5 Los traductores latinos

The literature on the risk-taking channel is limited, due to the challenges con- cerning the identification strategy of the link between monetary policy stance and risk taking by banks. One of these challenges is identifying the exogenous changes in monetary policy. This is because, monetary stance is endogenous in banks’ risk equation, and therefore it is difficult to identify an orthogonal shock for monetary policy. Another challenge is related to the availability of data. Assessing the link between banks’ risk taking and monetary policy requires examining the supply side of credit, as this is what reflects the appetite for more risk by banks, not the change in demand for credit and the quality of borrowers in periods of low interest rate. However, only detailed bank level data can disentangle the effect of low interest rate on credit supply from its effect on credit demand. Even if this data is available, in general it will be limited to a certain types of banks’ activities. For example, the Survey of Terms of Business Lending in the U.S. gives detailed information only of new commercial and industrial loans.

Empirical literature examines the risk-taking channel by looking either directly at how banks, during low interest rate periods, extend their supply of loans to riskier borrowers, or by linking the risk taking behaviour of banks to the loan rate charged to the risky borrowers compared to the rate charged to the less risky ones. Both approaches are conditional on banks’ specific characteristics and the state of the

economy. In particular,Jim´enez et al. (2014) identify four hypotheses by which the

link between monetary policy and risk taking can/should be addressed.

The first one is by identifying if banks change their lending standards when mon- etary policy changes. For example, engaging with borrowers who are in the past considered to be risky but now worth engaging with as their net worth has increased in a low interest rate period. A second method is to assess if banks will change the way by which they price new loans in a low interest rate period; this will make

ex-post risky borrowers also worth engaging with. The third method looks at the collateral values of the borrowers. Since, after a period of low interest rates, a contractionary monetary policy will devalue the net worth of the outstanding risky borrowers. Finally, the link between monetary policy and risk taking by banks can be examined by assessing the quality of loans in banks’ loan portfolios. Hetero- geneity is an important factor in the analysis, as the risk-taking channel will not operate in a similar way across different banks, different banking systems, and time. The empirical examination of the first two hypotheses require access to confidential data, whereas the assessment of the last two hypothesis can be carried out using bank level data which are publicly available in most countries.

To test the first hypothesis,Jim´enez et al.(2014) use confidential loan-level data

from the Spanish Credit Register covering the period from 1988 to 2006. They examined the risk-taking channel by measuring banks’ credit supply expansion in Spain in response to a change in monetary policy stance. Their results show that lower overnight interest rates induces less capitalized banks to grant more loan applications to ex-ante risky firms. The results indicate that these banks also commit to larger loan volumes with fewer collateral requirements to firms which have a

higher ex-post likelihood of default. Jim´enez et al. (2014) argue that the values of

collateral and the search for yield will be higher in the medium run than in the short- run. Furthermore, in the short-run the reduction of interest rates will reduce the burden on existing borrowers, and generally in the short-run the volume of existing

loans is larger than the volume of new loans. Jim´enez et al. (2014) also indicate

that the impact of monetary policy on banks’ risk taking is not symmetric amongst all banks. Small, liquid and weakly capitalised banks take on more risk when the

interest rate is low, as liquid assets hold a high cost with low return.8

To control for endogeneity of monetary policy and to address the first two hy-

8_{InJim´enez et al.(2014) paper, the problem of identifying the exogenous changes in monetary}

policy didn’t arise as monetary policies in Spain were set in Frankfurt up until 1999 and within the Euro-system afterwards.

potheses discussed above,Ioannidou et al.(2015) examined the impact of the federal funds rate on the riskiness (banks’ credit supply expansion) and pricing (the change in interest rate charges) of new loans granted in Bolivia between 1999 and 2003. The U.S. monetary policy will affect the Bolivian economy as the Bolivian peso during that period was pegged to the U.S. dollar, and hence a good indicator of

an exogenous monetary policy will be the U.S. federal fund rate. Ioannidou et al.

