The paper uses monthly data on financial stock index returns, tourism stock sub-index returns, effective exchange rate returns and interest rate differences from April 2005 – August 2013 for Taiwan that applies Chang’s (2014) novel approach for constructing a tourism financial indicator, namely the Tourism FinancialConditionsIndex (TFCI). The TFCI is an adaptation and extension of the widely-used Monetary ConditionsIndex (MCI) and FinancialConditionsIndex (FCI) to tourism stock data. However, the method of calculation of the TFCI is different from existing methods of constructing the MCI and FCI in that the weights are estimated empirically. The empirical findings show that TFCI is estimated quite accurately using the estimated conditional mean of the tourism stock index returns. The new TFCI is straightforward to use and interpret, and provides interesting insights in predicting the current economic and financial environment for tourism stock index returns that are based on publicly available information. In particular, the use of market returns on the tourism stock index as the sole indicator of the tourism sector, as compared with the general activity of economic variables on tourism stocks, is shown to provide an exaggerated and excessively volatile explanation of tourism financialconditions.
stance. In the 1990s, these indices became widely used to assess the stance of monetary policy. 1
Later on, a number of authors extended the idea of the monetary conditionsindex to other asset prices equally relevant for economic activity (such as long-term interest rates, equity prices and house prices, among others). These measures were called financialconditions indices and they intended to provide a broader measure of financialconditions than monetary conditions indices. Several international organisations, central banks, investment banks and academics have developed Financing Conditions Indices (FCI) to assess the prospects of economic activity and inflation, the appropriateness of the macroeconomic policy stance, and to guide financial investment decisions. Extensive work has been done to analyse financialconditions in the United States, and to a lesser extent in the euro area. Some work has been done also on Japan, the United Kingdom, and groups of developed countries. In what follows, we review several FCIs developed in the literature, including: the St. Louis Fed’s Financial Stress Index, the Chicago Fed National FinancialConditionsIndex (Brave and Butters, 2011), the ECB Global Index of Financial Turbulence (ECB, 2009), three indices constructed at the International Monetary Fund (IMF, 2008; Swiston, 2008; and Matheson, 2012); the OECD FinancialConditionsIndex (Guichard et al., 2009), the Goldman Sachs FCI, the Deutsche Bank FCI (Hooper et al., 2007 and 2010), the Bloomberg FCI (Rosenberg, 2009) and the Citi FCI (D’Antonio, 2008) and three indices developed in the academic literature (Hatzius et al.,2010; Hollo et al., 2011; and van Roye, 2011).
the Norwegian economy. Inspired by existing FCIs for other economies and adapting to a Norwegian framework, the construction of a financialconditionsindex for Norway is thus the main focus of this paper. 1,2 Several methods have been used to construct FCIs. Here, an underlying factor is estimated by using the method of principal components. This procedure allows for the inclusion of a large number of financial variables, yet a parsimonious model is retained. The estimated factor is taken as a measure of financialconditions, which in turn are expected to provide information about real economic activity. To examine the possible real- financial linkages, correlation analyses and analyses of in- and out-of-sample fit of a regression model are performed and supplemented with graphical inspections. The FCI is found to be a useful leading indicator of real economic activity: It is easy to estimate and available on a monthly basis. An alternative version of the index is also considered, where the FCI is based on financial variables purged of business cycle effects. This adjustment is done in order to create an FCI reflecting financial variables’ impact on economic growth, without including their
The foundation of the proposed the Tourism FinancialConditionsIndex (TFCI) is an application of the FinancialConditionsIndex (FCI), which is derived from the Monetary ConditionsIndex (MCI). As stated by the Bank of Canada, the MCI is an index number calculated from a linear combination of two variables, namely the short-run interest rate and an exchange rate, that are deemed relevant for monetary policy. Based on the MCI, the FCI takes account of an extra factor, namely real asset prices, such as house prices and stock prices, to assess the conditions of financial markets (see Beaton, Lalonde, and Luu,2009; Brave and Butters, 2011; Ericsson, Jansen, Kerbeshian, and Nymoen, 1997;Freedman, 1994, 1996a, b; Hatzius, Hooper, Mishkin, Schoenholtz, and Watson, 2010;Lin, 1999; Matheson, 2012; and Thompson, Eyden, and Gupta, 2015).
