5

Because we are modelling the underlying price process, not the value process of your stop-loss portfolio...


3

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 on a corporate bond or term loan, as an example. This is unrealistic even in equities. Let's say you hold 5% of the shares out on a small cap, do you just have ...


3

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 of sample. The reason is because even if data is normally distributed it is hard to estimate means of returns. The standard error for an estimate of a mean like ...


3

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, replicate the index roll properly (create a composite rolled-future index where the 3rd Friday return is VG1(Friday close) / VG2(Thursday close) - 1 and take the bid-...


2

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 effect of bad news will mostly be found in spikes in residuals around time of bad news releases. $\beta_0$ is return when all other factors in the model (...


2

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 create a large number of scenarios. Collect these scenarios in a matrix, R, say, with scenarios in rows, assets in columns. The portfolio returns for a given weight ...


2

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. Let me begin with the simplest of equations $$\tilde{w}=R\bar{w}+\epsilon,\epsilon\sim\mathcal{N}(0,\sigma^2).$$ Let us assume that this equation is an element of our ...


1

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 stop loss triggers combined to the strategy. Stop losses are helpful and some big asset managers (I believe Aussies are in this category) do consider this a very ...


1

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 can actually cause the business to go bust, and metrics like expected shortfall (if you end up in the tail, how ugly can things really get) are much more relevant ...


1

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 a swaption based on the assumption of normality (or lognormality) leaves you in a far better position than having no assumption, nor any model to begin with. ...


1

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 a risk free return. Among these risks are dividend risk, taxes and tax risk of different investors, interest rate risk and central bank policies, inter and ...


1

Summarizing the discussion in the comments: BHAR are computed using simple returns, not logarithmic returns.


1

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 passive exposure to the known risk factors included in the model. CAPM essentially does the same but only looks at the passive exposure to market index. I don'...


1

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 inference on the other. $β_0$ is the average return on this stock, the coef on dummy variable absorbs the abnormal returns. You could do this: $$R_t=\beta_0+\beta_{0}...


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