Marcelo Perlin

MS_Regress - The MATLAB Package for Markov Regime Switching Models

Markov state switching models are a type of specification which allows for the transition of states as an intrinsic property of the econometric model. Such type of statistical representations are well known and utilized in different problems in the field of economics and finance. This paper gives an overview of MS_Regress, a Matlab toolbox specially designed for the estimation, simulation and forecasting of a general markov regime switching model. The package was written in an intuitive manner so that the user have at its reach a large number of different markov switching specifications, without any change in the original code. This document introduces the main functionality of the package with the help of several empirical examples.

The Brazilian scientific output published in journals: A study based on a large CV database

We assemble a massive sample of 180,000 CVs of Brazilian academic researchers of all disciplines from the Lattes platform. From the CVs we gather information on key variables related to the researchers and their publications. We find males are more productive in terms of quantity of publications, but the effect of gender in terms of research impact is mixed for individual groups of subject areas. Holding a PhD from abroad increases the chance for a researcher to publish in journals of higher impact, whereas domestic PhDs publish more articles, but in journals of less impact. Thus, there is a trade-off between quantity and research impact. We also find that the more years a researcher takes to finish his or her doctorate, the more likely he or she will publish less thereafter, although in outlets of higher impact. The data also support the existence of an inverted U-shaped function relating research age and productivity.

System-Wide Liquidity Risk in the United Kingdom’s Large-Value Payment System: An Empirical Analysis

When settling their own liabilities and those of their clients, settlement banks rely on incoming payments to fund a part of their outgoing payments. We investigate their behaviour in CHAPS, the United Kingdom’s large-value payment system. Our estimates suggest that in normal times, banks increase their payment outflows when their liquidity is above target and immediately following the receipt of payments. We use these estimates to determine the robustness of this payment system to two hypothetical behavioural changes. In the first, a single bank stops sending payments, perhaps because of an operational problem. In the second, it pays out exactly what it previously received, relying exclusively on the liquidity provided by other system members. Using the observed uncertainty around our estimated behavioural equations, we derive probabilistic statements about the time at which the bank’s counterparties would run out of liquidity if they followed their estimated normal-time behaviour.

GetHFData: A R Package for Downloading and Aggregating High Frequency Trading Data from Bovespa

This paper introduces GetHFData, a R package for downloading, importing and aggregating high frequency trading data from the Brazilian financial market. Based on a set of user choices, the package GetHFData will download the required files directly from Bovespa’s ftp site and aggregate the financial data. The main objective of the publication of this software is to facilitate the computational effort related to research based on this large financial dataset and also to increase the reproducibility of studies by setting a replicable standard for data acquisition and processing. In this paper we present the available functions of the software, a brief description of the Brazilian market and several reproducible examples of usage.

Accessing Financial Reports and Corporate Events with GetDFPdata

This paper presents and discusses the contributions and usage of GetDFPData, which is an open and free software for accessing corporate data from the Brazilian financial exchange, B3. The distribution and popularization of an open-source algorithm for gathering and managing financial data can improve finance research and practice in two ways. First, it increases the number and quality of research in accounting and corporate finance. Secondly, it provides retail investors with reliable data that may help their allocation decisions. Initially, we analyze the use of this kind of data in a list of recent publications to show the relevance of financial reports and corporate events data for research in the fields of accounting and finance. Finally, we illustrate the use of GetDFPData in large-scale research, an empirical and reproducible example of a corporate finance study.

Lotka’s law for the Brazilian scientific output published in journals

Lotka’s law is a power law for the frequency of scholarly publications. We show that Lotka’s law cannot be dismissed after considering a massive sample of the number of publications of Brazilian researchers in journals listed on the SCImago Journal Rank and the Journal Citation Reports. For the SCImago Journal Rank, we found a power law with the Pareto exponent of 0.4 beyond the threshold of 50 papers. This means computing the ‘average number of publications’ of either a researcher or a discipline is of no practical significance.

Mispricing in the Odd Lots Market in Brazil

We study the case of mispricing in the odd lots equity market in Brazil. Contrary to expectation, odd lot investors are paying higher prices than round lot investors. The pricing difference between markets is affected by market returns, volatility and spreads. Our main hypothesis is that; once the assets traded in the odd lot market are more illiquid than their counterparts, the mispricing is driven by liquidity factors. We propose regulators to review the market design for odd lots in Brazil. We argue that reducing the minimal trading unit in the round lots market would benefit investors.

A Consumer Credit Risk Structural Model Based on Affordability: Balance at Risk

This paper introduces an approach designed for personal credit risk. We define a structural model related to the financial balance of an individual, allowing for cashflow seasonality and deterministic trends in the process. This formulation is best suited for short-term loans. Using this model, we develop risk measures associated with the probability of default conditional on time. We illustrate empirical applications by estimating an empirical model using simulated data and, on the basis of this model, find yield rate and maturity values that maximize the expected profit from a short-term debt contract.

Estimating the Intensity of News Based on Trade Data

This paper investigates the problem of identifying the strength of the incoming of news in the financial market. With the support of a microstructure model we are able to derive a simple formula that, based only on trade data, estimates the likelihood of having news for any given tradable asset in a particular time period. The formula can be easily implemented and takes just one input, the probability of a zero trade price difference conditional on the incoming of consecutive same sign trades. In the empirical part of the paper we investigate the properties of this proposed estimator of news intensity for twenty stocks from the Brazilian equity market, covering two full years from 2010 to 2012. The results are very encouraging and consistent across assets. First we find that the strength of news have a common component across all assets. We attribute that to the fact that the incoming of new information regarding the Brazilian economy will affect all stocks. We also see that the likelihood of news is strongly and positively related to volatility of price differences and negatively related to trade volume. The first can be explained by the fact that volatility is a bi-product of news and the second by the presence of traders avoiding the disclosure of private information by trading smaller volumes. In the empirical section we are also able to show that the intensity of news has a intraday pattern, with higher values at the beginning of the trading day and lower values at the end. This result is consistent with the view that the beginning of the trading day is the time when accumulated overnight information reaches the market, therefore increasing news intensity.

An Empirical Model for Spillover Effects in Price Discovery with Application to the European Bond Market

This paper examines spillover effects caused when market participants trade different financial instruments in a single operation. We develop and test an extended model for cross-correlation in the trading processes of different assets on the European bond market. We find a significant dependency of mid-quote changes and the order flow of the bonds with respect to the market as whole. We also show that trading intensity in the market affects the dynamics of the order flow of particular fixed income instruments. More precisely, a buy (sell) order is less likely to be followed by a buy (sell) order if the market is trading intensively. We explain this effect as an inventory problem where the volatility of prices forces market makers to improve trades in the opposite direction from the current order flow.