Pricing foreign currency debt in Hungary Zoltán Schepp – Mónika Pitz University of Pécs SEM 2nd Conference OECD Paris July 2015

Foreign currency borrowing in CEE Countries On growing contemporary literature on the cross-country properties of foreign currency borrowing  Rosenberg-Tirpák (2009): studies lending motives of banks  Fidrmuc et.al. (2011): examines motives  Brown et.al. (2011): specialties of small firms borrowing  Beckmann et.al. (2012): searches for reasons behind late repayment  Brown-De Haas (2012): relationship between foreign assets and foreign currency denominated lendingng  Yesin (2013): compares systemic risk of countries  Brown er.al. (2014): role of asymmetric information Hungarian case: unique in many terms  Competing narratives but quite little empirics

The story to understand In Hungary, the proportion of the foreign currency loans in the total loan volume reached 70%, most of which was in Swiss francs. Crisis: higher interest rates and depreciation of HUF  Increase of monthly instalments  20% non-performing loans Who should take the responsibility? –Households: lack of financial knowledge, irresponsible risk taking –Authorities: no prevention –Banks: shifting the risks to households Core policy and political question: –Had the Hungarian banks priced their power to the retail market, or passed through the shocks in funding costs faced by themselves!?

What was/is different in Hungary? The proportion of foreign currency loans to HH-s and non-financial companies in the EU, March 2011 (source: European Systemic Risk Board)

What was/is different in Hungary? (cont.) The proportion of euro and other (mainly CHF) denominations in the foreign currency loans in the EU, Aprill 2011 (source: European Systemic Risk Board)

Specialties of the the Hungarian case Contrast between individual and (non explicit!) systemic risk  Wide ER band (+-15%, from spring 2008: floating ER)  In the Baltic countries: narrow peg to the euro No significant transfers from guest workers in western Europe  Contrary to e.g. Romania To high swizz franc ratio  Compared to Baltic countries (with similar indebtness) To high indebtness  Compared to countries in them swizz franc debt played also an important role (Austria, Poland) High international capital and trade openness  ca. 200 Bn. EUR total foreign liabilities, and 50 Bn. EUR net foreign debt (2011 autumn), close to 200% trade/GDP ratio  This is not unique but of course important

HUF/CHF exchange rate movements The HUF/CHF rate depreciated on average 20-25%, and the CHF/EUR appreciated another 20% between 2005 and 2013.

Common macro-environment for taking/lending foreign currency denominated credits in Hungary Basic motivational factors  Currency risk premium on Hungarian financial assets (e.g. government bonds) – expected cost cut  Bad properties of HUF denominated credits – to high interest rates, volatility of term premium of longer maturities Factors hindering/distorting risk perception  Interest rate reactions of the NBH between 2003 and 2008  Political promises about euro zone accession (between 2002 and 2008 always 5 year ahead of…)  Disrupted („risk-based”) competition on the banking market  Systematic underestimation/ignoring of lending risk

Misperception and miscalculation of risk perceived de facto cost propability E[C]

Sector specific motivations in the case of local governments (LG) and business sector (BS) The retention (‘own-contribution’) needed to obtain grants from EU Structural and Regional Funds  (LG) no liquid capital, they raised the necessary funds by issuing foreign currency bonds with a maturity of 20 to 25 years  (BS) In addition to EU subsidies, banks provided FX-loans to fund real estate projects (weak income-generating capacity) The effects of the partial fiscal consolidation carried out by the second Gyurcsány government beginning in the middle of 2006  (LG) changes in the terms of financial support and task-sharing to the disadvantage of local authorities  (BS) missing demand at companies which had tried to compensate for declining income by the cost-benefits of FX- loans or even by carry trade speculation based on “forward- rate-bias”

Sector specific motivations in the case of local governments (LG) and business sector (BS) (cont.) The low interest rates on FX-loans permitted higher leverage  (LG) a substantial number of local authorities had been dealing with long-run financial problems, so issuing foreign currency bonds simply to kept their economic scope for action alive  (BS) the liquid part of the equity of Hungarian-owned SMEs was too low compared to the level of the firm pre-crisis economic activity and was replaced by the relatively cheap and easily available sources of credit Consequences (after crisis has hit)  Local governments: the central government took over the cumulative bank liabilities (ca. €4.5 Bn., between )  Corporates needed liquidity or restructuring, so „forintisation” and/or IR change has happent often relative fast and in a cooperative way (although with not equal powered parties…)

Why has become households foreign currency indebtness a systemic financial problem? The story once again. Increased instalments Late repayments Higher unemployment Lower lending, GDP… Higher interest rate (hh loans) Exchange rate depreciation Exchange rate depreciation Reference rate + risk premium Moral hazard ?

