23
In fact you have three papers available to go further:
The Avellaneda-Stoikov one, with proper model and an approximate solution
The Bayraktar-Ludvkosli one, with a solution for the linear utility function
The L-Guéant-Fernandez one, with a full solution for a generic utility function
I prefer the last one ;{)}
22
My two favorites books on microstructure are:
Barry Johnson's Algorithmic Trading and DMA - very good on technological aspects and for an overview of needed implementations;
L and Laruelle's Market Microstructure in Practice - for common knowledge and understanding of market microstructure and its mechanisms.
Recently, three new books have been published:
...
21
Since Quant Cup 1's objective was an efficient price/time matching engine, the data structure of the winning implementation might partly be what you are looking for.
Else the setup of LOBSTER is supposed to be quick.
21
It seems that your question refers to the microstructure noise defined in papers about intraday volatility estimates.
Originally, it comes from the bid-ask bounce, i.e. the fact that even if the volatility is zero, you have buyers and sellers at this price and consequently you observe prices at Bid or Ask prices, and not at mid-price. Because of that, if ...
19
The main issue measuring intraday volatility is called "signature plot": when you zoom in, the volatility measure (i.e. empirical quadratic variations) explode.
Similarly you have the "Epps effect" for correlations: when you zoom in, the correlations collapse (it is at least a mechanical effect).
For the volatility a lot of models can correct this:
- first ...
16
The specifics depend on if you're implementing for equities (order-based) or futures (level-based). I recommend https://web.archive.org/web/20110219163448/http://howtohft.wordpress.com/2011/02/15/how-to-build-a-fast-limit-order-book/ for a general overview of a good architecture for the former.
Building off of that, though, I have found that using array-...
16
Your question is very vague (e.g. what are you trying to measure, and what "tick data" do you have), but I'll give you some pointers:
In general, when people consider how prices evolve, they will tend to think about things like volatility and correlation dynamics. So I would start by defining exactly what you want to measure. The irregularity of time ...
14
The expression you have is fine. But more generally, for the intraday volatility, I don't think there "the correct definition". More like, whatever works in the given context. I found the following notes by Almgren pretty useful:
http://cims.nyu.edu/~almgren/timeseries/notes7.pdf
12
"quote spam", "book colouring", "quote stuffing", etc encompass any mechanism to modify the shape of the orderbook by a market participant who does not intend to really buy or sell shares thanks to these orders.
It means that someone fills the bid side of the book with 10,000 shares at different levels of price and does not want to buy at all, or only 100 ...
11
I've not yet read it, but Lehalle's recent book is bound to be a goldmine of good micro-structure bits and pieces. Market Microstructure in Practice
EDIT: I'm reading the book now, so far it's quite good.
11
Among matching rule, do not forget "auction calls", in most markets, you have one at the open and one at the close.
To give you the main reasons to use one matching engine rather than another:
Auction calls (i.e. fixings) are good to digest a lot of orders in a very short amount of time. It is why after a trading suspension, the trading starts with an ...
11
A public order book gives traders information not only on the current price of a security, but also the volume and structure of the entire supply and demand schedule.
Such information can be used for arbitrage and market manipulation strategies in various ways:
Spoofing: Inserting a large limit order as an apparent buy or sell signal which is canceled any ...
10
Joel Hasbrouck (imho, a leading expert in market microstructure) has a paper on this:
http://people.stern.nyu.edu/jhasbrou/Research/Working%20Papers/HS10-11-10.pdf
From the abstract:
Our conclusion is that increased low-latency activity improves traditional market quality measures such as short-term volatility, spreads, and displayed depth in the limit ...
10
This paper Dealing with the Inventory Risk. A solution to the market making problem, has a full bibliography and explains the intra day market making mechanism.
The model is made of two components:
a diffusion of the fair price (to model the market risk)
a point process (with an intensity in $A \exp -k \delta$ (where $\delta$ is the distance to the fair ...
10
I am not sure Dark Pools (DP) have been created to avoid "market manipulation". They have been created by firms because they found an advantage to create them (see Market Microstructure in Practice, L and Laruelle Eds.). The main reasons have been:
spare market fees, for DP created by brokers (like UBS MTF);
spare market impact, for block pools (like ITG/...
