Skip to main content
SpringerLink
Log in

The real effects of loan-to-value limits: empirical evidence from Korea

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

This study adds to a recent and growing literature that assesses the effects of macroprudential policy. We compare the effects of monetary policy and loan-to-value ratio shocks for Korea, an inflation-targeting economy and an active user of loan-to-value limits. We identify shocks using sign restricted structural VARs and rely on a recent approach within this method to conduct structural inference. This study finds that both monetary policy and loan-to-value ratio shocks have effects during the period that our sign restrictions applies on different measures of credit, i.e., real bank credit, real total credit and real household credit, as well as on real output, real consumption and real investment. We find though that loan-to-value ratio shocks have negligible effects on the price level. Both shocks, however, have non-negligible effects on real house prices, evidence that go beyond the period of the imposed sign restrictions. These findings indicate that for the period covered by this study, limits on loan-to-value achieved their financial stability objectives in Korea in terms of limiting credit and house price appreciation under an inflation-targeting regime. Furthermore, it attained these objectives without posing any threat to its price stability objective. Overall, these findings suggest that limits on loan-to-value have important aggregate consequences despite it being a sectoral, targeted policy instrument.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source Raw data obtained from Alam et al. (2019)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. A range of capital tools, including dynamic provisioning requirements, countercyclical capital buffer and time-varying leverage ratio caps are considered broad-based tools, while sectoral capital requirements, caps on foreign currency loans to corporates, limits on loan-to-value, debt-service-to-income ratio are some of the examples of these sectoral tools (IMF-FSB-BIS 2016).

  2. A recent study which addresses this limitation is by Richter et al. (2019). This study specifically measures the effects of changes in the maximum loan-to-value ratio on output and inflation, among others, for a panel of 56 economies, both from advanced and emerging economies using quarterly data from 1990Q1 to 2012Q2.

  3. In this study, Korea refers to the Republic of Korea.

  4. For similar arguments, see, for instance, Kim and Mehrotra (2018) and Glocker and Towbin (2015).

  5. Under the Bank of Korea Act, which took effect in 2011, the country’s central bank was also provided a mandate to pursue financial stability objectives. See, for instance, the discussion in Shin et al. (2017).

  6. Jung and Lee (2017) provide the dates of the introduction by countries of limits on LTV based on IMF data. According to this study, there are only three other countries that have a longer history than Korea in the implementation of such limits, namely, Hong Kong (1991), Singapore (1996) and Colombia (1999). Korea introduced theirs in 2002.

  7. Richter et al. (2019) is another study which veers away from the use of dummy variables. In their study, to quantify the effects of LTV limits, they extended the database for policy actions on housing markets constructed by Shim et al. (2013), and then use the changes in the maximum LTV ratios.

  8. For instance, at the very end of their paper, Goodhart and Hofmann (2008) made this conclusion, “A more rigorous and theoretical analysis of the role of the level of LTVs for house price and monetary dynamics and their interactions would, in our view, be a fruitful avenue for future research (p. 203).”.

  9. There is also the Korea Deposit Insurance Corporation (KDIC) but this agency is primarily responsible for managing and operating the deposit insurance funds and resolving ailing institutions.

  10. These designated speculative zones or areas tend to change over time but for most times cover Seoul and its surrounding metropolitan areas.

  11. The area of Gangbuk in Seoul and the neighboring area of Incheon were also designated as speculative zones during this period.

  12. These three areas were Seocho, Gangnam and Songpa.

  13. A similar empirical strategy was pursued by Kim and Mehrotra (2018, 2019) of not including simultaneously in the VAR specification real credit and house price.

  14. We follow the large body of literature that determines the effects of monetary policy by including all variables in levels (e.g., Christiano et al. 1999).

  15. Also referred to in the VAR literature as identifying assumptions or restrictions.

  16. For instance, Canova and Pappa (2007), Mountford and Uhlig (2009), Pappa (2009), Caldara and Kamps (2017) use sign-restricted VARs to study fiscal policy shocks; Dedola and Neri (2007) and Peersman and Straub (2009) also used sign-restricted VARs to study technology shocks; and, sign-restricted VARs were used by Baumeister and Peersman (2013), Kilian and Lee (2014), Kilian and Murphy (2012, 2014) and Lippi and Nobili (2012) to study oil price shocks.

