Category Financial Econometrics and Empirical Market Microstructure

Raising Issues About Impact of High Frequency Trading on Market Liquidity

Vladimir Naumenko

Abstract The aim of this paper is to consider some problems with evaluation of the impact of high frequency trading on market liquidity. The first part is devoted to difficulties of disentangling the impact of high frequency on market liquidity from other relevant factors. The remainder of the paper is intended to discuss some issues affecting the evaluation of the influence of high frequency trading on particular aspects of market liquidity.

Keywords Depth • High frequency trading • Market liquidity • Resiliency • Tightness

Over the last years, high-frequency trading (HFT) has become the object of ever – increasing attention on the part of market participants and academics as well as regulators...

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Model-Independent Price Jump Indicators

1. Extreme returns indicator: a price jump occurs at time t if the return at time t is above some threshold. The threshold value can be selected by two ways: it can be selected globally—one threshold value for the entire sample, for example, when the threshold is a given centile of the distribution of returns over the entire data set. Or, it can be selected locally, and consequently, some sub-samples may have different threshold values. A global definition of the threshold allows to compare the behavior of returns over the entire sample, however, the distribution of returns can vary, e. g...

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Backtesting StressGrades

Below we show several are early warning backtesting case studies on ETF’s representing major asset classes. We calibrated DStress for each ETF based on the largest daily drawdown dates (e. g., —9.6 % for SPY on December 1, 2008). If StressGrades are predictive, we would expect an escalation in PStress and decline and DStress as we approach the drawdown date (e. g., December 1 for SPY). In other
words, we would expect volatility to be high and rising before the peak endogenous stress events.

image216Again, for simplicity we use the Normal Distribution to calculate PStress in the case studies below.

Given that StressGrades are driven by volatility, we expect StressGrades to fail in predicting Black Swans, but to help in detecting Dragon Kings.

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Sample Selection Bias in Mortgage Market Credit Risk Modeling

Agatha Lozinskaia

Abstract The mortgage crisis that started in the U. S. in 2007 and lasted until 2009 was characterized by an unusually large number of defaults on the subprime mortgage market. As a result, it developed into a global economic recession and placed the stability of the world banking system in jeopardy. Therefore, the issues of credit risk modeling showed the shortcomings of the current credit risk practice. Truncation, or partial observability, and simultaneous equations bias causes sample selection bias. As a result, parameter estimates are biased and inconsistent. Firstly, we provide an overview of current approaches in the mortgage literature to control for the sample selection bias correction, such as the Heckman model and bivariate probit model with selection...

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Revisiting of Empirical Zero Intelligence Models

Vyacheslav Arbuzov

Abstract This paper describes a zero-intelligence approach implementation for the modeling of financial markets. We construct a mechanism of order flow and market engine simulation. We analyze stylized facts to estimate the quality of our models. The research is based on a 1 month order and execution history data of the Moscow Exchange (MOEX) for one stock (JSC “Aeroflot”).

Keywords Daniels model • Market microstructure • Mike-Farmer model • Order flow • Stylized facts • Tail exponent • Zero-intelligence models

JEL Classification G15, G17

1 Introduction

Agent-based models play an important role in understanding the mechanisms of financial markets driven by the advances in technologies that allow the creation and calibration of complex and very detailed mo...

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Probability of Default Models

Here, and later in the paper, the default is understood as one of the following signals for its registration:

• A bank’s capital sufficiency level falls below 2 %.

• The value of a bank’s internal resources drops lower than the minimum estab­lished at the date of registration.

• A bank fails to reconcile the size of the charter capital and the amount of internal resources.

• A bank is unable to satisfy the creditors’ claims or make compulsory payments.

• A bank is subject to sanitation by the Deposit Insurance Agency or another bank.

We propose a forecast probability of default (PD) model, which is based on the relationship between banks’ default rates and public information...

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