MEASURING UNCERTAINTY IN THE FINANCIAL SECTOR

Aytaç Erdoğan and Timur Hülagü1

Statistics Department, Central Bank of the Republic of Turkey

Abstract

In this study, we provide a measure of uncertainty for financial institutions in Turkey by using

a panel data set from the Financial Services Survey. In particular, assuming that higher ex-

ante uncertainty leads to higher errors when predicting the future, we construct a measure

derived from expectation errors of financial institutions by comparing their survey responses

about expectations and realizations on their turnover. Results show that our uncertainty

measure increases significantly in some certain circumstances such as Fed's decision to

decrease monthly bond purchases in May 2013 and recent political turmoil in Turkey.

JEL classification: D8, G2, C83 Keywords: Uncertainty, expectation errors, financial services survey data

1. Introduction

Uncertainty about how the economy will evolve is a key concern for agents in the

economy. Households and firms take actions according to their views on how likely it is that

the economy will be growing, stagnating, or in recession. Consequently, how they respond to

uncertainty has implications for economic activity. From the policymakers’ perspective,

uncertainty matters in several ways. Higher uncertainty about economic activity may defer

investment plans of firms, which in turn slows down the economy. Uncertainty about prices,

on the other hand, may decrease credibility of authorities which use inflation as an anchor.

Because of these, central banks follow closely the developments in uncertainty. For example,

in a quarterly inflation report (May 2014) prepared by the Bank of England, the word

“uncertainty” was used 47 times. However, economists mostly refer the volatility of a variable

as uncertainty, which might not always be a good proxy. Due to the lack of a proper

1 Address: İstiklal Cad. No. 10 Ulus, Ankara, Turkey. Corresponding email address: aytac.erdogan@tcmb.gov.tr.

The views expressed in this paper are those of the authors’ and do not necessarily reflect the official view of the Central Bank of the Republic of Turkey.

measure, this paper studies measuring uncertainty in the financial sector, using expectation

errors of managers.

A large and growing body of literature has concerned itself with the question of what

is the impact of time-varying business uncertainty on economic activity and what role does it

play in macroeconomic fluctuations.2 Guiso and Parigi (1999) show that uncertainty

decreases investment and slows down capital accumulation. Later, Bloom, Bond and van

Reenen (2007) empirically show that higher uncertainty leads to a weaker response to

demand shocks. The seminal contribution in Bloom (2009) formally shows in a partial

equilibrium model that firms employ “wait-and-see” strategies under increased uncertainty. In

a related paper, Bloom, Floetotto and Jaimovich (2009) show more evidence using data from

establishments, firms, industries and macroeconomic variables that uncertainty is

countercyclical. They then build on their theoretical general equilibrium model to study the

effects of uncertainty on economic activity.

In spite of its importance, measuring uncertainty is a real challenge for statisticians.

At a general level, uncertainty is defined as the conditional volatility of a disturbance that is

unforecastable from the perspective of economic agents. Following this definition, most

analysis has been done by computing volatility of past data, especially stock market data.3

However, this is not the most proper way to measure uncertainty. First, irrational exuberance

rather than economic fundamentals sometimes drive stock market developments (Guiso and

Parigi, 1999). Second, and more importantly, uncertainty is an expectation phenomenon and

measuring it with past information using time series data might lead to wrong results.

Therefore, several studies use survey data, in which participants report forecasts about some

economic variables.4

Bachmann, Elstner and Sims (2010) and Arslan et al. (2011) construct measures of

time-varying uncertainty from business surveys and examine their relationship with economic

2 For most recent studies see, for example, Arellano, Bai, and Kehoe (2010), Gilchrist, Sim, and Zakrajsek (2010),

Schaal (2012), Bachmann and Bayer (2011), Baker, Knotek and Khan (2011), Fernandez- Villaverde and Rubio-Ramirez (2010), Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry (2012), Leduc and Liu (2012), Nakamura, Sergeyev, and Steinsson (2012), Jurado, Ludvigson and Ng (2013), Başkaya, Hülagü and Küçük (2013). 3 Leahy and Whited (1996) use stock market volatility as a measure of uncertainty.

