MBI Publications

MBI Publications for Arjun Beri (5)

  • A. Beri, R. Azencott and I. Timofeyev
    Calibration of Stochastic Volatility Model under Indirect Observability of the Volatility Process
    (2012) (In Preparation)

    Abstract

  • R. Azencott , A. Beri, Y. Gadhyan, N. Joseph, C. Lehalle and M. Rowley
    Realtime Market Microstructure Analysis: Online Transaction Cost Analysis
    (2012) (In Preparation)

    Abstract

  • A. Beri, D. Chowdhury and H. Jain
    Agent-based and Macroscopic PDE models for foraging dynamics of competing ant colonies, and associated boundary interactions
    (2012) (In Preparation)

    Abstract

  • R. Azencott , A. Beri, A. Jain and I. Timofeyev
    Sub-sampling and Parametric Estimation for Multiscale Dynamics
    Communications in Mathematical Sciences (2013) (To Appear)

    Abstract

    We study the problem of adequate data sub-sampling for consistent parametric estimation of unobservable stochastic differential equations (SDEs), when the data are generated by multiscale dynamic systems approximating these SDEs in some suitable sense. The challenge is that the approximation accuracy is scale dependent, and degrades at very small temporal scales. Therefore, maximum likelihood parametric estimation yields inconsistent results when the sub-sampling time-step is too small. We use data from three multiscale dynamic systems, the Additive triad, the Truncated Burgers-Hopf models, and the model with the Fast-Oscillating Potential to illustrate this sub-sampling problem. In addition, we also discuss an important practical question of constructing the bias-corrected estimators for a fixed but unknown value of the multiscale parameter.
  • R. Azencott , A. Beri, Y. Gadhyan, N. Joseph, C. Lehalle and M. Rowley
    Realtime market microstructure analysis: online Transaction Cost Analysis
    Quantitative Finance (2013) (Submitted)

    Abstract

    Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts which of the orders are under performing while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method in the post trade analysis of algorithms can be taken advantage of to automatically adjust their trading action.

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