  17. Some other VAR-based studies have left unrestricted, the response of the central bank to an aggregate supply shock. For example, Eickmeier et al. (2009), Duchi and Elbourne (2016), Bijsterbosch and Falagiarda (2015), Finlay and Jääskelä (2014).

  18. The succeeding discussion on obtaining the posterior mode of the joint distribution of the accepted set of models draws from Kilian and Lütkepohl (2017).

  19. The superscript letter r stands for a particular random draw.

  20. This also follows from Kilian and Lütkepohl (2017).

  21. In implementing Eqs. (3)–(5) above to characterize the central tendency of the structural impulse response functions in a sign-restricted VAR according to Inoue and Kilian (2013), we use the original codes provided by Lutz Kilian in his website at: https://sites.google.com/site/lkilian2019/research/code. The original codes use both sign and magnitude restrictions to study the global oil market in a three-variable VAR. Portions of the codes were modified to implement a pure sign restrictions approach for this study. We gratefully acknowledge him for making the original codes available.

  22. Using also quarterly data, this is almost the same period covered by Tillmann (2015) and Jung et al. (2017). See the discussion in section II above.

  23. In contrast, Ardakani et al. (2018) posit a different start date of 1998Q2.

  24. For a similar strategy, see, for instance, Kim and Mehrotra (2018, 2019).

  25. See Appendix I, Table 4 of Alam et al. (2019) which provide a comprehensive comparison of their database to other existing databases on macroprudential policies.

  26. For brevity and to save space, the impulse responses of the remaining structural shocks are available upon request.

  27. This description of a shotgun pattern in the credible sets was coined by Inoue and Kilian (2013).

  28. We are thankful to a referee for this suggestion.

  29. We did not extend beyond a horizon of one year as the structural impulse responses are imprecisely estimated beyond this point. Glocker and Towbin (2015) also reported their estimates over the first 12 months.

  30. The impulse responses of the remaining structural shocks are again available upon request.

  31. Also refer to the Appendix for further details.

  32. The impulse responses of the remaining structural shocks are also available upon request.

  33. While the DSGE model of Angeloni et al. (2003) shows that investment reacts more strongly than consumption to a contractionary monetary policy, to the best of our knowledge, we are not aware of a similar model that provides for the relative sensitivities in consumption and investment to a contractionary LTV ratio.

  34. The impulse response results are available upon request.

  35. The impulse response results of this sub-section are also available upon request.

  36. To elaborate, in terms of Table 1a, having only two shocks identified means that the aggregate demand and supply shocks are now omitted, and the entries for each variable in the two pertinent rows in the table are left unrestricted.

  37. We note though that perhaps the disadvantage with a partial identification of having only two shocks identified in the structural model is that, by far, a lot of the draws in this part of the results were accepted. There were, in fact, 68,959 accepted draws that form the corresponding 68% credible sets of the impulse responses. See the discussion in Uhlig (2017) on having fewer as opposed to having a lot of retained draws in sign-restricted VARs is the appropriate result.

  38. These results are also available upon request.

  39. The impulse response results are available upon request.

  40. The impulse response results of this sub-section are available upon request.

  41. There were 10,220 accepted draws that form the corresponding 68% credible sets of the impulse responses, which are again numerous.

  42. These results are also available upon request.

  43. This ordering is similar to the one used in one of the robustness tests of Kim and Mehrotra (2018, 2019), although in this particular country-panel study an index of macroprudential policy was used rather than the LTV ratio.

  44. We obtain similar results when real bank credit and real house price are not simultaneously included in the VAR specification.

  45. Caution is needed when comparing the contractionary LTV ratio shock from the baseline to the one produced by the recursive ordering of an expansionary LTV ratio shock, unless one is willing to assume symmetry between expansionary and contractionary LTV ratio shocks. Nevertheless, in an earlier working paper version of this study where a sign-restricted expansionary LTV ratio shock was considered, similar results were obtained when compared to the responses of real output and real house price in the above Cholesky decomposition.

