retail Sales (ex-autos)
By:	John C. Parker
	1-minute return correlation
		Correlation Coefficients, using the observations 1 - 71
		5% critical value(two-tailed) = 0.2335 for n = 71
	AUD	CAD	CHF	EUR	GBP	JPY
	1	-0.6816	-0.7672	0.7755	0.759	-0.707	AUD
		1	0.7193	-0.6596	-0.6659	0.5896	CAD
			1	-0.9635	-0.9051	0.8233	CHF
				1	0.9212	-0.847	EUR
					1	-0.79	GBP
						1	JPY
	5-minute return correlation
		Correlation Coefficients, using the observations 1 - 71
		5% critical value(two-tailed) = 0.2335 for n = 71
	AUD	CAD	CHF	EUR	GBP	JPY
	1	-0.632	-0.7237	0.7075	0.7452	-0.657	AUD
		1	0.6061	-0.6044	-0.5514	0.5866	CAD
			1	-0.9829	-0.9056	0.8347	CHF
				1	0.9092	-0.84	EUR
					1	-0.845	GBP
						1	JPY
	10-minute return correlation
		Correlation Coefficients, using the observations 1 - 71
		5% critical value(two-tailed) = 0.2335 for n = 71
	AUD	CAD	CHF	EUR	GBP	JPY
	1	-0.6801	-0.6913	0.6974	0.6813	-0.703	AUD
		1	0.5742	-0.5811	-0.4945	0.6132	CAD
			1	-0.9812	-0.8691	0.8178	CHF
				1	0.895	-0.807	EUR
					1	-0.736	GBP
						1	JPY
	20-minute return correlation
		Correlation Coefficients, using the observations 1 - 71
		5% critical value(two-tailed) = 0.2335 for n = 71
	AUD	CAD	CHF	EUR	GBP	JPY
	1	-0.6915	-0.7418	0.7805	0.7476	-0.627	AUD
		1	0.58	-0.6022	-0.5234	0.5736	CAD
			1	-0.9757	-0.8799	0.6684	CHF
				1	0.8931	-0.678	EUR
					1	-0.694	GBP
						1	JPY
	30-minute return correlation
		Correlation Coefficients, using the observations 1 - 71
		5% critical value(two-tailed) = 0.2335 for n = 71
	AUD	CAD	CHF	EUR	GBP	JPY
	1	-0.6505	-0.7858	0.8068	0.7017	-0.642	AUD
		1	0.5347	-0.525	-0.4108	0.4609	CAD
			1	-0.9738	-0.844	0.7219	CHF
				1	0.8651	-0.706	EUR
					1	-0.649	GBP
						1	JPY

AUD/USD - Australian dollar USD/CAD - Canadian dollar USD/CHF - Swiss franc EUR/USD - Euro GBP/USD - British pound USD/JPY - Japanese yen