This document outlines objectives and critical success factors for a high frequency quantitative modeling and trading project in US stocks. The goals are to achieve optimal time frame modeling to generate revenue. Key factors are availability of tools, liquid stock tick data, and software. The approach is to establish an environment, explore models combining industry and academic approaches, and generate revenue. Examples include Engle models, Bayesian inference, particle filtering, and microstructure applications. Next steps are to set up the environment, explore promising models, and use Engle and other approaches.

1. - Valapet Badri
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2. Objectives
High frequency quantitative modeling and trading
in US stocks(NYSE and NASDAQ Markets)
Achieve this in an optimal time frame and
generate revenue stream for the firm
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3. Critical Success Factors
Availability of tools and information in proper
format
The most liquid stocks tick information would be
available initially as a snapshot
Sufficient infrastructure and Statistical software (S-
plus /Matlab) are available to perform quant.
Analysis.
My goal initially would be to focus on
establishing a basic environment
Minimal amount of time to be spent on pure
IT allied endeavors although they are
important for overall success of the project
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4. Facts & AdvantagesThere are other Hi-frequency hedge funds who have been in
operation for 8+ years and are ahead in terms of experience
and models for quant analysis and trading strategies
There are no standard textbooks for this areas that provide a
guide to success
How to take advantage and address gaps?
Develop High Frequency model that utilizes
recent developments in stock market
Existing time tested quant principles and recent academic
developments
“Get the right mix of industry models and academic models and
generate revenue within optimal time frame”
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5. Example approaches
Models under the framework of Engle (2000) such as
Exponential ACD model, UHF-GARCH and more.
Bayesian Inference via Filtering for Model
 Continuous-time Likelihood, Posterior, and Bayes Factors
 Filtering Equations and Evolution Equations for Bayes
Factors
Two Computational Approaches and their Consistency
 The Markov Chain Approx. Method and nearly likelihood
etc.
 Particle Filtering (or Sequential Monte Carlo)
A Micro movement Model
 Random-arrival-time State Space Model
More Financial and Market Microstructure Applications
(Frequentist approaches – interpolation, averaging, Splines)
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6. Next steps
Establishing a basic environment and acquire necessary
tools and infrastructure(details will be provided)
Explore promising approaches suggested to come up
with a quantitative model and transformation of the tick
level information.
Follow current industry trend and use a mix of Engle(Rob
Engle- Nobel laureate) type of time series analysis and other
approaches to generate revenue
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7. Next steps
Establishing a basic environment and acquire necessary
tools and infrastructure(details will be provided)
Explore promising approaches suggested to come up
with a quantitative model and transformation of the tick
level information.
Follow current industry trend and use a mix of Engle(Rob
Engle- Nobel laureate) type of time series analysis and other
approaches to generate revenue
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