15
votes
Accepted
Is volatility for the next day forecastable? To any extent?
Upon close reading, this appears to be 3 (interesting) questions, not one. I'm not sure if the mods have the tools needed to split it up, so I'm just going to write down the three questions as I see ...
- 729
9
votes
Accepted
Copulas simply explained
I found Coping With Copulas by Thorsten Schmidt really helped me to get a more basic understanding of copulas. As well as looking at some simple examples in R and thinking about different directions ...
- 206
7
votes
Copulas simply explained
In the theory of copulas you want to model a multivariate (often bivariate) distribution and keep the marginals fixed.
Thus you have random variables $X$ and $Y$ with cdf $F_X(x) = P[X \le x]$ and $...
- 13.2k
7
votes
Copulas simply explained
The best introduction to copulas I know, i.e. with rigour and intuition, is the following.
THE QUANT CLASSROOM BY ATTILIO MEUCCI
A Short, Comprehensive, Practical Guide to Copulas
Visually ...
- 26.7k
7
votes
Accepted
Is it possible to deal with non-normal distribution in Black-Litterman model?
Well there are two main things to consider here.
Many implementation of Black-Litterman use the market portfolio and the ex post volatility and correlation structure to back out implied returns to ...
- 2,874
7
votes
Accepted
Reconciling Two Claims About Volatility Under Fat Tails
I don't think the claim that "Lévy alpha-stable distributions are better descriptors of returns" is universally accepted.
While Mandelbrot (and others before him) has correctly identified ...
- 118
6
votes
Accepted
Quantile normal and lognormal
Quantiles are preserved under monotonic transformations, hence the quantile for $Y$ is simply the exponential of the quantile of $X$, no need for corrections whatsoever (see here for instance).
Put ...
- 13.9k
6
votes
Accepted
Produce the random variable for an asset from a uniformly distributed random varible
The question requires you to provide a method which uses uniform random variables and transforms them to generate realizations of the described asset values.
To give a bit more general answer: this ...
- 76
6
votes
Accepted
Is it always better to use the entire distribution of a financial returns series, not just $\mu$ and $\sigma$?
It depends.
For example, if you're doing option pricing in the log normal world returns are completely described by the mean and standard deviation. If you add jumps, you would also need to ...
- 7,598
5
votes
Consensus on Cauchy distribution for stock prices
I wrote a proof deriving the distribution of returns for all asset and liability classes. If there were no budget constraint, limitation on liability and liquidity had no cost, then you can prove the ...
- 4,066
5
votes
Accepted
Calculate VaR for a liabilty taking a exponential distribution?
The VaR of level $\alpha$ a loss random variable (the bigger the worse) is the quantity $q$ such that the loss is bigger with probability $1-\alpha$.
Thus we need a $q$ such that
$$
P[L>q] = 1-\...
- 13.2k
5
votes
Why worry about fat tails, if you can use stoploss?
Because we are modelling the underlying price process, not the value process of your stop-loss portfolio...
- 758
5
votes
Accepted
Change of measure
You can't have a precise argument without a precise definition.
In general, the appropriate notion of integral here is the Lebesgue-Stieltjes integral. In a fairly general setup, let $F: \mathbb R \to ...
- 186
5
votes
Accepted
Which financial time series have a PDF and/or CDF?
For a continuous variable the PDF is the derivative of CDF. So returns or prices don't have a pdf if the cdf is not differentiable, e.g. it "jumps" at some point. The simplest models we use, ...
- 1,242
5
votes
Accepted
Terminal wealth distribution from dollar cost averaging
I understand that you assume multiplicative gross returns, $W_t=W_{t-1}R_{t-1,t}=W_{t-2}R_{t-1,t}R_{t-2,t-1}$ and so on.
Let's assume that you are investing $I$ at the onset, and increase your ...
- 5,368
4
votes
Can Gaussianity of returns depend on the time frame?
Surely, there is; search for aggregational gaussianity in Google Scholar or ScienceDirect.
