# Tag Info

9

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 ...

8

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 ...

7

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 ...

6

If you have access to intraday data, they are better ways to estimate the bid-ask spread. If you have Open, High, Low and Close price on each 5min bin $b$ (or any other interval): the Close of the previous bin and the Open of this one are consecutive. Hence $dP(b)=C(b-1)-O(b)$ allows to define an estimate $\psi(b)$ of the bid-ask spread \psi(b):=\min_{b:\, ...

5

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 ...

5

There are a wealth of providers out there. Your best bet is to sign up for Alpaca markets, which then gives you a free API key to use Polygon.io - they have 1-minute bar aggregates going back a decade for 11k US securities

4

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 ...

4

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.

4

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 ...

4

If you have an Interactive Brokers account, you can get historical intraday index data, including SPX, through their API. Many developers find using the Interactive Brokers API to be a challenge, especially for collecting large amounts of data. If you want a more turnkey access, you can check out QuantRocket, which provides data collection tools on top of ...

4

There is a good and a bad point in using volume-time in place of calendar-time: on the one hand, you obtain a more "regular" time series but on the other hand, you cannot synchronize two time series this way: what is the the "common volume time" between two stocks? I am more familiar with the intraday aspect of re-timing than with the ...

3

+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

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

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. ...

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 ...

3

Neither. Nasdaq publishes a close price at 4:01:30 p.m. ET known as the Nasdaq Official Close Price ("NOCP"). Nasdaq also amends this price up to 5:15 p.m. ET if any trades that were used to calculate the NOCP are canceled or corrected. You can get NOCP prices directly from Nasdaq each day on their website. Information Links: NOCP PDF Nasdaq ...

3

Often no pre-made dataset exists when you have such specific requirements. Therefore, you have to find your own data by searching for penny-stocks that matches your criteria. This can be done via a stock-screener. Doing a quick google search on "Gap-up stock screener" I found this website, where I sorted by highest percentage gap-up changes. From ...

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

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

https://algoseek.com/ is an option if you are looking for historical intraday stock data in the US market. You can easily download their samples and the dataset you need on their Explore Data page.

2

Have a look at the TFX R package.

2

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 ...

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

For the SPX, First Rate Data - SPX intraday has about 15 years of 1-minute intraday data.

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 ...

2

There is a demo ticks dataset for S&P500: https://github.com/Jackal08/financial-data-structures/blob/master/raw_tick_data/ES_Trades.csv.zip It has 5.5kk entries for 20 days of the year 2013. The another source of intraday data is here: https://www.finam.ru/profile/akcii-usa-bats/google-inc/export/?market=25&em=20590&code=GOOG&apply=0&df=...

2

The main difference between GARCH and realized GARCH models applied on daily or intradily data might be not the time period but rather the data availability and its use. A GARCH model uses very little information, namely, only the observed price or return series. Often it squeezes out quite good results from it. When additional information such as data on ...

2

This is a quantifiable way to infer some understanding of the trade direction under very short time horizons (market microstructure). There exists a couple of other trade direction algorithms, which is neatly described in this paper.

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