Kiwiakos
  • Member for 7 years, 4 months
  • Last seen more than 1 year ago
5 answers
votes
6k views
Why aren't econometric models used more in Quant Finance?
16 votes

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.

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1 answers
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2k views
Difference between GARCH and Heston Volatility model
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14 votes

Heston gives an expression for the characteristic function, from which option prices can be computed. Therefore it can be calibrated (statically) on a set of vanilla option prices with different ...

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1 answers
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2k views
James Simons (Renaissance Technologies Corp.) and his model
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11 votes

In 1983 he was using Hidden Markov Models. Now he employs 100+ PhDs, therefore I expect he will have 50+ strategies using 200+ predictors. And set up as a production line, from the teams importing and ...

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3 answers
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2k views
Present and future role of pricing quants
11 votes

FO is shrinking across the large investment banks. The market is not developing new products that will need new pricing formulas, if anything it is reverting to more vanilla structures. Nowdays FO ...

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2 answers
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2k views
Why aren't the Fama-French 3 factors orthogonal to each other?
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9 votes

A factor model has the form $$r_{j,t}=\sum_n \beta_{j,n} f_{n,t}+\epsilon_{j,t}$$ Where $r_{j,t}$ is the return of stock $j$ at time $t$, $\beta_{j,n}$ is the sensitivity (factor loading) of stock $j$ ...

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2 answers
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624 views
How can the market price of a stock be significantly lower than its Bid and Ask?
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8 votes

I don't see this issue in Bloomberg, therefore I would assume it is just bad data in your source. Based on my snaps I suspect that your bid-ask is 15 min delayed. This stock is very active today, ...

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1 answers
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252 views
Why doesn't Variance-Gamma process flatten volatility skew for short term options?
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8 votes

VG belongs in the family of variance-mean mixture models. Given a horizon $T$ the distribution of log-returns $f$ is a mixture of Gaussians $f_G$ with randomised mean and variance. The randomisation ...

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1 answers
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442 views
SVI negative rates
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7 votes

I would say that $\log K/F$ points towards a log-normal type model. If I were you I would experiment with the moneyness defined as $K-F$ instead. This would make it consistent with normal dynamics. ...

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2 answers
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201 views
What's the name of this nearly-brownian stochastic process?
7 votes

The first process is a BM. The second does not exist in continuous time. The variance goes down too slowly with dt and the process blows up at the limit. You can break the (0,1) interval into 1, 100,...

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4 answers
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667 views
Implied volatility of a complex options position
7 votes

You can guesstimate by vega weighted implied vol. This is why: Say that you have a portfolio of options with prices $P_j$. Each one of them has a different pricing function $f_j$ (as function of vol) ...

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2 answers
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745 views
What is the preferred GARCH method in practice?
7 votes

I personally use the simple Garch(1,1) for volatility filtering in the risk management area. In fact in most cases I don't even estimate the parameters, I stick 0.94 for mean reversion, 0.04 for the ...

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2 answers
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684 views
Does the fact that volatility is not constant imply existence of skew?
7 votes

It is not the fact that volatility is time varying that creates the skew per se, but the fact that volatility is negatively correlated with the spot. That is to say, as the stock/index price declines ...

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9 answers
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9k views
Why the expected return rate of a stock has nothing to do with its option price?
7 votes

I think to gain intution you have to understand that the same agents that value the stocks will value the options. And agents compensate for volatility by demanding higher expected returns. Therefore ...

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3 answers
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394 views
Ran multivariate linear regression, checked normal probability plot, residuals are not normal. What can I do?
7 votes

Regression analysis, as a minimization of the sum of squared errors, does not require normality of the error term. The requirements are that errors are homoscedastic and uncorrelated. And these are ...

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1 answers
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2k views
How to get Geometric Brownian Motion's closed-form solution in Black-Scholes model?
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7 votes

If by 'solve' you mean how do we know that $\ln S_t$ is the right change of variable, then you can go by the following (not rigorous) line of thought: Ito's fomula suggests that given an SDE $$dX_t = ...

