Defining the Value-at-Risk

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En provenance du cours de University of Geneva

Portfolio and Risk Management

871 note

University of Geneva

Portfolio and Risk Management

871 note

Cours 3 sur 5 dans Specialization Investment Management

In this course, you will gain an understanding of the theory underlying optimal portfolio construction, the different ways portfolios are actually built in practice and how to measure and manage the risk of such portfolios. You will start by studying how imperfect correlation between assets leads to diversified and optimal portfolios as well as the consequences in terms of asset pricing. Then, you will learn how to shape an investor's profile and build an adequate portfolio by combining strategic and tactical asset allocations. Finally, you will have a more in-depth look at risk: its different facets and the appropriate tools and techniques to measure it, manage it and hedge it. Key speakers from UBS, our corporate partner, will regularly add a practical perspective on these different topics as you progress through the course.

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Risk Management

This fourth and final week is dedicated to risk. We will start by looking in more depth at different sources of risk such as illiquidity and currency risk but also at the different tools available to investors to perform risk management. But how should we measure risk? We will see that it may be valuable to go a step beyond standard deviation, the risk measure we used so far, and look at the Value-at-Risk and Expected Shortfall which focus on potential large losses. Finally, we will use the financial instruments at our disposal to hedge market and currency risk.

Defining the Value-at-Risk4:52

Computing the Value-at-Risk5:21

Defining the Expected Shortfall6:34

Computing the Expected Shortfall3:46

Risk management applied to portfolio allocation6:08

Banking regulation & Basel recommendations: How did we get there?7:28

Rencontrer les enseignants

University of Geneva- Tony Berrada
SFI Associate Professor of Finance
Geneva Finance Research Institute
University of Geneva- Ines Chaieb
SFI Associate Professor of Finance
Geneva Finance Research Institute
University of Geneva- Jonas Demaurex
Teaching Assistant
Geneva Finance Research Institute
University of Geneva- Rajna Gibson Brandon
SFI Senior Chaired Professor of Finance and Managing Director of the GFRI
Geneva Finance Research Institute
University of Geneva- Michel Girardin
Lecturer in Macro-Finance - Project Leader for the "Investment Management" specialization
Geneva Finance Research Institute
University of Geneva- Philipp Krueger
SFI Assistant Professor of Finance
Geneva Finance Research Institute
University of Geneva- Kerstin Preuschoff
Associate Professor of Neurofinance and Neuroeconomics
Geneva Finance Research Institute
University of Geneva- Olivier Scaillet
SFI Senior Chaired Professor of Finance and Vice-dean (research) at GSEM
Geneva Finance Research Institute

[MUSIC]

Hi I'm Olivier in this session I will talk about the value at risk and

how we can use that value at risk.

So again, we will address two questions.

The first question is what is a value at risk or

how can we define the value at risk?

And the second question that we will address is

how can we compute that value at risk in practice.

So, what is the value at risk?

So you have a very informal definition which so in deed a quantitive and a very

synthetic risk measure and the value at risk try to address very simple question.

So, what is the minimum loss level?

So that you can have for a given probability.

Okay, so let's look at the graph.

So we see that we have the loss distribution and

here the loss distribution is symmetric but it doesn't need to be symmetric.

So when you look at the value at risk here you focus on

the right tail of the loss distribution.

And as you can see in the right tail I have a level which is called

the value at risk.

And that level corresponds to the level so that I have a 1% probability level

to be above that value at risk through that level.

So to be concrete, if my value at risk is equal to $1 million loss,

it means that I will have a 1% probability level to have

a loss above that $1 million.

Okay, so this is in the definition of the value at risk.

So let me now be a little bit more formal in terms of the definition and

look at the formula.

So the first formula correspond to a probability level and

I can see that the poverty level is 60 equal to 1 minus alpha.

So is alpha is equal to 99%, 1 minus alpha will be equal to 1%, and

I look at the 1% probability and I look at the probability of which type of event.

when the event is very simple I look at the return of my portfolio which is

denoted by RT and I will add something, and

that something is called the value at risk.

So that when I consider the return on my portfolio plus my value at risk,

the sum of the two have a probability

that this sum is negative will be equal to 1 minus alpha.

For example, equal to one person.

So if I look at the definition, the value at risk is in fact,

can be viewed as a capital as a safety caution.

And this is why the value at risk by convention and

this is what you will find in the recommendation of the Bazaar committee,

the value at risk is defined with a positive sign.

Okay, do in fact it correspond to.

An actuarial standard, so for an actuary, when I look at a loss,

the loss has a positive side.

I will lose $1 million with a positive side.

If I'm a finals guy, so finals guys are used to use a minus, so for a finals guy,

you don't lose $1 million, but you will lose minus $1 million.

So this is a subtle difference when you look at the definition

of the value at risk.

So the value at risk is defined with a positive sign.

So now you can see that this first definition can be written as

a probability that minus the return on your portfolio

is above the value at risk is equal to 1 minus alpha.

And this is exactly the definition that I have used when I was looking

at the distribution of my losses, when I was looking at the level,

so that I have a point equal to one person to be above that level.

So above $1 million for example.

Now if you look at the definition of the return of the portfolio.

So the return of portfolio is simply define as a sum of the allocation,

here the allocation already noted by a, but

if you look at other presentation it might be for example define as W for weights.

So for example if I have 50% of my investment in IBM.

This A will be equal to 50%.

If I have 20% in Dell the A will be equal to 20%.

So if I multiply my weights or my allocation by my return, and I do that for

all of my asset and I sum everything, I will have the return on my portfolio.

So this is the definition of a portfolio return.

[MUSIC]

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