9
I recommend reading Cao, Hansch, and Wang (2004) "The Informational Content of an Open Limit Order Book". They present a simple model for an order-book price called the weighted price ($\mbox{WP}$):
$$
\mbox{WP}^{n_1 - n_2} = \frac{\sum_{j=n_1}^{n_2} (Q_j^d P_j^d + Q_j^s P_j^s)}{(Q_j^d + Q_j^s)}
$$
Where:
$n$ is the order book level
$Q_j$ is the size at ...
9
The best paper is probably Relative Volume as a Doubly Stochastic Binomial Point Process - James Mcculloch.
In this paper the volume is modelled via a Point Process, and theoretical laws are derived (with confident intervals, etc).
And we put elements about this in Market Microstructure in Practice, Chap 2.1.
Volume curves are analyzed, not only during the ...
9
Classical book on market microstructure is: Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris. It's a bit outdated (2002) and missing few recent market developments like dark pools etc. but the way it currently is it's already highly recommended reading.
Personally I'm waiting for the next edition of the same book, and surely ...
9
If you want to learn more about price pressure, you should look after market impact of metaorders, which is a more adequate term.
Because of the microstructure (i.e. the mix of orderbboks dynamics, trading rules, participants behaviours and habits, etc), the more you buy or sell, the more you influence the price an unfavorable way (for your trades).
Just ...
9
This is a very difficult question.
First of all you should read Almgren's slides on the topic: Using a Simulator to Develop Execution Algorithms.
First you need to backtest your strategy against a "replayer". Ok it is not perfect, but it gives you information anyway. Provided you add some "sanity limitation" to this simulator (i.e. do not allow you ...
8
There are several. This list is from Giyenko et al (2008)---in their work they compare all these different measures--- and includes spread proxies and price impact proxies. As for spread proxies:
"Effective Tick" (Holden 2007, Giyenko et al 2008)
"Holden measure" (Holden 2007)
"LOT Y-split" (Giyenko et al 2008)
"Roll measure" (Roll 1984)
"Gibbs measure" (...
8
The best overview I have seen so far is this paper which lists 214 (!) factors (or anomalies if you like) on over one hundred (!) pages:
Harvey, Campbell R. and Liu, Yan and Zhu, Caroline, …and the Cross-Section of Expected Returns (February 3, 2015). Available at SSRN: https://ssrn.com/abstract=2249314 or http://dx.doi.org/10.2139/ssrn.2249314
Abstract: ...
8
I found this power point and this paper to be an excellent source on this topic.
Here is a quote from the paper:
A square-root singularity for small traded volumes is highly
non-trivial, and certainly not accounted for in Kyle’s classical model
of impact [11], which predicts a linear impact ∆ ∝ Q. A concave impact
function is often thought of as a ...
7
Your first definition is wrong; I'm not sure where you got that from. Your second definition is correct: the ISO alerts the exchange that the submitting party has taken responsibility for RegNMS and requests a fill at only that venue's price; there is no routing away. Obviously, there is a huge red-tape burden to get permission to do this.
7
Very interesting question. I am not an expert on the subject, however, I was able to find a collection of papers on the subject that should get you started.
Here is a good and very informative paper that walks you through several tick by tick volatility estimators that seek to reduce the volatility imposed by market micro-structure:
Efficient estimation of ...
7
I think it's alive and well. I don't think there's a specific "decoupling" time, but if you look at e.g. Munnix et al. "Statistical causes for the Epps effect in microstructure noise", it seems that the biased correlation is about 60% of the real value for 1 min data and about 90% for 5 min data, so you could say that 5 min is pretty safe, but 1 min is ...
7
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 ...
7
The most commonly-known approach to this is described in Inferring trade direction from intraday data (1991) by Lee and Ready. You will find that the non-trivial part has to do with classifying trades that are reported inside the spread. I believe you will find that the Lee-Ready algorithm will outperform the naive midpoint reference approach suggested by @...
7
The Queue Reactive Model (by Huang, L and Rosenbaum) is an improvement of what Cont and de Larrard (CL) did.
This model is capturing
the inflows and outflows in each queue given the current state of the orderbook (it is one of your remark)
but more importantly, once one queue depletes, the discovered quantity is not taken at random (like in the CL model)
...
6
Statistical volatility is the standard deviation of a window of log returns. For example, 30-day statistical volatility is the standard deviation of 30, one-day log returns. The log return comes from the assumption that log stock returns are normally distributed.
Statistical volatility differs from implied volatility which is the volatility input to some ...
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