0:11

And that coefficient and first and foremost and

the far more important thing about the correlation coefficient.

I know statistics can be boring.

I used to teach statistics I know that nobody likes it.

And when you start talking about correlations and

things like that and co-variances.

People sort of disconnect this is not for me, this is not interesting.

You do have to pay attention here because and let me speak here from

a financial point of view from a portfolio management point of view.

You can never have a proper portfolio.

You can never build a proper portfolio if you ignore the idea of correlation.

Because whether and to which degree.

Assets move in sync, or in completely different cycles,

is what's going to determine how much you can diversify your portfolio.

And again, we haven't quite defined this concept of diversification just yet.

But what is important for

you to keep in mind is that you cannot have a proper portfolio.

If you ignore this concept of correlation.

Now, correlation sometimes, you know,

many of the things in finance are referred to with Greek letters.

We've already seen Beta and correlation is one of those.

And the, the typical letter used to describe correlations is basically Rho.

So, sometimes when you hear people talking about Rho they basically talking

about correlations.

And, what we're going to spend a few minutes now is,

is in trying to understand why these correlations are very important.

So first, let's, let's define it.

Correlations are always measured between pairs of variables.

It could be two assets.

It could be height and weight.

It could be anything.

Any two variables can have a correlation.

And that correlation can actually be estimated.

Now, how do we estimate the correlation for us, at this particular point,

it's not important.

The technical note is going to help you a little bit with that.

But what is important is that you understand the concept.

What is the correlation and why that correlation is important.

Now, first, what does it measure?

Well, it measures the strength of the relationship.

I can have two variables, which again, could be the return of an asset and

the return of another asset.

Or it could be the height and

the weight of all the people taking this course, right?

In which case you more or less would expect a positive.

Relationship, but that basically says something.

It says that there are two things about correlation that are important.

One, is the sign of the correlation, and the sign could be positive or

it could be negative.

If it's positive, it basically means that the two variables tend to move together.

So where you would expect taller people to be a little bit, heavier and

shorter people to be.

A little bit lighter so

in general if you actually look at it over a, a large number of people.

You would expect a positive correlation between height and, weight.

In the same way that you would expect a positive correlation in

finance between risk and return.

So one thing that is important.

About those correlations is the sign.

Is the correlation positive or is the correlation negative.

If you were selling ice cream for example.

In the, and then you actually looked at sales of ice cream and temperature.

Well more likely than not there's going to be a negative correlation because

the colder is the temperature.

The less ass, the less ice cream you're going to sell.

So a correlation could be positive.

Or a correlations could actually be negative.

Now that's not the only thing that matters.

The other thing that matters in terms correlations are basically the strength of

the relationship.

Because it's strength relationship can be very loose and by that I mean that.

If you know the value of one of

the two variables there's not much that you can say about the value of the other.

Or it could be very strong.

And by very strong I mean that if you know the value of one variable.

You can make a fairly accurate prediction in terms of what would be the value of

the other variable.

And so it's important that you keep in mind these two dimensions of correlations.

Correlations measure the sign.

Positive or negative?

Do they move together?

They tend to move together, or do they tend to move in opposite directions?

And the strength.

is, is there a clear relationship between the two?

By knowing the value of one variable, can I make an accurate prediction or

a very loose prediction about the value.

Of the other variable.

So this correlation coefficient that we're looking at.

This row that we're looking at, measures the sign and

the strength of the relationship between these two variables.

And by measuring the sign and the strength obviously the sign can only be two.

Could be positive or could be negative.

Of course it could be zero, too, but that would be a very.

Special case, and in terms of the strength, it could be weak or

it could be strong.

We're going to get a little bit more technical in just a second.

But for now let's say strong relationship,

basically tells me if I know the value of one variable.

Gives me a very accurate predication of the other.

And the weak, if I know the value of one variable, it doesn't tell me a whole lot.

About the variable of the other.

Now let's do a little bit of theory, just a tiny bit of theory.

And, but this, this is important.

The range in which correlations fall can go between one on the positive end and

minus one on the negative end.

Now, on the positive end, the meaning of a correlation equal to one.

There's two important things.

Remember that we're looking at the sign.

And we're looking at the strength.

Well, in terms of strength, it doesn't get any stronger than that.

That is, if I can, if I could tell you.

If you give me the value of one of the two variables.

And I could tell you exactly what the value of the other is going to be.

Then that is basically a correlation equal to one.

That is what sometimes we call it deterministic relationship if

you know the value of one.

You can tell exactly, not approximately, not pretty accurately, but

exactly what would be the value of the other.

In other words, if you have X on one axis and Y on the other axis.

The relationship between X and Y is given by a straight line, and

all the points would actually fall along the line.

So that if I give you the value of one,

you could tell me exactly what is the value of the other.

That would be a positive relationship, and a relationship equal to one.

It doesn't get any stronger than that.

It gives you total accuracy, total predictability.

Know the value of one variable, you will know exactly the value of the other.

Let's jump on the other end, and let's go to the range, to the value of minus one.

Well, minus one means more or

less the same, the only difference is that now, the relationship is negative.