4 Using survey data to measure uncertainty has its own complications as well. Many studies use standard

deviations of point forecasts of participants, i.e. disagreement among them, as a proxy. Bomberger (1996) shows that disagreement comoves with consensus uncertainty and hence is a good proxy for total uncertainty. However, this is criticized by Butler and Rich (1998) by drawing attention to violations of modeling assumptions for maximum likelihood estimation that result from the overlapping nature of the forecasts. Hülagü and Şahinöz (2012), on the other hand, show that using disagreement as a proxy for inflation uncertainty might not be suitable for the Turkish data. Therefore, uncertainty can be measured most accurately by using density forecasts, which are non-existent in most surveys though. See Rich, Song, Tracy (2012).

activity over the business cycle for German and Turkish data, respectively. Both studies use

forecast errors of participants as a proxy for uncertainty, stemming from the identifying

assumption that higher uncertainty causes higher forecasts errors.5 They both show that

uncertainty has a strong relationship with output and can be used as a leading indicator. Our

paper is closely related to these studies, and we employ the same methodology with the

latter. Unlike them, we measure uncertainty in the financial sector. Our results show that

forecast errors of financial sector managers can be used as a good proxy for uncertainty as

our measure shows big hikes at times when most important recent events in Turkish and

international financial sector occurred. However, it should be noted that we only provide

visual evidence due to lack of a long time series. A more proper empirical research can be

done after enough data length is achieved.

The remainder of the paper is organized as follows: The next section introduces our

data. We explain the methodology in section 3. Results and discussion are given in section 4

and section 5 concludes the paper.

2. Data Description

Tendency surveys give early warning signals before the crisis and therefore acts as

an economic radar. The economic crisis has increased the importance of early warning

signals, therefore survey data has been used intensively and systematically throughout the

world. Financial Services Survey (FSS) is one of the surveys of “The Joint Harmonized EU

Programme of Business and Consumer Surveys.” This programme, which was implemented

by European Union (EU) in 1961, has expanded extensively both in terms of content and

regional scope since the launch date. Due to the growing importance of financial services

sector, the most recent addition is FSS, which was included in the program in 2006. In

Turkey, FSS was started in May 2012 and is fully harmonized with the EU programme.

In Turkey, FSS covers 6 sectors, which are given in Table 1. These sectors are:

banking, insurance, financial leasing, factoring, financing companies and brokerage firms.

Sectors are weighted according to their total assets. In order to determine which institutions

will participate within each sector, a “cut method” of 85 percent is used.6 However, due to

significant share of banking in the financial services sector, all 49 banks in Turkey are

included in the survey.

5 In a a dispersed information theoretical framework similar to the one used in Morris and Shin (2002), Arslan

et al. (2011) prove that higher uncertainty increases the probability of making forecast errors. 6 The reason to apply cut method is that there are so many institutions in these sub-sectors. Shares of most

institutions in the total sector are quite insignificant. Due to infeasibility of conducting the survey as a census the cut method is chosen.

Table 1. Summary Statistics of FSS Participants

Sector Number for Institutions Sector Share (%) Coverage (%)

Banking 49 92.5 100.0

Insurance 20 3.3 85.6

Financial Leasing 11 1.6 88.3

Factoring 28 1.1 85.2

Financing 5 0.8 84.8

Brokerage 20 0.7 87.2

FSS for Turkey is aligned with the principles of the EU programme. In particular,

questions in the survey are exactly the same with other countries. The questionnaire consists

of two question forms, which are given in the appendix. The first form comprises five

questions, which are asked at a monthly frequency, and refer to the past development of the

business situation, past and expected demand developments, and past and expected

employment developments. The second form of fourteen questions is part of the quarterly

questionnaire and is used in January, April, July and October. The quarterly questions refer

to past and future assessments of operating income, operating expenses, profitability, capital

expenditure, and the competitive position. As for all the other business surveys, there are

three different answer categories: increase/improve, remained unchanged, and

decrease/deteriorate.

