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None of the previous answers have mentioned the fact that Bloomberg supports an API with support for all the main languages (C, C++, Java, Python, Perl -- and even Node and Haskell support on GitHub), on all the relevant operating systems: Windows, Linux, OS X, Solaris. This includes support for tick data which is stored in a rolling window (ie from ...


5

The use of kernels to estimate volatility using intraday data is "nothing more" than combining: intraday volatility estimation kernel smoothing Thus you have to take care about the "usual pits" of these two approaches. Intraday volatility estimation. I hope you know the "signature plot" effect. Of course if you use the proper estimation method, it should ...


4

The traditional way is to pre-filter the returns thanks to the a relation similar to : $r^{f}_{t} = r_{t} /\phi_{t}$ where $r_{t}$ are the squared log returns, $r^{f}_{t}$ the filtered squared returns and $\phi_{t}$ the periodicity component. $\phi_{t}$ is a deterministic intraday component (the seasonal effect at time $t$). We estimate the GARCH model on ...


3

Complete intraday data history can be obtained through the Thomson Reuters DataScope Tick History (TRDTH) archive: http://thomsonreuters.com/tick-history You may ask them for a trial subscription.


3

Unmatched buyers and sellers prices are called bid/ask offers. What you are referring to is the order book. Yes, this is something you can see in real time if you subscribe to it with your broker (L2 quotes, should be well under 100$ a month). L1 quotes are the 1st level bid/ask and the recorded transactions while L2 quotes are for the full order book ...


3

I think the answer is driven by asking yourself a few questions : If you are a practitioner at what frequency are you able to trade and want to trade ? (you are limited by this so no need to go to higher frequencies than that in any case) What effect(s) do you want to study ? What is a common frequency used by practitioners or academics for the question ...


3

The data is numbered by order in the date column. Its not a real timestamp, to find the actual time, you need to look at the header, where the exact time where the data starts is noted. For 1 second intervals, change the i-flag in the URL to 1, like here: https://www.google.com/finance/getprices?q=SAP&x=ETR&i=1&f=d,c,o,h,l&df=cpct&auto=...


3

In any finite sample, it is always possible for the Zhou estimator to return a negative number, even though we know the unobservable parameter being estimated is non-negative. This is a well known issue in the academic literature. There are several approaches to dealing with this problem: 1) Ignore it. (I don't like this one). It is particularly nefarious ...


3

Here's the SPX & DAX data in CSV format (you can open in Excel): http://real-chart.finance.yahoo.com/table.csv?s=%5EGDAXI&d=11&e=1&f=2014&g=d&a=10&b=26&c=1990&ignore=.csv http://real-chart.finance.yahoo.com/table.csv?s=%5EGSPC&d=11&e=1&f=2014&g=d&a=0&b=3&c=1950&ignore=.csv and this ...


3

Create a new price series that has a value for every minute, e.g. by carrying the last observation forward. Then compute returns from this new price series. (There are simpler approaches for this particular case, but I'd prefer the one outlined above as it is conceptually clear.) A sketch in R. (Disclosure: I am the maintainer of packages PMwR, from which ...


2

It could be useful for optimal trading to have accurate estimates of the intraday seasonalities. Seasonalities come from a mix of: rythms (for instance European curves are impacted by US open and news announces) events (news) market design (proximity of fixing auctions) From an estimation viewpoint, you see that the more you can take these effects into ...


2

What is your objective? There are many approaches that can accomplish this in broad terms but whether it is sensible depends on your application. For example if you are interested in intraday breaks in the levels process you can look at OLS with a priori indicator function breaks, or perhaps a univariate Kalman filter with a stochastic slope coefficient ...


2

Using a realized kernel for calculating volatility will give you results in the same resolution as the data you feed them. So if you feed them minute-by-minute data, then the volatility will be calculated minute-by-minute. What that really means is that only once per minute will you have a good estimate of the volatility of whatever asset you're looking at. ...