(2015) reported evidence that initiating loans with a sub-prime credit rating or loans

to riskier borrowers with current or past non-performance become more likely when the federal funds rate is low. What is more, the results confirm that banks’ risk perception also changes when the interest rate decreases, as Bolivian banks reduce the loans rate charged to risky borrowers compared to less risky ones. In the context

of banks’ characteristics, the results were similar toJim´enez et al.(2014) regarding

liquidity, size and capital. However, Ioannidou et al.(2015) have added that banks

with low funding from foreign financial institutions take on more risk due to the lack of external monitoring.

Unlike the previous two papers, Altunbas et al.(2014) used a market based risk

measure to examine the link between bank risk taking and monetary policy on a sample of 643 banks in Europe and U.S. from 1998 to 2008. Banks’ risk is estimated by the Expected Default Frequency (EDF) which is supplied by Moody’s KMV. Their results confirm the existence of the risk-taking channel, and their findings are

in line withJim´enez et al. (2014), in the sense that banks tend to grant more risky

loans only in the medium run. However, in the context of banks’ characteristics,

their findings are contradictory to those of Jim´enez et al. (2014) and Ioannidou et

al.(2015). They find that liquid and well-capitalized banks are less tolerant towards

new risk than illiquid and less capitalised banks. These contradictory results could be due to country specific characteristics.

The Federal Reserves Survey of Terms of Business Lending questionnaire asks banks to rate the risk of new loans to businesses based on a number of borrowers’

related factors.9 Buch et al. (2014a) used these factors to classify new loans into different risk categories and assess the riskiness of banks’ loan portfolios by examin- ing shifts across risk categories. They examine how shocks to commercial property

prices and monetary policy affect the riskiness of new loans using a FAVAR model.10

Their results indicate that there is no evidence of increased risk taking at the ag- gregate level of the banking system after an expansionary monetary policy shocks or an unexpected increase in housing prices. However, they argue that there are important differences across banking groups at the bank level. In particular, they show that bank risk increases for small domestic banks while it declines for foreign banks and remains unchanged for large domestic banks.

Similar to the approach used by Buch et al. (2014a), Maddaloni and Peydr´o

(2011) also rely on answers from banks to the Bank Lending Survey for Europe and

the Senior Loan Officer Survey for the U.S.. Both of these surveys request banks to provide qualitative answers (no figures are required) on the lending standards they apply to customers (supply of credit) and on the loan demand they receive

(demand of credit). Maddaloni and Peydr´o (2011) use these information alongside

securitisation activity and banking supervision standards to assess the impact of changes in the short-term and long-term interest rates on lending standards for both businesses and households. They find that banks’ risk tolerance increases when the short term interest rate is low, and this increase is amplified in high securitisation activity and a weak supervision environment. However, these findings do not hold for long-term interest rate.

Buch et al. (2014b) examine the link between banks and the macro-economy for the U.S. using bank level data from the Call Reports. Data for more than 1500 commercial banks and major macroeconomics variables are used in a FAVAR

9_{These factors are: customer’s credit history; the health of the customer’s cash flow; credit}

rating; access to alternative sources of finance at favourable terms; management quality; collateral’s value and liquidity; and quality of the guarantor.

to analyse the dynamic impact of an identified orthogonal macroeconomic shocks (supply; demand; monetary policy; and house price) on bank’s variables. They mea- sured banks’ level of risk using two different measures. The first one is the share of nonperforming loans which is a backward-looking measure and gives an overall indication of the quality of credit stock. The share of non-interest income in bank’s total income is used as a forward-looking measure of risk since it gives an indication on how volatile the income of the bank is. They find that the backward-looking measure of risk tends to decline after expansionary macroeconomic (including mon- etary) shocks, which is contradictory to the results found in the papers discussed above. However, the forward-looking measure of bank risk increases after expan-

sionary monetary policy shocks. Moreover, Buch et al. (2014b) findings indicate

that a number of factors explains heterogeneity in individual bank’s responses to

macroeconomic shocks. Specifically Buch et al. (2014b, p. 716) argue that “Bank

size, capitalization, liquidity, risk, and the exposure to real estate and consumer loans matter for risk and lending responses of individual banks to monetary policy and house price shocks”.