The foundation of the proposed TCI is an application of the FinancialConditionsIndex (FCI), which is derived from the Monetary ConditionsIndex (MCI). As stated by the Bank of Canada, the MCI is an index number calculated from a linear combination of two variables, namely the short-run interest rate and an exchange rate, that are deemed relevant for monetary policy. Based on the MCI, the FCI takes account of an extra factor, namely real asset prices, such as house prices and stock prices, to assess the conditions of financial markets (see Beaton, Lalonde, & Luu, 2009; Brave & Butters, 2011; Ericsson, Jansen, Kerbeshian, & Nymoen, 1997; Freedman, 1994, 1996a, b; Hatzius, Hooper, Mishkin, Schoenholtz, & Watson, 2010; Lin, 1999; and Matheson, 2012). Further details are given below in Section 4.
Our identification of shocks to financialconditions starts from the approach taken in the literature in examining credit supply shocks. Busch et al. (2010) assume that a positive credit supply shock is associated with a decline in the lending rate to non-financial corporations that boosts activity and triggers a tightening of the monetary policy rate – in effect, the lending spread narrows. However, this approach relies on having data for the composite lending rate for the non-financial sector. Such data are available for China only from 2009. To overcome this problem Breitenlechner and Nuutilainen (2017) use monthly PBoC statistics on the share of loans priced above/below the benchmark lending rate to proxy average lending rate. Our approach uses instead the information available in financialconditions indices (FCIs), follow Darracq et al. (2014) who use FCIs in an SVAR as a means to help the identification of credit supply shocks (see also Kapetanios et al., 2017). This approach assumes that a positive credit supply shock is associated with a softening of the financing environment (and a loosening of the FCI) which boosts activity, raises credit volumes and triggers a tightening of the monetary policy rate. Aikman et al. (2017) also use an FCI for the United States to identify shocks to financialconditions which they judge as representing the ease of access to credit which affects economic behaviour. They offer three structural interpretations for such a shock: a time-varying risk premia of investors determined by bank capital constraints; endogenous reactions of financial intermediaries to episodes of low volatility; or a change in the perceived dispersion of investment project returns. As Charts 3 and 4 show, there is a very close correlation between the lending spread and the financialconditionsindex over the period 2009-2017. That would suggest that the FCI has also provided a good guide to the lending rates for the non-financial sector and should help us to identify shocks that represents shifts in the ease of credit access for broader Chinese economy.
However, this analysis, although providing benefits to the monitoring and evaluation of the aggregate creditworthiness of the non-financial sector (for example, allowing an insight of the firms’ financial and economic characteristics), has also highlighted some important limitations. Firstly, there is no currently –yet– no aggregate measure for financial soundness or one that allows the identification of balance sheet risks and vulnerabilities. At the Central Bank, the state of these features of non-financial firms is determined by analyzing separately a restricted set of financial ratios, at the present moment being: current ratio (as a proxy of liquidity), return on assets (as a proxy of profitability), debt-asset ratio (as a proxy of indebtedness), and non-operative expense to the earnings before interests and taxes (EBIT) ratio (as a proxy of financial burden). That said and for a particular time period, an overall conclusion of the private corporate sector’s financial health is achieved by comparing the latest figures of individual ratios with historical ones, especially with those observed in periods of high stress.
( 2014 ) also has important implications for regulation; the model economy can remain in a crisis state for prolonged periods, if initial capital cushions are too slim. This papers underlying model shares these same characteristics, as needed to explicitly study output volatility.
