5
votes
Why worry about fat tails, if you can use stoploss?
Because we are modelling the underlying price process, not the value process of your stop-loss portfolio...
- 872
4
votes
Accepted
Do normal returns make the mean-variance portfolio model perform properly?
No, even if returns were perfectly normal (it really doesn't matter whether mean is zero and standard deviation is 1 - they can be anything), it wouldn't ensure that markowitz would perform well out ...
- 7,827
3
votes
What is better: A negatively skewed return or a positively skewed returns distribution?
The usual answer is that most risk assets tend to exhibit left-skew, with correlations ->1 into the left tail (ie diversification breaks down). And so positively skewed assets have attractive ...
- 5,001
3
votes
Why worry about fat tails, if you can use stoploss?
Not sure if this question deserves to be further piled onto, but alas...
Large, institutional portfolios nearly always hold relatively illiquid and OTC traded instruments. There is no stop-loss order ...
- 980
3
votes
EuroStoxx50: long index and short futures
Small details accumulated over 10 years will explain the discrepancy. You need to simulate the actual strategy i.e. include cost of funding the long index leg, cost of margining the futures leg, ...
- 1,356
3
votes
Accepted
Why can we assume that asset return rates are normally (or lognormally) distributed?
In the colloquial sense of the word "justified," it is not justified. I will describe why it is justified mathematically and under what circumstances and in what case it is not justified.
...
- 4,299
2
votes
Accepted
How to calculate the BHAR (Buy-and-Hold Abnormal Returns)?
Welcome to Quantitative Finance!
I reckon the BHAR (Buy-and-Hold Abnormal Returns) formula you are referring to is
$$\text{BHAR}_{i,h} = \prod_{t=1}^{h}(1+R_{i,t}) - \prod_{t=1}^{h}(1+R_{m,t})$$
where ...
- 1,028
2
votes
Interpretation of t-test in event study with dummy regression
As far as your second model concerned:
Abnormal returns for good news is $\beta_4$
The t-value of 3 tells it is significantly different from 0
The model does not account for effect of bad news so the ...
- 741
2
votes
Optimize portfolio of non-normal binary return assets
For a single period, I would consider scenario optimisation: simulate
your assets' returns (which you can do since you know their
statistical properties, including correlations), and in this way ...
- 3,176
2
votes
Why can we assume that asset return rates are normally (or lognormally) distributed?
It is justified in that you obtain some better information than you have without it.
For example, you can make the assumption and acknowledge some inaccuracy in your model. The inaccuracy of pricing ...
- 8,942
2
votes
Why can we assume that asset return rates are normally (or lognormally) distributed?
The awful truth is that we assume that returns that are optically quite close to (log)normal are indeed (log)normal, because it makes the associated mathematics of solving almost ANY subsequent ...
- 5,001
1
vote
What is better: A negatively skewed return or a positively skewed returns distribution?
It's a little simplistic to say that positive skew is better, you could for example have a return distribution which is negatively skewed but has a mean of 10%, versus a positively skewed one with a ...
- 221
1
vote
Why worry about fat tails, if you can use stoploss?
This is a real life empirical example: my ex-colleague now runs a trend following strategy (with some leverage time-to-time) and did not lose money during the recent market crash all thanks to his ...
- 1,830
1
vote
Why worry about fat tails, if you can use stoploss?
all metrics like VaR (how much you can lose on a given day) are based on a confidence interval in the distribution.
but the most important part of risk management is tail risk /extreme loss, which ...
- 370
1
vote
EuroStoxx50: long index and short futures
From a mathematical perspective and under the capital market assumptions of finance theory, you would be right. However in real life, there are a number of risks that remain that keep this from being ...
- 5,652
1
vote
Computing Buy-and-hold abnormal returns (BHARs) $= \prod_{t=\tau_1}^{\tau_2}(1+R_{i,t}) - \prod_{t=\tau_1}^{\tau_2}(1+R_{m,t})$
Summarizing the discussion in the comments: BHAR are computed using simple returns, not logarithmic returns.
- 10.6k
1
vote
Fame-French alpha for a single stock
The reason for using Fama French for portfolios is generally that you try to quantify whether your anomaly/strategy etc. is actually capable of providing returns in excess of what could be achieved by ...
- 185
1
vote
Interpretation of t-test in event study with dummy regression
For the t ratio, you should re-parameterise your equation so that $(β_0+β_4)$ is treated as one coefficient, say $\gamma$ or a coeff of a single variable. you cannot use one's t ratio for making ...
- 191
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