Journal of Banking and Finance

The file reports data used for the paper: the risk-ajusted return managed carry trade.

This file contains monthly long-short returns from decile portfolio sorts based on CRO_M and CRO_A. Portfolios are value-weighted and based on NYSE breakpoints.

This database contains information on large corporate and middle market commercial loans filed with the Securities and Exchange Commission, or obtained through other reliable public sources. The Compustat database complements the former database by providing information on borrowers. The fusion of the two databases uses the link table provided by Roberts and Chava (2008). Each contract listed in DealScan, referred to as a deal, consists of one or several facilities or tranches. DealScan provides a unique identification number for each deal allowing the identification of all the tranches belonging to the same deal. Different tranches in a deal can show heterogeneous characteristics in terms of interest rate spreads, amount, currency, maturity, default probability, among others. In particular, not all members of a syndicated loan participate in every tranche of the deal. Our initial information was composed of deals originated in the US market for the period 1986-2013 (inclusive) for which borrower accounting information is available in Compustat. In our empirical analysis, several measures related to the syndicate structure and its previous relationships with the borrower require using the information of the four years prior to each deal active date. This implies that deals from 1986-1989 are only used for this purpose, and therefore the final sample only considers loans issued between 1990 and 2013. Additionally, some loans are excluded from the 1990-2013 period. First, loans to borrowers that are government entities, banks, or financial institutions, and/or regulated borrowers, such as transportation and public utilities (industries identified as SIC 91-99, 60-67 and 40-49). Second, deals where at least one tranche is denominated in currencies other than the US dollar, or for which some basic information is missing, such as the facility amount or tranche interest spread. For the sake of homogeneity, deals with a base rate other than LIBOR were also excluded. After this cleaning process, the final sample consists of 32,102 tranches corresponding to 21,034 deals to 5,206 borrowers over the period 1990-2013. However, of those 32,102 potential observations, a smaller number have complete information for all the variables that we will use in each particular econometric model.

1. Credit risk of a loan with one normal macroeconomic driver. 2. Credit risk of a loan with one lognormal macroeconomic driver. 3. Calculation of worst case scenario with two macroeconomic drivers on EBA data.

Daily currencies, monthly macroeconomic and monetary variables

This is the dataset we used to investigate the simultaneous relationship among bank capital, risk and profitability, taking into account bank ownership and the emergence of Pan-African cross-border banks. The data contains hand-collected bank level data from all the 8 countries of the West African Economic and Monetary Union (WAEMU) countries for the period 2000-2014. The countries are split into lower middle-income (LMICs) and low-income (LICs) according to the World Bank classification. In addition to bank level data, there is data on macroeconomic aggregates for the 8 countries.

Data and code used in the paper

Data for analysis

Extract the .zip-file into one folder (sub-folders for the raw data will be created). Then run the GIMS_DataAnalysis.R for the main results, GIMS_DataAnalysis_Return.R for the RETURN results, GIMS_DataAnalysis_2Assets.R for the EXPERIENCE results, and GIMS_DataAnalysis_2Markets.R for the 2MARKETS results.