While this newer class of models does not dwell on monetary policy—unlike this paper—somewhat older papers do. Curdia and Woodford ( 2010 ), spurred by the global financial crisis, considers whether monetary policy should respond to financialconditions—credit spreads in particular—following the recommendations of McCulley and Toloui ( 2008 ), and Taylor et al. ( 2008 ) (while Christiano et al. ( 2010 ) favors a response to aggregate credit growth). Gambacorta and Signoretti ( 2014 ) builds on Curdia and Woodford ( 2010 ) by adding further frictions, and Gertler and Karadi ( 2011 ) advances a model where central banks can complement the private sector’s intermediation function, thereby carving out a role for asset purchases.
more volatile. 5
This paper finds that the “vulnerable growth” approach of Adrian et al. (2019) is also relevant for the euro area, but it is important to use an appropriate indicator of financialconditions. Some intuitive indicators, such as the retail lending spread or the sovereign spread are not useful indicators of tail risks to growth. Bond spreads and the stock volatility work reasonably well and the euro area TED spread is the best among our individual indicators. A simple aggregation of the individual indicators with a principal component does not yield a particularly informative financial indicator for estimating risks to growth. We find that the most informative financial indicator is the CISS, which aggregates individual indicators in a nonlinear way capturing the “systemic” nature of events.
and growth at risk. The literature has shown that an increase in excess bond premium leads to a contraction in the supply of credit and to an increase in the downside risk to growth, but has little effect on its upside potential. The ensu- ing increase in economic risk makes firms even more cautious in responding to business conditions, ultimately leading to lower investment and an even more pronounced downside risk to the economy. We provide evidence that this financial-economic risk channel is not only statistically significant, but also economically important. We measure how the system reacts in a good versus a bad environment (Bekaert and Engstrom, 2017). We compute the forecast of the US industrial production under a scenario in which the system is hit by a sequence of negative real and financial shocks (bad environment), and compare its performance to a situation where the system is hit by symmetric positive real and financial shocks (good environment). The bad environment is characterized by a much more pronounced downturn, with a maximum con- traction of monthly industrial production of more than 2%. This compares with an expansion of slightly more than 1% under the good environment.
However, the current unconventional monetary policies of AEs in the form of low or even negative nominal policy rates as well as quantitative easing together with unintended consequences of the ﬁnancial sector reforms across the world pose new challenges to EMEs. Until recently because of better growth prospects and lower levels of leveraging, EMEs attracted capital in ﬂows with their non- ﬁnancial corporates having lion shares. The normalization of global monetary policies has reversed the course of capital ﬂows and led to the sizable depreciation of EM currencies against the dollar. This creates in ﬂationary pressures due to the pass-through impact and impairs balance sheets due to tightening global ﬁnancial conditions and worsening growth prospects. Further- more, the sharp fall in oil prices exacerbates the deterioration in economic outlook for commodity importing countries. Going for- ward, the normalization of the US monetary policy, diverging monetary policies of AEs, uncertainty in commodity prices and the risk of Chinas economic hard-landing all will likely to contribute to volatile global ﬁnancial markets and widening spreads for EMEs.
15 In a previous version, I used factor analysis approach (FA) instead of principal components approach (PCA) to
calculate the FCI. FA and PCA are similar because both create variables that are linear combinations of the original ones. But they differ in thatk while PCA accounts a maximal amount of variance for observed variables, FA accounts for common variance in the data. That is one of the reasons why FA is generally used when the research purpose is to detect data structure (i.e., latent constructs or factors) or causal modeling, while PCA is generally preferred for purposes of data reduction (i.e., translating variable space into optimal factor space), but not when the goal is to detect the latent factors. As one of the main objectives here is to obtain an aggregate index that reflects rare financial events that may not be generalized through the whole financial system, it sounds plausible to extract the maximal variance and not the common one. Besides, when applying FA, some series are discarded because of sample adequacy and goodness-of-fit criteria while the whole data set is used when PCA is applied.