  46. The other suggested ways were, namely, to bring in additional weakly identifying information, use fully identifying assumption, and combining the approaches. The reason for the choice of reporting the identified set was that this was the most feasible approach for this study. For further details on these alternative approaches, refer to Baumeister and Hamilton (2020).

  47. Again, similar interpretation goes for the responses of the price level to a contractionary monetary policy shock, and the LTV ratio to a contractionary LTV ratio shock.

References

  • Aikman D, Bush O, Taylor A (2018) Monetary versus macroprudential policies: causal impacts of interest rates and credit controls in the era of the UK Radcliffe report. NBER Working Paper Series No 22380, National Bureau of Economic Research, Cambridge MA

  • Alam Z, Alter A, Eiseman J, Gelos G, Kang H, Narita M, Nier E, Wang N (2019) Digging deeper-Evidence on the effects of macroprudential policies from a new database. IMF Working Paper Series, International Monetary Fund

  • Alpanda S, Zubairy S (2017) Addressing household indebtedness: monetary, fiscal or macroprudential policy? Eur Econ Rev 92:47–73

    Article  Google Scholar 

  • Amir-Ahmadi P, Matthes C, Wang M (2016) Drifts and volatilities under measurement error: assessing monetary policy shocks over the last century. Quant Econ 7:591–611

    Article  Google Scholar 

  • Angeloni I, Kashyap A, Mojon B, Terlizzese D (2003) The output composition puzzle: a difference in the monetary transmission mechanism in the Euro Area and U.S. J Money Credit Bank 35:1265–1306

    Article  Google Scholar 

  • Antolin-Diaz J, Rubi-Ramirez J (2018) Narrative sign restrictions for SVARS. Am Econ Rev 108(10):2802–2829

    Article  Google Scholar 

  • Ardakani O, Kishor K, Song S (2018) Re-evaluating the effectiveness of inflation targeting. J Econ Dyn Control 90:76–97

    Article  Google Scholar 

  • Arias J, Caldara D, Rubio-Ramirez J (2019) The systematic component of monetary policy in SVARs: an agnostic identification procedure. J Money Econ 101:1–13

    Article  Google Scholar 

  • Basto R, Gomes S, Lima D (2019) Exploring the implications of different loan-to-value macroprudential policy designs. J Policy Model 41(1):66–83

    Article  Google Scholar 

  • Baumeister C, Hamilton J (2020) Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions. NBER Working Paper Series No 26606, National Bureau of Economic Research, Cambridge MA

  • Baumeister C, Peersman G (2013) The role of time-varying price elasticities in accounting for volatility changes in the crude oil market. J Appl Econom 28(7):1087–1109

    Article  Google Scholar 

  • Bijsterbosch M, Falagiarda M (2015) The macroeconomic impact of financial fragmentation in the euro area: which role for credit supply? J Int Money Financ 54:93–115

    Article  Google Scholar 

  • Blanchard O, Watson M (1986) Are all business cycles alike? In: Gordon R (ed) The American Business Cycle. University of Chicago Press, Chicago, pp 123–160

    Google Scholar 

  • Boeckx J, Dossche M, Peersman G (2017) Effectiveness and transmission of the ECB’s balance sheet policies. Int J Cent Bank 13(1):297–333

    Google Scholar 

  • Breitenlechner M, Scharler J, Sindermann F (2016) Banks’ external financing costs and the bank lending channel: results from a SVAR analysis. J Financ Stab 26:228–246

    Article  Google Scholar 

  • Buch C, Eickmeier S, Prieto E (2014) Macroeconomic factors and microlevel bank behavior. J Money Credit Bank 46(4):715–751

    Article  Google Scholar 

  • Caldara D, Kamps C (2017) The analytics of SVARs: a unified framework to measure multipliers. Rev Econ Stud 84(3):1015–1040

    Article  Google Scholar 

  • Canova F, De Nicolo G (2002) Monetary disturbances matter for business fluctuations in the G-7. J Monet Econ 49:1131–1159

    Article  Google Scholar 

  • Canova F, De Nicolo G (2003) On the sources of business cycles in the G-7. J Int Econ 59(1):77–100

    Article  Google Scholar 

  • Canova F, Pappa E (2007) Price dispersions in monetary unions: the role of fiscal shocks. Econ J 117:713–737

