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
The lead paper in the January 2011 Journal of Finance (Hendershott, Jones, and Menkveld) addresses algorithmic trading (AT). In short, they find that AT improves liquidity as measured by bid-offer spreads. Taking the econometrics as correct (it is in the Journal of Finance) the next question is if bid-offer spreads are a sufficient statistic for measuring ...
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 ...
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.
20
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:
...
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 ...
17
The term has a different meaning to different people.
to econometricians, microstructure noise is a disturbance that makes high frequency estimates of some parameters (e.g. realized volatility) very unstable. Generally this strand of the literature professes agnosticism as to the its origin;
to market microstructure researchers, microstructure noise is a ...
17
Some cynical but functional definitions:
It's what you can't model if you're not using tick by tick data
It's what proper quant pricing theory doesn't know how to model yet
It's information (order book behavior) that reflects momentary fluctuations in the supply/demand of a given contract, rather than its underlying value (eg an arbitrage free price)
The ...
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
14
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-...
14
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 ...
13
There are rigorous econometric definitions, as has already been eluded to by others. For practical purposes, microstructure noise is a component of a price process that exhibits mean reversion on some (possibly time-varying) frequency.
This reversion is particularly attractive to liquidity provisioners, who seek to profit from this noise component (along ...
13
This answer is my ongoing attempt to consolidate some recent commentary on this hot topic.
A good place to start for anyone thinking about this question is the Economists's Buttonwood: Not So Fast, which mentions recent research by Biais and Woolley (2011) and Dichev, Huang, and Zhou (2011).
Does Algorithmic Trading Improve Liquidity? This paper claims yes....
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
Here's a blog post with a general overview of some possible implementations.
howtohft_howtobuildafastlimitorderbook - (mirror of the original posting)
The posting was originally on the website www.quantcup.org - this site is up for sale but I leave the broken URL to help future searchers:
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
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 don't know if you can really improve, the point of Market Making is that you don't know when you'll be executed.
It also depends a lot on the type of product you're trading, it's not the same business Market Making far from the money options (where you will never be executed but just offer a reference price and answer traders phone calls) and MM on Bonds/...
9
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 ...
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 ...
8
The defining characteristic of "high-frequency" is not the number of trades, but instead it is the number of orders you place, and in particular how often you are changing those orders. The scratch rate (cancel/fill ratio) is often very high. For every 1,000 orders you place, you might get 5 fills. This is the single most defining criteria of whether someone ...
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
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 ...
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 ...
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