And so basically we have, if we have X here and

Y here, we have a line with a negative slope.

And all the points along that line would actually fall exactly along that

line with a negative slope.

And again it remains the case that if I give you the value of one variable.

Then you can tell me exactly what will be the value of the other.

In terms of the strength it doesn't get any stronger than that.

So the two extremes plus one and minus one is as strong as a relationship can be.

And it's as strong as it

can be because then the relationship becomes deterministic.

You don't predict more or less what the other variable will be.

You actually predict exactly what the other variable would be.

Now with the way I'm expressing this, you can safely guess that in

finance we don't have any relationship with values of one or values of minus one.

There are no deterministic relationships in finance.

So what matters, is whether we're getting close to one extreme or

close to the other.

Because that would actually indicate a very strong correlation between the two.

But in finance, you know, finance is not physics.

In physics you, you have.

And in mathematics, you have a lot of deterministic relationships.

In finance, we don't have those.

But it's still important.

Although we do not find in practice,

variables, financial variables that are correlated equal to one.

Or correlating equal to minus one.

It's important that you know, that this is the highest possible value.

And the lowest possible value because the closer we get to those extremes,

then the stronger the relationship actually is.

So, again, it's important to keep in mind that although in financial markets.

We do not expect to find valuables that have a correlation equal to one or

equal to minus one.

The extreme values of the correlation coefficient,

it is important to know the theoretical streams.

Because what we really want to know is that the closer

the correlation coefficient gets to one.

Or the closer it gets to minus one, then the stronger.

Is the, the relationship between the two variables that we're looking at,

and the more predictability there's going to be between these two variables.

Now, on the same token, as we get away from the streams and

we're getting closer to zero.

Then the relationship becomes weaker and weaker and weaker.

And, and actually becomes weaker.

Well, remember what that means.

It basically means that if I give you the value of one variable there's very little

that you can tell me about the volume of the other.

When we are getting, approaching zero on both from the positive side and

from the negative side.

Then we have more than certainty.

Basically our model doesn't work if I tell you one variable,

there's very little you can tell me about the the value of the other variable.

Now, here's one important thing.

Although in theory, a correlation could be positive or

could be negative, it could be all the way or all the way to minus one.

For example if you look across world equity markets, or

if you look at individual stocks within a market.

If you look at long enough period of time,

you're going to find that all these correlations are positive.

And the reason they're positive, its back to some of the issues we discussed,.

In the first session that is something tends to pull all the returns in

the same direction and that is what we call the market factor.

That is each company will be affected by the lot of individual factors and

in the portfolio sort of diversify way,.

But, there's going to be someone pulling or something pulling the return.

Those global factors, macro factors, pulling the return of all the companies in

the same direction or all the market in the same direction.

Remember, this is on average and over time.

On any given year, some stocks will go up and

some stocks will go down within the market.

And some markets will go up and

some markets will go down within the world market.

But, what really matters is that you know, that degree of

diversification that we obtain when we put all these these assets together.

Now, why is it that it is positive?

Well it depends on that market factor but let me go back one,.

Final time to the to the the markets we were working with in Session One.

And if you actually look at last line now,

that give you the correlation between each individual market and the world market.

And so if you look at the 1.00 for the world market.

Well that basically tells you that the correlation between a variable and

itself is going to be one.

It's like plotting the same variable twice.

And so by definition, the correlation between a variable and

itself is always going to be one, but look at the other numbers.

The US market, very high correlated with the world market.

The Spanish market, pretty highly correlated with the world market.

And the Egyptian market, much lower correlation with the world market.

And this should not be surprising.

The reason it should not be surprising is because Egypt is an emerging market.

Emerging markets tend to be a little bit more isolated from large world

capital markets and large world equity markets.

And so you would expect that small more isolate markets.

Almost by definition would have a lower correlation that large and

more integrated market.

But what I wanted to highlight with this.

Look at the data that we've been looking at so far.

Is that all these three correlations are positive.

The correlation between the U.S. and the world market.

Spain and the world market.

And Egypt and the world market.

All of them are positive.

In different degrees.

The U.S. is the most highly correlated.

Egypt is the least highly correlated.

But all of them tend to move in the same direction.

That basically means remember.

The fact that all the correlations are positive,

that means that when the world market goes up, these three markets tend to go up too.

It doesn't really, we're not talking about causality here.

We're not saying that because the world market goes up.

The Spanish or the Egyptian market go up or the other way around.

What we're saying is simply that they tend to move together.

And just in passing let me mention that, that is an important thing.

Correlation does not measure causation.

And, and what basically that says.

Is that when you say that two variables have a very large positive correlation, or

a very large negative correlation.

You're, you're not implying anything about which one the determines the other.

What you're saying is that they tend to move together.

In the same direction or

the opposite direction and that they're very strongly related.

But you're not saying anything when you calculate a correlation in terms of

which one is affecting the other.

It doesn't matter whether x affects y or affects x, the only matter is.

The only thing that matters is whether they tend to move together in

the opposite directions.

Whether their relationship is strong or

their relationship is actually much weaker.

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