    Article  Google Scholar 

  • Canova F, Paustian M (2011) Business cycle measurement with some theory. J Money Econ 58:345–361

    Article  Google Scholar 

  • Carreras O, Davis P, Piggott R (2018) Assessing macroprudential tools in OECD countries within a cointegration framework. J Financ Stab 37:112–130

    Article  Google Scholar 

  • Cecchetti SG, Rich RW (2001) Structural estimates of the US sacrifice ratio. J Bus Econ Stat 19(4):416–427

    Article  Google Scholar 

  • Cerutti E, Claessens S, Laeven L (2017) The use and effectiveness of macroprudential policies: new evidence. J Financ Stab 28:203–224

    Article  Google Scholar 

  • Christiano LJ, Eichenbaum M, Evans C (1999) Monetary policy shocks: What have we learned and to what end? In: Woodford M, Taylor J (eds) Handbook of macroeconomics, vol 1. Elsevier Science, New York, pp 65–148

    Chapter  Google Scholar 

  • Dedola N, Neri S (2007) What does a technology shock do? A VAR analysis with model-based sign restrictions. J Monet Econ 54:512–549

    Article  Google Scholar 

  • Del Negro M, Otrok C (2007) ’99 Luftballons: monetary policy and the house price boom across US states. J Monet Econ 54(7):1962–1985

    Article  Google Scholar 

  • Duchi F, Elbourne A (2016) Credit supply shock. J Macroecon 50:51–71

    Article  Google Scholar 

  • Eickmeier S, Hofmann B, Worms A (2009) Macroeconomic fluctuations and bank lending: evidence for Germany and the Euro Area. Ger Econ Rev 10(2):193–223

    Article  Google Scholar 

  • Ellis C, Mumtaz H, Zabczyk P (2014) What lies Beneath? A time-varying FAVAR model for the UK transmission mechanism. Econ J 124:668–699

    Article  Google Scholar 

  • Faust J (1998) The robustness of identified VAR conclusions about money. Carnegie-Rochester Conf Ser Publ Policy 49:207–244

    Article  Google Scholar 

  • Fendoglu S (2017) Credit cycles and capital flows: effectiveness of the macroprudential policy framework in emerging market economies. J Bank Financ 79:110–128

    Article  Google Scholar 

  • Financial Stability Board (2017) Peer review of Korea

  • Finlay R, Jääskelä J (2014) Credit supply shocks and the global financial crisis in three small open economies. J Macroecon 40:270–276

    Article  Google Scholar 

  • Fouejieu A (2017) Inflation targeting and financial stability in emerging markets. Econ Model 60:51–70

    Article  Google Scholar 

  • Fry R, Pagan A (2011) Sign restrictions in structural vector autoregressions: a critical review. J Econ Lit 49(4):938–960

    Article  Google Scholar 

  • Galati G, Moessner R (2018) What do we know about the effects of macroprudential policy? Economica 85(340):735–770

    Article  Google Scholar 

  • Gambacorta L, Hofmann B, Peersman G (2014) The effectiveness of unconventional monetary policy at the zero lower bound: a cross-country analysis. J Money Credit Bank 46(4):615–642

    Article  Google Scholar 

  • Georgiadis G (2015) Examining asymmetries in the transmission of monetary policy in the euro area: evidence from a mixed cross-section global VAR model. Eur Econ Rev 75:195–215

    Article  Google Scholar 

  • Glocker C, Towbin P (2015) Reserve requirements as a macroprudential instrument-empirical evidence from Brazil. J Macroecon 44(158):176

    Google Scholar 

  • Goodhart C, Hofmann B (2008) House prices, money, credit, and the macroeconomy. Oxf Rev Econ Policy 24(1):180–205

    Article  Google Scholar 

  • Greenwood-Nimmo M, Tarassow A (2016) Monetary policy shocks, macroprudential shocks and financial stability. Econ Model 56:11–24

    Article  Google Scholar 

  • Hristov N, Hülsewig O, Wollmershäuser T (2012) Loan supply shocks during the financial crisis: evidence for the Euro Area. J Int Money Financ 31:569–592