Questionnaires are addressed to senior managers and participation is voluntary. But

a high response rate is critical for the quality and reliability of the results. FSS of Turkey has

an average response rate of approximately 90 percent.7

7 The other common property is having a representative sample. According to this property, the institutes

which conduct surveys should ensure that the samples chosen for each survey are representative of the sector. The sample size must be large enough to provide reliable data. FSS for Turkey covers 133 financial institutions. This sample size is large enough to provide reliable data for Turkish financial services sector. This coverage frame should be updated regularly so as to include mergers, acquisitions, new firms and also bankruptcies. For this reason the general survey framework is updated at the end of each year.

3. Methodology

In this study, we provide a measure of uncertainty for financial institutions in Turkey,

which is similar to Bachmann, Elstner and Sims (2012) and Arslan et al. (2011) constructed

for manufacturing companies. Assuming that higher ex-ante uncertainty leads to higher

errors when predicting the future, we construct a measure derived from expectation errors of

financial institutions by comparing their survey responses about expectations and

realizations on their turnover. Accordingly, answers to two different questions in the survey

with different timing are used. In particular, response (in the time survey) to the question

which asks expectations regarding the demand for services (turnover) in the next three-

month period is compared with response (in the time survey) to the question which

asks about realizations in the past three-month. Therefore, answers to expectation questions

and realization questions will cover the same period. This property allows us to analyze

expectation errors. For example, if a financial institution expects an increase in turnover for

the next three months but does not report an increase when asked again three months later,

we mark this as the financial institution made an expectation error, , at time . This is

summarized in Table 2.

Table 2. Computation of Expectation Errors,

Development over the last 3 months (t+3)

Increased Remained unchanged Decreased

Expectations

over the next 3

months (t)

Increase 0 - 1 - 2

Remain unchanged 1 0 - 1

Decrease 2 1 0

Then, we construct total uncertainty as the sum of weighted squared institution-

specific errors:

∑

, (1)

where is the weight of financial institution , and ∑ . One can see that an

increase in the uncertainty measure can stem from two different factors. First one is the

aggregate effect, where an increase (or a decrease) in the mean expectation results in

higher . In other words, when an unpredictable aggregate shock hits the economy most

participants will face an error in their forecasts and hence will increase. So, we define this

by the sum of weighted errors:

∑ . (2)

The second factor is the discrepancy. When participants make different errors, this

means that there are idiosyncratic shocks. Therefore, the discrepancy is defined by the

weighted squared differences of expectation errors from the mean errors:

∑ ( )

(3)

Now, one can easily show that

by identity. That is, total uncertainty is the sum of (squared) aggregate factors and

idiosyncratic factors. Under the scenario when a big aggregate uncertainty shock hits the

economy, we will have and meaning that the aggregate factor drives the total

uncertainty up. On the contrary, when uncertainties are limited to idiosyncratic effects (local,

regional or institution-specific), we will have small and big In the results section, we

analyze this decomposition, how these factors have emerged in Turkey and which factor has

driven total uncertainty more.

On the other hand, uncertainty is a second moment phenomenon. However, first-

order effects, or level effects, can also be resulted from economic events and an

identification issue may arise. Therefore, if one would compare uncertainty with events

occurred in the economy, level effects should be taken into consideration as well. To do this,

we compute balances as the weighted answers to questions about expectations:

∑ (4)

where is the response given by participant at time , and can take values , for will

increase, will remain unchanged and will decrease. is an indicator of, on average, how

participants expect their future turnover. In other words, a high (low) value of reflects

optimistic (pessimistic) expectations.