What else could effect interest rates? We assume basically four main price shocks: –Reference rate –Risk premium –Loan portfolio quality –Fiscal burden –Interest rate pricing (stock and new loans) Housing loans Non-purpose mortgage loans External funds of banks SVAR

Data Time span  Monthly data between 2005M1 and 2013M12 Reference rate  3 month CHF (chflibor) in base points Risk premium  5Y sovereign CDS (cds) in base points Loan portfolio quality:  impairment rate (impair)  used proxy: recognized impairment of assets (shows the losses caused by non-performing loans) / % of total assets Fiscal burden:  Corporate tax, special bank tax (2010-), early repayment losses, financial transaction tax/duty / % of total assets

Data

Structural VAR model (2008M M12) Household sector: B 0 z t = k+B 1 z t-1 +B 2 z t-2 +…+B p z t-p +u t z t =(d(chflibor) t, d(burd_hh) t, d(cds) t, d(impair_hh) t, d(y) t ) T Corporate sector: B 0 z t = k+B 1 z t-1 +B 2 z t-2 +…+B p z t-p +u t z t =(d(euribor) t, d(burd_corp) t, d(cds) t, d(impair_corp) t, d(y) t ) T k= c + crisis t

Identification Constraints on immediate effects are based of theoretical considerations burd cds impair y libor

Impulse response functions – housing loans

Impulse response functions – mortgage equity withdrawals

Impulse response functions – corporate loans

Statments based on SVAR results Results are highly sensitive to the sample  Between 2005M1 and 2012M3 increased tax burdens, augmented country risks and a declining readiness to pay contributed to the rising interest rates of housing loans. (Pitz-Schepp, 2013)  The price flexibility of the population may have been higher in the case of free-purpose mortgage loans, which restricts pricing opportunities of banks (2005M1-2012M3)  When analyzing separately the post-crisis time period 2008M M12 we found that the above effects no longer appeared and that only the reference rate (negative sign!) and the CDS were decisive.  According to the corporate sector, only the effect of euribor proved to be significant.

VECM for housing loans We suppose a long run equilibrium relationship between cost components and IR on existing credit stock.  2005M1-2013M12 monthly data, no crisis dummy  Lags determined by LR test (hh: 2, mew: 1 period) Johansen procedure: cointegrating vector exists  With all four cost factors being significant Question in the politics:  Where IR changes unfair? Policy question:  Was it pricing to the market or cost based pricing? The opportunity to change (the institutional setup) was a failure in itself.  but 7,5 year long nothing has happen in the politics!

Data

Vector error correction modelling ( ) chfliborBurdcdsimpair housing 0,96 (0,33) -1,73 (0,69) 0,66 (0,11) 0,56 (0,14) mew 1,15 (0,29) -3,53 (0,63) 0,65 (0,10) 0,91 (0,14) For housing loans we used a two-lag model and for mortgage equity withdrawals a single-lag model The Coefficients of the Normalised Co-integrating Vector and Standard Errors With the Johansen procedure we estimated the cointegrating equations expressing the long-run dynamic, which indicated a significant relationship for all four cost typess.

The Significant Coefficients of the Estimated VEC Models and Test Results housing Error correctiond(housing(-1))d(housing(-2))C Coefficient Standard errors R-squared0.456 Adj. R-squared0.392 mew Error correctionC Coefficient Standard errors R-squared0.197 Adj. R-squared0.148 ER coefficients and constant are significant in both cases.

A possible narrative debt Interest rate E[MC]=S(chf,t0) E[MU]=D(chf,t0) MC(chf,t1) Stock (chf) S(huf,t0) D(huf,t0)

Debate on unfair-banking in Hungary Wide ranging conceptual confusions is associated with the notion of fairness and naivety of banks by repricing: –IR changes where „unfair” (eg. reverse to LIBOR changes) –Banks where „naive” because higher IR-s leading to worse quality of loan portfolio The VECM show significant long run equilibrium relationship between existing cost factors and mortgage interest rates. Proportional and symmetric: see LIBOR Less than proportional: impairmant (losses) and cds Inverse (asymmetric) relationship: taxes –But this is a good news for the debtors! Banks were not naive by IR changes, but they had from the beginning market power and a kind of monopolistic price

Conclusion All four types of shock might have been played a role in determining interest rates for housing loans, i.e. the cost shocks of banks are more or less accurately reflected in the interest rates applied by domestic banks. In a long run view pricing seemed to be cost-based.  This cost have been covered by the debtors, but very recently banks has to take beck it (some 3 Bn. EUR!)  This is not necessary fair, because of the regulatory failures also has been made for a very long time. All three parties (state, banks, debtors) made mistakes  everyone tried to act bilateral based on power distribution  But the best solution of the problem needs (would have needed…) a cooperation of all three parties

Many thanks for your attention!