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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 ...


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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.


3

There are a couple of issues with your example. First, for this ticker, there is a problem with the Yahoo price data for the period 2014-11-26 through 2014-12-03 in which the prices drop about 80% and then return to their trend line. This appears to be related to a stock split which Yahoo isn't handling properly and isn't real. Its causing part of your ...


2

1) Spurious autocorrelation of non-synchronous trading data was analyzed in this article: http://www.amazon.com/An-econometric-analysis-nonsynchronous-trading/dp/1245789457 During some time intervals a lot of trades occur and during some nothing happens(so prices are stale). So serial correlation of traded prices may be present but this may be due to stale ...


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 ...


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


1

Approach 1 is parametric regression, whereas approach 2 is non-parametric regression. How are they related: non-parametric regression models the entire distribution of all possible function forms, and then do the integration to calculate a single value E[Y|X]. It is function-form free. In contrast, parametric linear regression ASSUMES that the function ...


1

In the Johansen methodology there are five models unrestricted constant and unrestricted trend unrestricted constant and restricted trend unrestricted constant and no trend restricted constant and no trend no constant and no trend. As these models are nested they can be tested sequentially using likelihood ratio tests. For the usual sample sizes these ...


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Machine learning is a very wide field. Most often it is used for classification or regression tasks when you have labelled data to train the model. For example you show thousands of labeled pictures with an apple and computer "learns" what set of features gives high probability that picture contains an apple (for example, round, red etc). Now in your case ...


1

The R function you have to use is the lm() function. On QuickR you can find a simple and clear tutorial on how to estimate a linear (multiple) regression model generally using the lm(). As further reference, I suggest you to read the Introducing R tutorial about linear model by G. Rodriguez. I did not read the paper you cited, but, anyway, you should ...


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The relation between volume and the price dynamics (via volatility and jumps), has been explored by various academic papers. Just cite this one and its contained references: Wang, T., & Huang, Z. (2012). The relationship between volatility and trading Volume in the Chinese Stock Market: A volatility decomposition perspective. Annals of Economics and ...


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https://mechanicalmarkets.wordpress.com/2015/02/16/protecting-client-interests-anonymity-in-us-equities/ does analysis similar to the question here. It examines the post-trade performance of orders grouped by their MPID (only UBSS and anonymous orders had enough data points to report). It also looks at market impact upon the addition of a new order. ...


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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 ...



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