4. Results

4.1 Developments in the Total Uncertainty

In 2013, financial market trends were heavily influenced by announcements from the

major central banks. On May 22, 2013, the Fed announced that it was planning to taper its

asset purchasing programme amid firming macroeconomic conditions.8 The announcement

ended the era of globally abundant money, had severe impact on financial markets

throughout the world, and in turn led to a decline in the capital flows to emerging economies.

Credit Default Swaps (CDS), which protect investors against bond losses in case of a

sovereign default and show risk perception of the economy abroad, increased significantly

after the announcement (see Figure 1). However, this was not the only event that shook

Turkish financial markets. Starting late May, a wave of demonstrations against the

government and civil unrest, which is called Gezi protests, occurred and lasted a couple of

months. Under these circumstances, capital inflows to Turkey have also weakened and

uncertainty about what would happen in domestic financial markets increased significantly.

Therefore, any measure of uncertainty should hike in June 2013. In fact, increased by

eight times month-on-month figures and by almost 200 percent with respect to early 2013

averages (see Figure 2).

Figure 1. Turkey Sovereign CDS Spread

8 Similarly, measures adopted by the Bank of Japan (BoJ) to boost inflation had a decisive impact on the

movement of the yen and Japanese asset prices. In the euro area, yield spreads continued to narrow in 2013,

pursuing a process that began in mid‑2012 with the announcement of possible sovereign bond purchases on secondary markets via outright monetary transactions (OMT), as a highly accommodative monetary policy was maintained.

Figure 2. Total Uncertainty and Recent Economic Events

For the rest of 2013 and first half of 2014, global uncertainties about monetary

policies, weak capital flows towards emerging economies, increased regional uncertainties

due to geopolitical problems and the resulting rise in risk premiums in national markets have

been the main factors affecting Turkey’s economic outlook. Although the CDS spread

difference between emerging economies and Turkey diminished again in October and

November 2013, domestic political risk increased substantially due to corruption claims

against the government. This in turn caused a deterioration in risk perception of Turkey,

where spreads jumped to 248 from 188 in December, 2013. In line with these CDS figures,

total uncertainty increased by 40 percent and remained high in the first quarter of 2014.

From a global point of view the most important factor that influenced global markets in this

period was the Fed’s monetary policy decision to taper its asset purchases. Another

remarkable development was the negotiations about a new quantitative easing program to

avert the risk of deflation in the Euro Area. With advanced economies having started to

recover and monetary policies being normalized, emerging economies tightened their

monetary policies to some extent. Besides global risks, Turkey held local elections in late

March amid corruption claims and increased political uncertainty. Figure 1 shows that our

uncertainty measure reached its highest level in March 2014.

Local elections were a success for the ruling party and the outcome lowered political

tensions. This is observed in as well. After a declining trend in the second quarter and

reaching very low levels in May, uncertainty has risen again recently. In June 2014, war and

terrorism fears in Ukraine and Iraq were the main driving force in increased uncertainty.

Turkey has significant economic relations with these neighbors and, before June 2014, Iraq

0

0.2

0.4

0.6

0.8

1

1.2

1.4

FED Announcement, Gezi Protests

Domestic Political Problems

Local Elections

Problems in Ukraine and Iraq

had become the biggest export market for Turkish goods. Increased violence weighed

heavily on export figures and raised concerns about the future. Amid these tensions, has

increased to its high levels again.

One question that come up is “are these hikes represent level shocks rather than

uncertainty shocks?” This is a relevant question because adverse level shocks can also

increase forecast errors. In its regression analysis, Arslan et al. (2011) controls for level

effects by adding expectations about own-production volume of firm managers. Similarly, in

Figure 3, we depict total uncertainty with the balance, of the turnover expectations. As

one can see, level effects move in line with uncertainty only for February 2014. Before the

local elections, expectations deteriorated and this could be a complementary factor in the

rise of . On the other hand, increases in total uncertainty are not accompanied with

adverse level shocks. For example, expectations remained stable in May 2013 when Fed

announcement on its asset-purchasing programme increased significantly. To sum up,

level shocks are important but they are not necessarily the main factor in the developments

of our uncertainty measure.

