Suna Reyent

Calculating Country Risk Observed by Betas

The model as it is applied assumes exposure to currency risk, but it does not measure it. This is because I regressed the U.S. dollar returns of these countries to the global index, and did not include a currency variable to control for that risk.

Note that this kind of practice builds even more assumptions into the model, such as the assumption that the PPP and uncovered interest rate parity conditions hold and that investors are indifferent between domestic and foreign assets and do not demand a currency risk premium (which is rarely the case).

The model specification assumes that all markets are perfectly integrated into the world markets and there are no constraints to invest into any of the markets. Note that multi-factor models and conditional regressions such as usage of GARCH could account for these conditions but they would not give the kind of simple one-shot picture I was looking for.

Again, it could be argued that the model is incorrectly specified because it does not take into consideration many of the factors that are relevant. However I had two very simple questions, which is why I chose to go with one-factor ICAPM:

1) Which stock markets present the most significant day-trading risk?
2) How do these markets move up and down with respect to the global market in a single trading day?

Calculating Country Risk Observed by Betas

Mr. Chris B, thank you very much for the positive criticism and encouragment. That means a lot, I really appreciate it.

Calculating Country Risk Observed by Betas

Mr. Huebert,

I used to make a living producing and publishing quantitative models, tables / information for sell-side research. Developing proprietary models remains one of my areas of expertise. There are people who value this kind of information because they know exactly how to (or how not to) interpret them.

This is part of a much bigger risk management project that I'm consulting on. I thought it would be interesting to publish some of my findings. A lot of detail is given precisely because some people (such as commenters above) have doubted the accuracy of the measurements such as correlations.

This is precisely why a lot of detail is given. It is the nature of such quantitative work. I generally do not provide recipes for many models that I design because by their nature they are proprietary. However what I've done above is a very straightforward exercise for many experts in the field of computational finance and I thought publishing my findings would be an interesting real-world test for the CAPM model.

That having been said, I do not provide investment advice through Seeking Alpha medium nor do I market my services as such. This should be clear to many readers of my work. Thus I have made it clear that none of the above should be interpreted as an investment recommendation in any way.

Calculating Country Risk Observed by Betas

Mr. Flash Gordon, please be advised that while the ten-year correlation with respect to Czech Republic is relatively low at 0.45 placing the beta at 0.83, the two-year correlation presented in the two-year table has increased to 0.65, placing the beta at a more risky 1.14. This already verifies your observation that the Czech Republic remains a moderately risky market in terms of its systematic risk. Thanks.

Calculating Country Risk Observed by Betas

If I run these regressions on the past 6 months of data I'm confident that I will get significantly higher correlations. That's a good idea for another writeup. I will actually run the numbers and publish what I get.

However that's the nature of the beast when it comes to diversification. It does work surprisingly well most of the time until it doesn't, especially when you need it the most.

A powerful market crash as the current one is one such example. Every country got affected, even those that show the lowest betas.

Calculating Country Risk Observed by Betas

Beta measures the systematic risk of the security with respect to the market, and by its nature it is not diversifiable with another long security position. This means that a high beta security is more risky than a low beta one. What CAPM infers is that Germany and England have more systematic risk than Egypt and Morocco. This means that you will have more exposure to "market risk" if you are long England, and even more so if you are long Brazil.

There ARE ways of making portfolios beta neutral and you may indeed protect yourself from the general market swings by hedging yourself with futures positions or in some other fashion. The reason why I'm saying this is that you mention that a stock (or country) may have a high beta but it may still perform better at the end of the time frame. If that's the case you have captured that illusive alpha. This is a possibility but not always the case with all high beta stocks (or countries).

I hope this clarifies why I feel somewhat excited about Egypt and Morocco and several other emerging markets in Asia as a result of this study. By owning them, you will NOT gain a heavy exposure to "market risk", but you will still carry the unsystematic risks associated with these individual markets. Hopefully those unsystematic risks you carry are risks to the upside.

The point of the beta measurement is that it gives you a way of measuring your exposure to market risk by the virtue of owning a certain stock (or country). If I'm already long various markets, for instance, why do I add on the extra risk of Brazil if I'm not sure whether Brazil carries an alpha, or the ability to outperform the rest of the world?

Even if Brazil carried an alpha, by owning the country I would in essence be leveraging my long position if I'm already long the U.K., for instance. If I'm a hedge fund I have the option of getting rid of the total global beta of my position via various short futures positions or by shorting likewise beta markets, but you can imagine that all this is getting fairly sophisticated in terms of computational capabilities and beyond the realm of the ordinary investor like you and me. Not to mention that these types of "market neutral" positions have been shown to be not so market neutral when betas shift or when unexpected events occur under the category of "unsystematic risks".