risk” analysis. 3 The index can be easily replicated for a large number of countries. This broadens the analysis outside of the US, the economy for which the largest number of financial condition indices has been developed. In the rest of the analysis we will refer to this index as ‘TVP-FCI’. We contrast this benchmark with two simple alternatives based on simple weighted averages. The choice of the weights is deliberately naive, and reflects the purpose of the index. First, we construct an index that measures the level of stress in the economy, by assigning half of the weight to spreads and to stock market volatility. The level of interest rates, the exchange rate, as well as stock returns and house prices, account with approximately equal weights for the rest of the index. We call this index the ‘WA-FSI’, to reflect the fact that it is based on a weighted average (hence WA) and that it reflects more heavily measures of stress than financialconditions in normal times (hence FSI). The second alternative is geared more towards capturing the actual cost of financing for economic agents, and gives a predominant role to the level of interest rates, as well as to equity valuations. We call this second alternative the ‘WA-FCI’. Although all our analysis is based on monthly data, the WA-FCI relies on indicators that would be available also at at very high (daily) frequency. We see these latter two indices as complementary. The former, loading more heavily on indicators that signal strains in the financial system, could be more useful in the context of financial stability analysis. The latter, being more representative of the level of credit costs and being available at higher frequency, could be of greater interest for monetary policy monitoring.
firm that index funds triggered excessive finan- cial speculation. Though empirical literature yielded differing findings and contentious interpretations of such findings, there is hardly any conclusive evi- dence to prove that long-only index funds have significantly raised the level or volatility of prices of agricultural commodities with their futures tran- sactions. Numerous further empirical studies have been published in the meantime that support this assessment (e.g. Gilbert/Pfuderer, 2013 ; Sanders/ Irwin, 2013 ; Schmitz/Moleva 2013 ). A very recent survey study commissioned by Foodwatch (Bass,
between individual contribution and
Investment policy of pension funds should be very conservative, especially in the period of market instability and negative trends of financial market because the investment safety is in the first place. The possibility of voluntary pension funds’ property growth in Montenegro in this and following years does not have to be limited if the accumulation of collected contributions is provided by many fund members in a long period of time, i.e. in future. Pension funds, as well as life insurance companies, must invest their own funds into safe property types and securities with high rating. It is also necessary to provide tax relief for asset payments into pension funds. In other words, pension funds need a regime including: (a) contributions tax‐free (contri‐ butions for all kinds of incomes from labour should be tax‐free); (b) fund incomes tax‐free (in order to avoid double taxation of fund incomes), and (c) taxation of funds’ payments (in order to increase payment sums for
A.3.1. Firm level dataset - SIR 2013 (BvD Amadeus)
The first sample is derived from the firm-level database used for the firm-level analysis in Chapter 3 of the SIR 2013 (ECB Structural Issues Report, “Corporate finance and economic activity in the euro area”). This was originally based on the Bureau van Dijk Amadeus database, which includes comparable financial information for public and private companies. The ECB database included firms located in all 17 euro area countries covering the period from 1993 to 2010. However, as suggested by the authors, owing to a widespread increase in the number of observations across all countries since 2000, in particular for smaller firms, the final published dataset includes data from 2001 to 2010. Moreover, as a final step, and in order to calculate certain variables that required lagged observations in the report and to increase the reliability of firms’ data, companies were only considered if they had been monitored for at least three consecutive years. This condition represents a major difference from the CompNet approach where firms could enter and exit from the database every year. For this reason, we slightly revised the SIR sample to let firms be present also for less than 3 consecutive years and we updated this sample to get data until 2012. Moreover, we applied the same data cleaning procedure for the outliers.
JEL classification: G01, G28, E58, D44
We examine the financialconditions of dealers that participated in two of the Federal Reserve’s lender-of-last-resort (LOLR) facilities--the Term Securities Lending Facility (TSLF) and the Primary Dealer Credit Facility (PDCF)--that provided liquidity against a range of assets during 2008-09. Dealers with lower equity returns and greater leverage prior to borrowing from the facilities were more likely to participate in the programs, borrow more, and--in the case of the TSLF--at higher bidding rates. Dealers with less liquid collateral on their balance sheets before the facilities were introduced also tended to borrow more. There also appear to be some interaction effects between financial performance and balance sheet liquidity in explaining dealer behavior. The results suggest that both financial performance and balance sheet liquidity play a role in LOLR utilization.
