The FCI is constructed from the principal components analysis of a set of selected variables , in line with the methodology used by other indicators of similar characteristics, such as the Financial Stress Index prepared by the St. Louis Fed (STLFSI) or Kansas City Fed (KCFSI).
The principal components analysis is a statistical method for extracting the factors responsible for explaining the joint movements of a group of selected variables. After assuming that financial conditions are the main factor that influences the overall movement of the selected variables, by extracting the first principal component, one that best explains this whole movement, an index that has a useful and representative economic interpretation is constructed.
Specifically, the principal components analysis builds a linear transformation picking a new coordinate system for the original dataset, in which the larger variance of the data set is captured by the first principal component. This method reduces the dimensionality of the data set , retaining those characteristics of the data set that contribute most to its variance, in a way that the first components contain the "most important " aspects of that information. Intuitively, this technique allows us to find the causes of variability in a data set and sort them by importance.
So, first, a set of variables reflecting the financial conditions of the economy is selected, so that as the financial conditions change, these variables tend to move together reflecting these changes. After assuming that changes in financial conditions are the most important factor explaining the whole movement of the selection of variables, by the components analysis that factor is identified, using the first principal component. After standardizing the selected variables, each variable is multiplied by its respective coefficient, arising from the first principal component and the FCI is equivalent at each reporting date to the sum of each transformed variable, which then allows decomposing changes in FCI by each variable. That is, you can determine which variable or sub-set of variables contributed most to explain the change in financial conditions at the time. FCI higher values reflect greater joint movement of the group of selected variables and thus indicate a worsening in financial conditions prevailing.
The FCI uses variables and financial and economic data in the public domain and are available on a monthly basis. In order to understand various aspects of financial conditions, the FCI is constructed from two sub-indexes. One of them is intended to capture the financial conditions facing Argentina economy locally, named Local Conditions Index (LCI). The other is intended to capture the financial conditions facing the economy Argentina Externally, named External Conditions Index (ECI ) . The FCI arises from the simple average of these two sub-indexes, each prepared by the method described above. It takes as its starting point the period since the restructuring of public debt in mid- 2005.
The sub-indexes are constructed from a set of variables specially selected to identify the status of financial conditions locally and externally. Thus, there were taken into account variables related to capital markets, the financial system, the degree of dollarization, liquidity and solvency of banks, sovereign risk, among others.
The Local Conditions Index (LCI) considers a set of 10 selected variables: the volatility of Merval, the level of Merval, the spread between the official exchange rate and the blue-chip, the spread between implicit devaluation in NDF futures and the local market, the spread between the growth of deposits in pesos and dollars from the private sector, the growth of total deposits of the private sector, the steepening of the NDF curve, the 7 days Call interbank rate, the Badlar private rate and the spread between the Badlar and Call rate.
The External Conditions Index (ICE) considers a set of 10 selected variables: the yield on 10 year US Treasury bonds, the steepening of the curve for US Treasury bonds, the EMBI Global Composite Index excluding Argentina, the European CDX to five years, the spread between 3-month LIBOR and 3-month OIS (Overnight Indexed Swap), the volatility of the Brazilian real, the spread between the yield on 10 year US Treasury bonds and the performance of US TIPS to 10 years, the TED spread (the spread between three months Libor and the yield on the benchmark three months US Treasury), the VIX (volatility of S&P 500) and the spread between the EMBI Global composite excluding Argentina and the EMBI Global Argentina.
El ICF refleja en forma razonable los diversos períodos de estrés en las condiciones financieras que experimentó la economía argentina en los últimos años. A su vez, los dos subíndices que componen el ICF capturan diferentes efectos entre sí a lo largo del período, enriqueciendo la lectura del ICF a partir de las contribuciones por variable.
The FCI reasonably reflects the various periods of stress in financial conditions experienced by Argentina 's economy in recent years. Moreover, the two sub-indexes that make up the FCI capture different effects to each other throughout the period, enriching the interpretation of the FCI from contributions by variable.
For example, unlike External Conditions Index, the Local Conditions Index reasonably capture the tensions associated with the political crisis in the agricultural sector in early 2008, or conversely, improvements associated with "financial summer" in 2010. Moreover, unlike the Local Conditions Index, External Conditions Index reasonably capture the initial stresses associated with the onset of the sub-prime crisis in mid-2007, or the worsening of the situation in Europe in 2010. Also, both indicators forcefully recognize the worsening financial conditions in late 2008 and during 2009, before the intensification of the international financial crisis and the deterioration of the local economy, the drought that affected the agricultural sector and the nationalization of the pensions system (AFJP’s), among other factors that negatively impacted on the financial conditions at that time.
Finally, the FCI correlates very well with the evolution of economic and financial activity. Indeed, the damage that the FCI showed since 2007, and intensified from mid-2008, anticipated the evolution of economic and financial activity in the period. Likewise, the deterioration in the FCI in late 2011 also corresponds to the deceleration showed by economic and financial activity. Thus, the FCI constitutes as a novel and interesting leading index for economic and financial activity.