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When interest rates are high you want the average direction in which interest rates are moving to be downward; when interest rates are low you want the average direction to be upward. John Hull#Average 66
John Hull#Average 66
There are challenges in terms of the measurement of VAR for what are known as nonlinear derivatives, where things like gamma and vega are important dimensions of the risk. John Hull#Challenges 66
John Hull#Challenges 66
I guess any simple idea that is really good will catch on quickly. John Hull#Catch 58
John Hull#Catch 58
Briefly speaking, our conclusion is that stochastic volatility does not make a huge difference as far as the pricing is concerned if you get the average volatility right. It makes a big difference as far as hedging is concerned. John Hull#Far 55
John Hull#Far 55
The problem with interest rates are that you are not modeling a single number, you are modeling a whole term structure, so it is a sort of different type of problem. John Hull#Different 46
John Hull#Different 46
We started giving presentations at practitioner conferences in 1986, and since then all of our derivatives research has been stimulated by contact with practitioners. John Hull#Conferences 45
John Hull#Conferences 45
If each of your time steps is one week long, you are not modeling the stock price terribly well over a one-week time period, because you are saying that there are only two possible outcomes. John Hull#Long 44
John Hull#Long 44
Our research led on to other things, such as the fact that exchange rates are not lognormally distributed. John Hull#Distributed 39
John Hull#Distributed 39
Our tree is actually a tree of the short-term interest rate. The average direction in which the short-term interest rate moves depends on the level of the rate. When the rate is very high, that direction is downward; when the rate is very low, it is upward. John Hull#Average 38
John Hull#Average 38
One important measurement issue concerns the fat tails problem that I mentioned earlier. VAR is concerned with extreme outcomes. If the tails of the probability distributions we are using are too thin, our VAR measures are likely to be too low. John Hull#Concerned 30
John Hull#Concerned 30
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