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2 answers
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890 views
Interpret simulation results ($P$ and $Q$ measures)
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6 votes

I believe that the confusion arises because of the wrong treatment of NIG. The answer to the question you link is misleading, as it simulates under P which is not appropriate for option pricing. None ...

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1 answers
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117 views
What just happened in the market?
5 votes

What happened was the BoJ announcement. Such large scale news are well covered in mainstream media (ft, bloomberg, etc) and also mainstream anti-media (eg zerohedge).

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1 answers
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223 views
Why the diff of signal is called positions and what does it mean in backtesting?
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5 votes

My understanding, in that context, is that signal indicates that you want to hold a share (signal is 1) or hold no shares (signal is zero). Therefore taking the diff will tell you if you want to buy (...

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3 answers
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44k views
How is PnL calculated
5 votes

Assuming that you are working for a bank, there are three different P&Ls depending on the function/ usage: Actual P&L calculated by Finance/ Product Control and is based on the actual price ...

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1 answers
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3k views
parameters in Heston model and their impact on volatility smile
Accepted answer
5 votes

Intuition: You can think of the vol smile as a reflection of the risk neutral distribution (compared to the Black Scholes Gaussian density). A fat tailed distribution creates the smile: fat tail -> ...

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4 answers
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10k views
Is a stationary process necessarily mean-reverting?
5 votes

The concept of 'mean reversion' is tricky in continuous time. Most people would call 'mean reverting' a process where the drift pulls back towards a long run mean, and I assume that this is what you ...

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1 answers
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134 views
How can risk-neutral pricing find the right price for securities if it doesn't account for risk premia?
4 votes

The price of a derivative does not explicitly depend on the expected return of the underlying, however the price change or return of the derivative depends on the return of the underlying. Hence the ...

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3 answers
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229 views
In portfolio theory, has volatility a logical place as an asset class?
4 votes

IMO: Volatility is a risk factor not an asset class. Asset classes are collections of assets and volatility is not one. Options, volatility derivatives, etc, are asset classes which might offer ...

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5 answers
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651 views
Heston Model Integration Oscillations
4 votes

I'd use FFT or similar rather than direct integration. Here is an old paper with Heston example: Option pricing using fractional FFT

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1 answers
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1k views
how to derive critical values for augmented Dickey–Fuller test (ADF) using Monte Carlo method?
4 votes

The ADF test assumes the DGP $$ \Delta y_t = \alpha +\beta t +\gamma y_t +\delta_1 \Delta y_{t-1}+\cdots +\delta_k \Delta y_{t-k}+\epsilon_t $$ The parameters are estimated using OLS on a sample of ...

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3 answers
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4k views
How to interpret negative log return more than -100%?
4 votes

Large? ? The relationship between normal and log returns is $$(normal return) = exp(log return)-1$$ Therefore log-returns can be from $-\infty$ to $+\...

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6 answers
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3k views
Why don't real-world probabilities affect the price of a call in a 1-step binomial model?
4 votes

"But just for fun, let's say Pr(S1=Su)=1% and Pr(S1=Sd)=99%, in which case, on average, the call at time 1 would be worth 0.01*10 = 0.1$. How would anyone be willing to pay 9.28$ for that ? I'm ...

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3 answers
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2k views
For $B_t$ a Brownian motion what is the probability that $B_1>0$ and $B_2<0$?
4 votes

B1~N(0,1) and B2=B1+Z, for Z~N(0,1). From that E(B1*B1)=E(B1*B2)=1, E(B2*B2)=2. Therefore they are bivariate Gaussian with covariance matrix (1,1;1,2) therefore probability is around 12%, which is the ...

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2 answers
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320 views
Stationary distribution for square root process
4 votes

There is a shortcut around the Forward Equation when you are looking for the stationary distribution. Let me write $$ dX = \mu(X)dt +\sigma(X)dW $$ for $$ \mu(x)=b(1-x)-ax\ \text{ and }\ \sigma^2(x)...

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2 answers
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2k views
Best written quantitative finance papers
4 votes

Journals that have a wider audience tend to have better written papers, such as Journal of Finance, Review of Financial Studies, or Journal of Financial Economics. Editors there try to ensure that ...

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