To make the long story short, I do believe that beta is a fairly good way of measuring risks, at least among what's available to us. It does NOT, however, tell us whether a security or country is going to have successful performance or not.

In Search of Low (or Negative) Correlation Between Asset Returns

I'm sorry I can't claim much expertise or experience in Monte Carlo simulation. However I'm aware that it involves generating outcomes with a computer, which represent possible returns for the asset or portfolio. Of course they are generated using *assumptions* concerning distribution of returns, however the distribution doesn't have to be "standard normal distribution" or anything like that.

For instance to determine possible returns for a bond portfolio, the analyst needs to define the set of interest rates, or some path, which is clearly influenced by the analyst's preconceptions about the functioning of the bond market as well as international liquidity flows or the Federal Reserve and things like that (which will probably mirror the recent past, hence my objection to "forward-looking data"). What you put into the model determines what you get out of it. Of course these are categorized as "model risk".

That's all I know...

In Search of Low (or Negative) Correlation Between Asset Returns

I definitely agree. I think I overstated my case and there are instances where "forward-looking information" is a lot more applicable such as in calculating P/E ratios of companies. A trailing P/E ratio (and even 12-month forward P/E) would have been useless in calculating the value of Google when the stock was trading at $150, if I only looked at past information I wouldn't have bought it at that price, and wouldn't have decided to triple my position at around $170 (that I did because Wall Street was NOT covering it at the time.)

Of course there are models that incorporate company growth and clearly stock market takes that into account as we see certain stocks that seem very expensive in terms of traditional valuation ratios but might not be that way.

One of the conclusions I was trying to arrive at with that article was that it is possible to incorporate these so called "high risk" companies in a framework of a more diversified portfolio and clearly some of my "high-flying bets" in technology have failed (such as Sigmatel, my all-time embarrassment, and no it did NOT have a high P/E when I bought it and I did NOT increase my position as the stock fell, one of my trading rules), however if all these volatile stocks are incorporated in a portfolio that has enough oil, energy, materials and defensive consumer goods companies and such, you'll be able to absorb those losses and it will be a lot easier to carry a stock that requires an iron stomach.

I have reservations about Monte Carlo analysis however if it’s done with the knowledge that the assumptions that go into the model may be wrong, or worse biased, I think it can be a very useful tool.

But I’ve seen things in financial practice that do resemble pseudo-science rather than being a framework of hypothesis testing – we cannot “prove” something, that wouldn’t be science, theories are good to the extent that they are open to falsification.

I’m very happy with all the reactions the article is getting. I should probably announce that I hold Google and Sigmatel at this moment, though Google has become a significantly larger part of my portfolio and Sigmatel is about to go to zero, literally. Probably time to rebalance. I’m saying these to make the point that a portfolio can be successful overall even if it includes a stock that is close to extinction.

In Search of Low (or Negative) Correlation Between Asset Returns

Hi,
Thank you for all comments. Firstly, I wanted to make the article accessible and intuitive rather than a scientific treatise, I'm aware that every serious econometrician or statistician who is involved with portfolio theory will strictly look at asset returns and not even bother with the actual prices. I wanted to put these up to show the logic behind that and show the differences between the two.
Secondly, I'm aware that market concerns itself with forward-looking information, however the supposedly "forward-looking data" you put in your black box models are called "begging the question" in philosophy. That is, by the very action of putting in your "forward-looking data", you're already making predictions about the future so all these "black box" models are nothing more than glorified astrology to me. In other words, it is another version of Descartes' famous proof of the existence of God, but the assumptions he makes to come to that conclusion already presuppose what he's trying to prove, and hence the famous "Cartesian Circle" in philosophical terms.
My article does not imply that future will resemble the past, I've already stated that in the end if you read the article thoroughly.
I'm saying that a simple understanding of how correlation works will lead people to make more intelligent decisions about stock and etf ownership. And I'm showing historical data to support that idea, because I just don't think it is appropriate to show "forward-looking data" to support what I'm trying to prove, however I'm aware that's what the financial industry does.

In Search of Low (or Negative) Correlation Between Asset Returns

Hi,
The "Trend" is the index itself, or in the case of commodities, the actual futures price. I just had to differentiate betwen the index and its daily (or monthly) returns.