<t>Agreed. Indeed, if CAPM holds then the average Treynor ratio is the same. Good point.But I still miss the usefulness of the ratio. It seems to tell us something about historical over/underperformance with respect to systematic risk? As it would be r_M-r_f + \frac{\epsilon}{\beta}. Any enlightenme...

<t>Hi, I have read today a mention about the Treynor ratio. Apparently it is a similar thing to the Sharpe ratio but defined as follows:T_A=(r_A-r_f)/beta_A, in other words excess return divided by beta.The problem is this: Since according to CAPM r_A=r_f+beta_A*(r_M-r_f) we can rewrite the Treynor ...

<t>Yield=return. The riskier an asset, the more return you need to be compensated for the risk you take (assuming that it cannot be hedged). Since companies are riskier than countries (financially speaking, and this does not relate to the quality of the ones leading the entities, at least in general...

<t>Please be more specific. The new drug is tested whether it is better than the old one and the trial is defined as successful if the results allow you to say with a 95% confidence, that the new drug is indeed better than the old one. Is that correct? If it is so, then this has nothing to do with t...

<t>ok, I see.Just to finish this issue with the CAPM: Even though the volatility is finite, the independent jump process seems to impact beta (as it impacts sigma_S and beta=sigma_S/sigma_M*corr(S,M)). So my last question:Is the statement that beta only considers the amount of market risk of an asse...

<t>Alan,That's extremely helpful, thank you. Putting it in math terms helps a lot.But actually this means that a GBM with a jump process has infinite volatility? Or depends on the measurement intervals and can be anything? This is quite disturbing and leads to another question:In CAPM beta is define...

<t>Hi,This is a repost, as I haven't received any answers in the general forum. I would guess that the readers of this forum have probably less problems with the answer.Assumption:Imagine the following process: A standard geometric Brownian motion with constant volatility sigma and a superimposed di...

<t>Assumption:Imagine the following process: A standard geometric Brownian motion with constant volatility sigma and a superimposed discrete risk process that multiplies the share price from one (known) point in time T onwards with x>1 or y<1 (there is a probability p_x that it will be S*x and a pro...

a domino always occupies a white and a black square. If you remove 2 of the same colour then you are left with 50 of one colour and 48 of the other, so it is impossible to cover two squares.

Sorry list, my formula is a bit misleading as stated in my comment of Dec 28.dS_t is NOT a differential, it means d*S_t with d=dividend. It would have been better to write "D" instead of "d".Nevertheless, S_t is stochastic. That's the problem here.

No, it's certainly not zero, the integral describes d*the area under the curve in a stock price chart.

because of, let's say, moderate response rate here a clarification of the problem: the dS_t expression might be misleading, it is of course d*S_t. So it writes:We can take d out of the integral and expectation:So, you see, the actual problem is that I want an integral of S_t.

If the picture hangs x above the floor then wrap 2*x+epsilon of string around each nail. If we neglect friction the picture will touch the floor after removing one nail. I guess it's not the solution you were looking for...

with no dividends its "equal to". Instead of exercising you'd rather sell your option, where you also get the time value.It should actually read: C not less than c.

<t>This depends on the correlation, or better the joint probability distribution. You haven't mentioned any assumption regarding to this. Typically they might be correlated with each other. You can model that nicely with copulas. But you need to define the dependence structure, ie which copula, and ...