Figure 3. Total Uncertainty and Level Effects

4.2 Decomposition of Total Uncertainty

As mentioned above, total uncertainty is the sum of aggregate factors and

discrepancy (idiosyncratic factors). This decomposition of total uncertainty in Figure 4 reveals

that the latter is the driving factor for total uncertainty. Even though both factors comove

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Total Uncertainty Balance (Right Axis)

except for the hike in late 2013, it is clear that has been higher than which means

idiosyncratic factors have significantly affected the total uncertainty in Turkish financial sector

and aggregate effects have been limited. This is somewhat surprising in the sense that all

events listed above are economy-wide and they are expected to increase more than

Nevertheless, the biggest hike that happened in February 2014 can be explained by

idiosyncratic factors as most polls diverged before the local elections and this could shape

expectations of institutions differently. Furthermore, there is a strong correlation (0.48)

between these factors in this period.

Figure 4. Decomposition of Total Uncertainty

5. Conclusion

This paper studies measuring uncertainty in the financial sector, using expectation

errors of managers. Our results show that forecast errors can be used as a good proxy for

uncertainty as our measure shows big hikes at times when most important recent events in

Turkish and international financial sector occurred. This finding is robust when we take into

account the level effects. We further decompose total uncertainty into two, aggregate and

idiosyncratic factors. Our data reveals that the latter is the main driving factor for total

uncertainty in the 2013-2014 first half period. However, it should be noted that we only

provide visual evidence due to lack of a long time series. A more proper empirical research

can be done after enough data length is achieved.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Total Uncertainty Aggregate effect Discrepancy

References

Arellano, Christina, Bai, Yan, and Patrick Kehoe, "Financial Markets and Fluctuations in Uncertainty," Federal Reserve Bank of Minneapolis Research Department Staff Report, April 2010. Arslan, Yavuz, Atabek, Aslıhan, Hülagü, Timur, and Saygın Şahinöz, “Expectation Errors, Uncertainty and Economic Activity”, Central Bank of the Republic of Turkey Working Paper No. 11/17, 2011. Bachmann, Ruediger, and Christian Bayer, “Uncertainty Business Cycles - Really?”, NBER Working Paper 17315, 2011.

Bachmann, Ruediger, Elstner, Steffen, and Eric Sims, "Uncertainty and Economic Activity: Evidence from Business Survey Data," NBER Working Paper Series, Vol. w16143, pp. -, (2010). Available at SSRN: http://ssrn.com/abstract=1630148. Başkaya, Soner, Yusuf, Hülagü, Timur, and Hande Küçük, 2013, “Oil Price Uncertainty in a Small Open Economy,” IMF Economic Review, 61 (1), (2013), 168-198. Bloom, Nicholas, "The Impact of Uncertainty Shocks," Econometrica, 77 (2009), 623-685.

Bloom, Nicholas, Bond, Stephen, and John Van Reenen, "Uncertainty and Investment Dynamics," Review of Economic Studies, 74 (2007), 391415.

Bloom, Nicholas, Floetotto, Max, and Nir Jaimovich, "Really Uncertain Business Cycles," mimeo, Stanford University, 2009. Bloom, Nicholas, Floetotto, Max, Jaimovich, Nir, Saporta-Eksten, Itay, and Stephen J. Terry, “Really Uncertain Business Cycles”, NBER Working Paper 18245, 2012. Bomberger, William A. “Disagreement as a measure of Uncertainty”, Journal of Money, Credit and Banking 28 (1996), 381-392. Fernandez-Villaverde, Jesus, and Juan F. Rubio-Ramirez, "Macroeconomics and Volatility: Data,Models, and Estimation," mimeo, University of Pennsylvania, 2010. Gilchrist, Simon, Sim, W, Jae, and Egon Zakrajsek: “Uncertainty, Financial Frictions, and Investment Dynamics”, Unpublished Manuscript, Boston University, 2010. Guiso, Luigi, and Giuseppe Parigi, "Investment and Demand Uncertainty," Quarterly Journal of Economics, 114(1) (1999), 185-227. Hülagü, Timur, and Saygın Şahinöz, "Is Disagreement a Good Proxy for Inflation Uncertainty? Evidence From Turkey," Central Bank Review, 12 (1), (2012), 53-62.

