# Tag Info

18

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 postulated Geometric Brownian Motion could be interpreted as an AR(1) process on the logarithm of the stock price as you discretise the SDE from which it is a solution,...

11

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.

8

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 management setting. They have some limited value in strategic and tactical asset allocation. The ineffectiveness of time-series modeling in asset management stems ...

7

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 the following: It is possible (under some reasonable assumptions) to express any $MA(q)$ model as an $AR(\infty)$ model (and vice-versa for expressing $AR(p)$ ...

6

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 the univariate case to calculate proper scaling of volatility or its contributions if there are (cross-) auto-correlations. This happens in the P world due to ...

5

The idea of skipping a month was already in Jegadeesh and Titman 1993. The key academic paper in this area. Jegadeesh himself (without Titman) discovered a 1-month return REVERSAL effect in 1990, so it makes sense that he would take out 1 month in calculating returns in his later (1993) study. He already knew what happens to stocks that are up a lot ...

3

Yes, it exists and it is called ccgarch package. You can install that by simply running in R install.packages("ccgarch") and learn more about that on the CRAN relative paper. Moreover, I suggest you to read this lecture hold by the author during an R conference. Hope this help.

1

Welcome to quant.SE! I do not have specific experience with the CARR Model, however, I had a short look in the paper you mentioned: As far as I understand the model specification you just implement a GARCH(p,q) estimation for the range $R_t:=\max{P_\tau}-\min{P_\tau}$ where $\tau=t-1,t-1+\frac{1}{n},\dots,t$ where $n$ is the number of intervals used in ...

1

$D_{n}$ are the dummy variables taking a value of one from each point of sudden change of variance onwards, and $d_{n}$ are the estimated coefficients in relation to these breaks. So to apply your model: 1- Find the breaks using the ICSS algorithm. 2- Apply a garch model to your data by including dummy variables obtained in (1) in the conditional variance ...

1

You can use RATS software in which VAR GARCH is inbuilt function with CCC, DCC VECH and BEKK for co-variance estimation.

1

Here is a very good online library for econometrics ebooks: http://www.uebook.net/economics/econometrics

1

I guess the best way to test herding using intraday data is to use Hawkes modelling. Hawkes processes capture the fact an event is a consequence of a previous one (endogenous) or totally new (exogenous). A good start is Chapter V of Thibault Jaisson's PhD thesis: Market activity and price impact throughout time scales. It is of course related to market ...

Only top voted, non community-wiki answers of a minimum length are eligible