In fact, 5 minutes returns are leptokurtic and fat-tailed; then as you increase timeframe, returns become ...
4
votes
Accepted
Density of Geometric BM via Fokker-Planck
Hi bcf: This is a good question. As you pointed out below,
\begin{align*}
p_0 &= \delta(y-y_0)\\
&=\delta(e^w-y_0).
\end{align*}
Then,
\begin{align*}
p_0 * g &= \int_{-\infty}^{\infty}\...
- 20.4k
4
votes
Accepted
What the implied distribution really is?
In this related question How to derive the implied probability distribution from B-S volatilities?, it is shown how to infer the implied probability density of the future prices of a risky asset from ...
- 13.9k
4
votes
How to simulate the exponential law over an interval of the form [0,T]?
You should post on mathematics.stackexchange
I answer but I should not.
Let $X $ be an exponential r.v. of parameter $\lambda $
$$P (X<u|X <T)=\frac {P (X<min (u,T))}{P (X <T )} $$
So ...
- 2,362
4
votes
Compare two distributions for forecasting returns
Answer
If you assume your returns are independent (yes your models might loosen this assumption) then the two models, $Q_1$ and $Q_2$ assign probability distributions to the returns on any given day, ...
- 7,997
4
votes
Accepted
Sampling from an empirical distribution
Financial returns exhibit well known time-dependancy in its higher conditional moments. For starters, just about no matter how you produce a time series of conditional volatility, it will be exhibit ...
- 2,356
4
votes
Accepted
What is the distribution of the risk-free asset?
The standard way to think about this is that at time $t$ the riskless asset gives you known return of $r_{f,t}$ over a short time period. However, this rate may itself be time-varying and stochastic ...
- 1,242
4
votes
Accepted
FX spot distribution with student-t returns
1. Theory
The Student $t$ distribution does not exhibit a moment generating function
$$
M_X(t)=\mathbb{E}\left(e^{tX} \right)
$$
Hence, there exist no closed form solution for $M_X(t=1)=\mathbb{E}\...
- 5,368
4
votes
Accepted
Taleb's Black-Swan: interpretation of the exponent
I finally got the idea behind the example. To illustrate it in a more general setting I will present a rigorous proof:
Let $x_k$ denote the salary and $b_k$ the number of persons that earn $x_k$ or ...
- 183
4
votes
Fat tailed can be estimated through a t-distributions?
B is the correct choice.
I honestly would wish multiple choice would not even exist. It is the worst way of testing knowledge in my opinion.
Without knowing the details of what was taught, I would say ...
- 4,208
4
votes
Integral of brownian motion wrt. time over [t;T]
The last integral is correct as
$$\int_t^T W_s ds = \int_t^T (T-s) dW_s \sim N\left(0, \int_t^T(T-s)^2ds\right) = N\left(0,\frac{1}{3}(T-t)^3\right).$$
Ref. Arbitrage Theory in Continuos Time (Björk, ...
- 155
3
votes
Accepted
Can Gaussianity of returns depend on the time frame?
My main reference will be "Dan Xu, Christian Beck - Transition from lognormal to chi-square superstatistics for financial time series"
Non-equilibrium statistical mechanics (more specifically, ...
- 335
3
votes
How to combine Gaussian marginals with Gaussian copula to obtain multivariate normals?
You can express the Normal distribution by Sklar's Theorem in terms of Gaussian Marginals and Gaussian Copula as follows:
$$F(x_1,...,x_n)=C(F(x_1),...,F(x_n))=C^{Gau}(N(x_1),...,N(x_n))$$
So the ...
- 5,629
3
votes
Accepted
Brownian Bridge's first passage time distribution
What about this sketch of an answer: Let's put $T=1$ in your formula to simplify the notation. Then $Y_b(t)$ is a Brownian bridge where $Y_b(0)=0$ and $Y_b(1)=b$.
This can be written as $Y_b(t) = b\ ...
- 4,217
Only top scored, non community-wiki answers of a minimum length are eligible