Jurado, Kyle, Ludvigson, C.Sydney, and Serena Ng, “Measuring Uncertainty”, NBER Working Paper 19456, 2013. Knotek, S, Edward, and Shujaat Khan, “How Do Households Respond to Uncertainty Shocks?”, Federal Reserve Bank of Kansas City Economic Review, 2011 Leahy, John V., and Toni M. Whited, "The Effect of Uncertainty on Investment: Some Stylized Facts," Journal of Money, Credit & Banking, 28(1) (1996), 64-83. Leduc, Sylvain, and Zheng Liu, “Uncertainty Shocks are Aggregate Demand Shocks” Federal Reserve Bank of San Francisco, Working Paper 2012-10, 2012. Morris, Stephen, and Hyung S. Shin, "The Social Value of Public Information," American Economic Review, 92(5) (2002), 1521-1534. Nakamura, Emi, Sergeyev, Dmitriy, and Jon Steinsson, “Growth-Rate and Uncertainty Shocks in Consumption: Cross-Country Evidence”, Working Paper, Columbia University, 2012. Rich, Robert, Song, Joseph, and Joseph Tracy, “The Measurement and Behaviour of Uncertainty: Evidence from the ECB Survey of Professional Forecasters”, Federal Reserve Bank of New York Staff Report, No 588, 2012. Rich, R. W., Butler, J. S., “Disagreement as a measure of uncertainty: A comment on Bomberger”, Journal of Money, Credit and Banking 30, 1998, 411-419. Schaal, Edouard. “Uncertainty, Productivity, and Unemployment in the Great Recession”, Unpublished paper, Princeton University, 2012.

APPENDIX: SURVEY QUESTIONS

Monthly Form

Q.1 How has your business situation developed over the past 3 months?

Q.2 How has demand (turnover) for your company’s services changed over the past 3

months?

Q.3 How do you expect the demand (turnover) for your company’s services to change over

the next 3 months?

Q.4 How has your firm’s total employment changed over the past 3 months?

Q.5 How do you expect your firm’s total employment to change over the next 3 months?

Quarterly Form

Q.1 How has your business situation developed over the past 3 months?

Q.2 How has demand (turnover) for your company’s services changed over the past 3

months?

Q.3 How do you expect the demand (turnover) for your company’s services to change over

the next 3 months?

Q.4 How has your firm’s total employment changed over the past 3 months?

Q.5 How do you expect your firm’s total employment to change over the next 3 months?

Q.6 How has your operating income developed over the last 3 months?

Q.7 How do you expect your operating income to develop over the next 3 months?

Q.8 How have your operating expenses developed over the last 3 months?

Q.9 How do you expect your operating expenses to develop over the next 3 months?

Q.10 How has the profitability of your company developed over the last 3 months?

Q.11 How do you expect the profitability of your company to develop over the next 3

months?

Q.12 How has your capital expenditure developed over the last 3 months?

Q.13 How do you expect your capital expenditure to develop over the next 3 months?

Q.14 How has the competitive position of your company developed over the past 3 months

in your country?

Q.15 How has the competitive position of your company developed over the past 3 months

within European Union?

Q.16 How has the competitive position of your company developed over the past 3 months

outside European Union?

Q.17 How do you expect the competitive position of your company to develop over the next 3 months in your country?

Q.18 How do you expect the competitive position of your company to develop over the

next 3 months within European Union?

Q.19 How do you expect the competitive position of your company to develop over the

next 3 months outside European Union?