2

+1 for "feeling like the data is out there to be parsed for free". lol If data is just for toys, do: http://www.dxfeed.com/historical-tick-data/ They offer (free) tick data for May 6 2010 (flash crash). Scrape google. This question: Free intra-day equity data source


2

Stationarity. The distribution of returns is non-stationary. Moreover, standard deviation of returns is not constant over time. Symmetry. The distribution of returns is approximately symmetric with increasing leptokurtosis as sampling frequency increases. However, large drawdowns are not matched with equally large upward movements. Gaussian behavior. Returns ...


2

The answer to your question probably depends on the type of the security you want to query the data from, their vendor (not Bloomberg, the original vendor) and your license with Bloomberg. I don't remember having no access to intraday data, but I remember having limited history for sure (more data implied more fees as far as I can remember). But in general,...


2

How about: Barnhart, Scott W. "The effects of macroeconomic announcements on commodity prices." American Journal of Agricultural Economics 71.2 (1989): 389-403. This article analyzes the immediate reaction of a representative sample of commodity prices and two T-bill yields to the unanticipated components of thirteen macroeconomic announcements. ...


2

Check out Quandl's collection of intraday data from AlgoSeek: https://www.quandl.com/vendors/as These are historical trade-based minute bars showing OHLCV for stocks. If you click on each database in the link above, you'll see a tab for "Free Sample Data" to the left. For example: https://www.quandl.com/data/ASTRAN/documentation/free-sample-data Note ...


1

There are papers about improving *ARCH models by using other estimators than the classic squared returns estimator. Here are some links: In the paper How Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq - 100 Stock Index for example the researches compare different estimators such as ...


1

Have a look at Tick Data. They sell trade (and optionally quote) data for a lot of different instruments including the EUA futures. Their pricing is 125 USD for a one year tick history and you need to order for at least 250 USD at a time. I ordered some Eurex DAX future tick history from them. The order process was flawless and their pricing was ...


1

Tick Data has some sample equity data with bid/ask. First go to the Tick Data equities web site: https://www.tickdata.com/equity-data/ Then find the Sample Data link.


1

I am sure that some people do this. Generally, there is some evidence that informed traders choose to trade in the option markets first (Easley et.al, 1998). This is especially true if an informed trader has bad news about a short-sale constrained stock. In this case the option market leads the equity market. Moreover, I was told that there are some people ...


1

Have a look at the TFX R package.


1

There are various alternatives to your problem: If you think that Normal distribution is not appropriate then you can use other distributions like t-distribution, skewed t-distribution, generalized error distribution, skewed GED etc. All these distributions are available in R (rugarch package) and Eviews too. As you are interesting in studying the impact ...


1

I gather from your question that you are looking for an accurate measure for your portfolio volatility. Keep in mind that the portfolio volatility is not equal to the weighted sum of its components and you have to estimate the correlation / covariance structure of your portfolio components. The volatility part is much easier to estimate than the covariance. ...


1

(I don't have enough community points to comment, but this is not a proper answer) Do you mean implied volatility or realised? If the latter I would suggest using futures prices as each of those indices have futures that are trading live at 12:00 Berlin time. Futures tend to be what practitioners look to for the most up-to-date valuation of an underlying ...


1

If you really just want the charts, you can get this on the TD Ameritrade platform. You do need a funded account, so it is not exactly "free" but there aren't any fees associated with it. In their desktop client, you can select "On Demand" mode. This gives you the ability to rewind to earlier points in history for sim trading purposes. Just rewind back ...


1

It's been a long time but just in case someone else happens on this question, see: DOES ANYTHING BEAT 5-MINUTE RV? A COMPARISON OF REALIZED MEASURES ACROSS MULTIPLE ASSET CLASSES


1

The post is quite old, but an interesting question indeed. Here is how I would go about it: In a regression $y_n = \alpha + \beta x_n + u_n$ the estimator of the slope coefficient is $\beta = \text{cov}(x, y)/\text{std}(y)$ To avoid distractions let's assume for a moment that everything is standardized: means are zero and variances are one. Then, the slope ...


1

This reminds me of a paper by Rama Cont: "Empirical properties of asset returns: stylized facts and statistical issues.". You can download here: http://www.cmap.polytechnique.fr/~rama/papers/empirical.pdf He also has a paper on volatility clustering: "Volatility clustering in financial markets: empirical facts and agent-based models.", which may be of your ...


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