BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//talks.cam.ac.uk//v3//EN
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:19700329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:19701025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
CATEGORIES:CMS Seminars from business and industry
SUMMARY:Statistical classifiers of RFQ acceptance rates in
  FX electronic market making - A. Abutaliev\, R. T
 ank and T. Brooks\, Barclays Bank
DTSTART;TZID=Europe/London:20240306T140000
DTEND;TZID=Europe/London:20240306T150000
UID:TALK212344AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/212344
DESCRIPTION:In foreign exchange (FX) markets\, participants ca
 n execute transactions in a variety of different w
 ays.  One particularly prominent protocol is refer
 red to as an ‘RFQ’ or Request for Quote.\n\nIn thi
 s scenario\, a customer asks for a price in a curr
 ency pair and notional\, and a dealer responds wit
 h a quote\, which is either accepted (a trade happ
 ens) or declined (no trade happens) by a customer.
  The empirical frequency of acceptance is referred
  to as ‘RFQ hit ratio’\, and is widely recognised 
 as a key performance indicator of any electronic F
 X business operation. It is therefore essential to
  understand what factors may influence RFQ accepta
 nce\, to what extent and how they do so. \nElectro
 nic markets generate large volumes of high frequen
 cy data which can be used to help tackle this prob
 lem\, meaning that statistical leaning techniques 
 naturally present themselves as promising tools. T
 he nature of RFQ workflow in particular\, with its
  binary outcome for a given customer request and d
 ealer response\, make it amenable for analysis in 
 a classification setting. This setting has been ex
 tensively studied over many decades by scholars an
 d practitioners alike.\nWe look at theoretical and
  practical aspects of building a statistical class
 ifier of FX RFQ acceptance\, with a particular emp
 hasis on out-of-sample predictive ability by featu
 re type and fitting method.\n
LOCATION:MR4\, Centre for Mathematical Sciences
CONTACT:Stephanie North
END:VEVENT
END:VCALENDAR