8.5 INFORMATION VALUE
The start date of the crisis period was set as October 13, 2008 because the Hungarian Central bank intervened in the foreign exchange swap market on this day, thus it can be viewed as the beginning of the 2008-2009 financial crisis in Hungary. Tying the start of a crisis period to intervention is a common practice in the literature (see: Laeven & Valencia, 2008; Kaminsky & Reinhart, 1999). The end of the stress period was defined as May 15, 2009 as this yields a big enough window for evaluation. The evaluation period was also constrained to be around the crisis. This was done because between 2009 and 2016 there were several smaller but important financial events but it is unclear ex-ante how large the financial stress index should be in these periods. Before calculating the IV the factors were broken down into deciles of equal size to ensure that higher deciles have enough observations in them. The deciles were further distributed into 2 bins: A good observation bin, and a bad observation bin. The IV is then calculated for each factor with the following equation:
ABSTRACT: An index model based on DRASTIC and the related SINTACS is applied for groundwater vulnerability assessment in the highly urbanized area of Chennai City, India. In index models, empirical indices are allocated to certain intrinsic, physical parameters defining the underground and the land sur- face with regard to their capability to protect the aquifer against surficial contamination. Different from numerous former DRASTIC vulnerability studies, the specific site conditions as well as the monsoon- driven rainfall pattern are considered by adapting certain input parameters and their scores and weights to obtain a more realistic spatial distribution of the groundwater vulnerability. The adapted parameter maps are ‘Depth to water table’, ‘Groundwater recharge’, ‘Aquifer media + Vadose zone’, ‘Soil media + Land cover’, ‘Topography’, and ‘Hydraulic conductivity’, which are weighted and overlain to one composite map displaying mostly medium groundwater vulnerability. Fuzzy logic is then applied as an additional approach, wherefore four suitable parameters with continuous data sets are fuzzified first and, after de- fuzzification, combined with the two remaining parameters. The resulting map shows a slight increase of groundwater vulnerability compared to the classic approach. The two vulnerability maps can be a used as a preliminary estimation tool to prevent further groundwater quality deterioration in the study region.
4 Data and forecasting methodology
We use monthly data covering the period from March 1973 to August 2012. In- dustrial production index (y), consumer price index (π) and the fed funds rate (r, an average of daily figures) are taken from the Federal Reserve Bank of St. Louis (FRED) Database. Choosing a good proxy to describe financial market conditions is not a trivial task. We use the Financial Condition Index ( f ci) constructed and main- tained by the Chicago Fed (see Brave and Butters (2012) and references therein). F ci is a real-time indicator extracted using dynamic factor analysis from a set of over 100 series describing money, debt, equity markets and the leverage of finan- cial intermediaries. As such, it represents to our knowledge the broadest available summary of financialconditions in the US. This has two key advantages. First, by including f ci we eﬀectively turn our (linear or nonlinear) VARs into factor models, or FAVARs, that exploit a much larger information set than they would if we used instead a "plain vanilla" financial indicator, such as a bond spread or a credit ag- gregate. This minimizes the possibility that the (otherwise relatively small) size of the dataset might bias the results in favour of nonlinear models — a crucial point, given our objectives. Second, the predictive power of many financial variables is known to be unstable over time, and using a broad indicator allows us to reduce the risk of obtaining results that are too heavily aﬀected by the idiosyncratic behavior of specific variables in specific subperiods. As a robustness check, we replicate our analysis replacing f ci with the Excess Bond Premium of Gilchrist and Zakrajsek (2012). The results, documented in the Annex, show that our main conclusions hold under this alternative specification. 7 We note that both indicators have been found