De Graeve, Kick, and Koetter (2008) use a combination of micro and macro data in a hazard model for bank stress and VAR model to examine the relation between monetary and financial stability. Examining the largest European economy,

Germany, and data from 1995 to 2004, they find similar results toBuch et al.(2014a).

In particular, they show that the average probability of bank stress increases after one year of an unexpected tightening of monetary policy. This response is not the same across all banks, as the response of small and not well capitalised banks is stronger than publicly owned banks.

The literature on the risk-taking channel of monetary policy is large and is

not limited to the banking sector. For example, Hau and Lai (2016) examine the

impact of a decrease in the real short-term interest rate, in eight European countries, relative to the European Central Bank (ECB) monetary policy on the investor asset

allocation process in the mutual fund industry. They found that loose monetary policy encourage investors to shift their portfolio investments out of money market

funds and into the riskier equity market funds.11

Di Maggio and Kacperczyk (2017) also study the impact of low interest rates on the reach for yield behaviour in the mutual funds industry. Specifically, they examine the response of money market funds to the low interest rate environment using weekly data on the universe of U.S. prime funds and found that these funds take on greater risk by investing in longer-maturity and riskier asset classes, even though they are designed to hold only safe, short-term assets.

Pension funds responses to changes in monetary policy are also examined in the risk-taking channel literature. Pension funds managers have the temptation to take on higher risk to reach for yield in order to avoid making larger contribution.

Chodorow-Reich(2014) finds that pension funds with worse funding status or shorter liability reached for higher returns during the period 2009 to 2011, but not after that period. This increase in risk taking is necessary since funds with shorter liability

have less time to make up any funding short. Similarly,Joyce, Liu, and Tonks(2017)

examined the impact of quantitative easing (QE) policies on the portfolio allocation decisions of large institutional investors, specifically UK insurance companies and pension funds. They found that these institutions shift their portfolios away from government bonds towards corporate bonds, increase in risk taking, in response to the Bank of England asset purchases.

The empirical studies, related to the banking sector, discussed above have ap- plied various models to investigate the risk-taking channel without simultaneously considering how the increase in risk in the financial system will spread. Moreover, the increase in banks’ risk might be associated with many channels, not only the

11_{Money market funds are mutual funds that considered relatively safe as this type of funds}

invest only in short-term debt securities like U.S. Government Treasury products. Whereas equity funds are also a mutual fund, but one that primarily invests in stocks.

channel of monetary policy. In particular, the increase in risk might be due to a change in the level of activities of the economy or, more importantly, this increase might be as a result of a sector shock or a shock in a bank or a group of banks. Analysing all these points simultaneously require an advanced and comprehensive model with the possibility of including a large set of variables of different institutions. While the FAVAR can consider the empirical content of a large set of variables by means of principal components analysis, the economic interpretation and the iden- tification of the factors is problematic. Furthermore, even though the FAVAR can accommodate a large set of variables for a single institution or country, its ability to link these institutions or countries in a global setting is not clear.

Moreover, the empirical studies discussed in the previous section have not con- sidered how the increase in risk in the financial system will spread. The risk of contagion within the financial system became more pronounced in recent years as a result of the increased financial integration and inter-linkages.

Therefore, to provide a detailed analysis to the risk-taking channel, and to ad- dress the impact of monetary policy and macroeconomic shocks on bank’s risk while accounting for possible spillover and feedback effects, a coherent global model that includes a large set of variables from many institutions is required. This chapter aims to model a number of bank specific variables over a period of time and across a number of banks, by applying the Global Vector Auto-regression (GVAR) modelling

approach. This modelling approach is first proposed by Pesaran et al. (2004) and