31
votes
Accepted
Why aren't econometric models used more in Quant Finance?
It's an interesting question.
I particularly agree with the $\mathbb{Q}-\mathbb{P}$ dichotomy mentioned by many.
I would add to the other answers that, come to think of it, the Black-Scholes ...
- 14.5k
17
votes
Why aren't econometric models used more in Quant Finance?
I think you need to differentiate between Q-quants vs P-quants. The former might not use Econometrics, but P-quants use them a lot.
- 4,307
12
votes
Why aren't econometric models used more in Quant Finance?
Traditional econometric (time series) models are of little or no value in forecasting market prices for purposes of "making money", i.e, generating excess return over a benchmark in an asset ...
- 3,595
11
votes
Accepted
Risk Model Validation
You should read this regulatory guidance:
U.S.:
SR 11-7:
https://www.federalreserve.gov/supervisionreg/srletters/sr1107a1.pdf
(it is identical to FHFA AB 2013-07 Model Risk Management Guidance, OCC ...
- 11.7k
9
votes
What are the significant implications of the long-run average variance rate and why Engle won the Nobel Prize for ARCH model development?
The best answer to your question is probably given by the Nobel prize committee itself in "The Prize in Economic Sciences 2003 - Advanced Information" document. You should read it in full. Below is an ...
- 1,694
8
votes
Why aren't econometric models used more in Quant Finance?
Having thought about this I think the following reason is also important and wasn't mentioned so far:
When you look at the inner working of this whole class of econometric models it all boils down to ...
- 27.3k
8
votes
Why aren't econometric models used more in Quant Finance?
My answer is very much in the spirit of Kiwiakos' answer.
E.g. in this paper (where I am one of the coauthors) we use VMA (vector moving average) models (in the multivariate case) and AR models in ...
- 13.6k
8
votes
Accepted
what does the cover page of Guyon and Labordere's Nonlinear Option Pricing represent?
Julien Guyon was so kind as to explain the story behind the cover and gave me permission to share it with the rest of the community:
There's no direct link between the contents of the book and the ...
- 8,436
7
votes
Confusion with volatility smiles implied by different models
In the context of option pricing, "implied volatility" always refers to the equivalent diffusion coefficient in the geometric Brownian motion (GBM) dynamics that is necessary to match an observed ...
- 5,949
7
votes
Bermudan Swaptions - Payer vs. Receiver (LGM)
I’m guessing you are finding that your model overvalues Bermudan receiver options and probably undervalues Bermudan payer options. The rationale for this has more to do with supply and demand than ...
- 16.2k
6
votes
What's the logic behind binomial model ups and downs?
one of the most fundamental results states that the binomial model converges towards the Black Scholes model if the step size $\Delta t$ converges to zero.
The Black Scholes model is an option ...
- 1,436
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 ...
- 8,436
6
votes
Accepted
What does it mean that model can reflect the ”volatility smile”
A model that reflects the volatility smile is one with dynamics that approximate pricing that yields an implied volatility smile. However, your question makes me suspect you are fuzzy on some of these ...
- 2,860
6
votes
Accepted
Interpretation of parameters in the CGMY model
Have a look at page 311 in the original paper from Carr, Geman, Madan and Yor (2002). The paramters are for the names of the authors. They explain the role of each parameter there. Note that $C>0$, ...
- 15.1k
6
votes
Risk Model Validation
Model Validation process usually consists of:
1. Conceptual Soundness Review (model assumptions, mathematical
representation, limitations)
Here you should try to re-derive the model from scratch and ...
- 5,938
5
votes
Accepted
How to derive this approximation of the risk-neutral expectation of the variance?
We first list the assumptions.
\begin{align*}
g_{t+1} &= \mu_g + \sigma_{g, t} z_{g, t+1}, \tag{1}\\
\sigma_{g, t+1}^2 &= a_{\sigma} + \rho_{\sigma} \sigma_{g, t}^2 + \sqrt{q_t} z_{\sigma, t+1}...
- 21k
5
votes
KMV-Merton Probabilties of Default vs Moody's EDF
I understand that Moody's uses an empirical distribution while KMV
uses a normal distribution in order to calculate these probabilities
KMV doesn't use a normal distribution to map distance to ...
- 1,549
4
votes
Confusion with volatility smiles implied by different models
Just wanted to point out a few small issues in your statement and maybe help with the conceptual model of these formulas.
implied volatility is defined as the value of the parameter σ we need
to ...
- 1,862
4
votes
Validating a Credit Scoring Model without Data
If you don't have a significant amount of losses in your portfolio to validate the model, you should be able to obtain external loss data and adjust it where necessary to better fit your organization. ...
- 1,549
4
votes
George Soros models
I haven't posted on SE much, so hope you will not mind if I also answer some comments here.
The best paper I have seen articulating what Soros does is by Flavia Cymbalista, a psychologist writing in ...
- 63
4
votes
Model Validation Criteria
If the model you're talking about is something that prices and risk manages an exotic (since you mentioned you calibrated to vanillas), I'd like to see:
How does the evolution of the volatility ...
- 1,865
4
votes
Is there a standard model for market impact?
In practice all impact models are sub-linear. Despite this is fact (seen in many academic publications, commercial and proprietary models), there is an interesting argument for using a linear impact ...
- 306
4
votes
Why change numeraire for the LIBOR Market Model
the point of the LMM is to evolve several different rates simultaneously. If you have rates $f_i$ from $t_i$ to $t_{i+1}$ and take a bond expiring at $t_j$ as numeraire then only the rate $f_{j-1}$ is ...
- 6,853
4
votes
What are the significant implications of the long-run average variance rate and why Engle won the Nobel Prize for ARCH model development?
$V_L$ is the long-run variance (or the unconditional variance) if and only if $\gamma=1-\sum_{i=1}^n \alpha_i$, because the long-run variance compatible with the model
$$
\sigma_n^2 = \gamma V_L + \...
- 2,632
4
votes
Architecture of a global pricing library with immutable payoffs
That's the best question that nearly no one asks. I'm with you on Quantlib and Strata, haven't really seen a very good design around but I've seen quite a few bad ones.
It is definitely doable and has ...
- 51
4
votes
Is it possible to adapt Fama French Model with a 6 factor Model?
A conceptual problem with ESG as a factor is that ESG criteria seem to me about preferences over how firms operate rather than preferences over when cashflows occur? I don't think this is a strong ...
- 6,864
4
votes
Accepted
Problem at deriving Bachelier formula with interest rates
As explained by @byouness, using Itô's Isometry, we get:
$$\begin{align}
V(S_T)&=V^{\mathbb{Q}}\left(\int_0^T\sigma e^{r(T-s)} dW^\mathbb{Q}_s\right)
\\[9pt]
&=E^{\mathbb{Q}}\left(\left(\int_0^...
- 7,855
4
votes
Closing prices are predicted very well but returns are predicted poorly
The previous day price is an excellent predictor of the next day price, however the previous day return doesn't tell you much about the next day return.
- 2,888
3
votes
How to derive this approximation of the risk-neutral expectation of the variance?
Directly from the paper:
We assume that the representative agent in the economy is equipped
with Epstein–Zin–Weil recursive preferences. Consequently, the
logarithm of the intertemporal ...
- 1,886
3
votes
Validating a Credit Scoring Model without Data
I do not know the regulatory rules for this case, but methodologically you could take another similar dataset "peer data" and then check how correctly your model predicts the losses of this dataset.
- 5,739
Only top scored, non community-wiki answers of a minimum length are eligible