    Article  Google Scholar 

  • Igan D, Kang H (2011) Do loan-to-value and debt -to-income limits work? Evidence from Korea. IMF Working Paper Series, International Monetary Fund

  • IMF-FSB-BIS (2016) Elements of effective macroprudential policies: lessons from international experience. Mimeo

  • in’t Veld J, Kollmann R, Pataracchia B, Ratto M, Roeger W (2014) International capital flows and the boom-bust cycle in Spain. J Int Money Financ 48:314–335

    Article  Google Scholar 

  • Inoue A, Kilian L (2013) Inference on impulse response functions in structural VAR models. J Econom 177:1–13

    Article  Google Scholar 

  • International Monetary Fund (2013) The interaction of monetary and macroprudential policies. International Monetary Fund

  • Jarociński M (2010) Responses to monetary policy shocks in the east and the west of Europe: a comparison. J Appl Econom 25:833–868

    Article  Google Scholar 

  • Jarociński M, Smets FR (2008) House prices and the stance of monetary policy. Fed Reserv Bank St. Louis Rev 90(4):339–365

    Google Scholar 

  • Jeanneau S (2014) Financial stability objectives and arrangements—What’s new? BIS Papers No 76. Bank for International Settlements

  • Jung H, Lee J (2017) The effects of macroprudential policies on house prices: evidence from an event study using Korea real transaction data. J Financ Stab 31:167–185

    Article  Google Scholar 

  • Jung Y, Kim S, Yang DY (2017) Optimal macroprudential policies and house prices in Korea. Emerg Mark Financ Trade 53:2419–2439

    Article  Google Scholar 

  • Kilian L, Lee TK (2014) Quantifying the speculative component in the real price of oil: the role of global oil inventories. J Int Money Finance 42:71–87

    Article  Google Scholar 

  • Kilian L, Lütkepohl H (2017) Structural vector autoregressive analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kilian L, Murphy D (2012) Why agnostic sign restrictions are not enough: understanding the dynamics of oil market VAR models. J Eur Econ Assoc 10:1166–1188

    Article  Google Scholar 

  • Kilian L, Murphy D (2014) The role of inventories and speculative trading in the global market for crude oil. J Appl Econom 29:454–478

    Article  Google Scholar 

  • Kim C (2014) Korea’s experiences with macroprudential policy. In: Akerlof G, Blanchard O, Romer D, Stiglitz J (eds) What have we learned? Macroeconomic policy after the crisis. IMF and MIT, Cambridge, pp 99–126

    Google Scholar 

  • Kim S, Mehrotra A (2017) Managing price and financial stability objectives in inflation targeting economies in Asia and the Pacific. J Financ Stab 29:106–116

    Article  Google Scholar 

  • Kim S, Mehrotra A (2018) Effects of monetary and macroprudential policies-evidence from four inflation targeting economies. J Money Credit Bank 50(5):967–992

    Article  Google Scholar 

  • Kim S, Mehrotra A (2019) Examining macroprudential policy and its macroeconomic effects—some new evidence. BIS Working Papers No 825. Bank for International Settlements

  • Kuttner K, Shim I (2016) Can non-interest rate policies stabilize housing markets? Evidence from a panel of 57 economies. J Financ Stab 26:31–44

    Article  Google Scholar 

  • Lee JK (2013) The operation of macro prudential policy measures: The case of Korea in the 2000s. In: Canuto O, Ghosh S (eds) Dealing with the challenges of macro financial linkages in emerging markets. World Bank, Washington DC, pp 227–279

    Chapter  Google Scholar 

  • Lippi F, Nobili A (2012) Oil and the macroeconomy: a quantitative structural analysis. J Eur Econ Assoc 10:1059–1083

    Article  Google Scholar 

  • Mallick S, Sousa R (2013) The real effects of financial stress in the Eurozone. Int Rev Financ Anal 30:1–17

    Article  Google Scholar 

  • Malovaná S, Frait J (2017) Monetary policy and macroprudential policy: rivals or teammates? J Financ Stab 32:1–16

    Article  Google Scholar 

  • Mishkin F (2012) Central banking after the crisis. Mimeo

  • Morgan P, Regis P, Salike N (2019) LTV policy as a macro-prudential tool: the case of residential mortgage loans. J Financ Intermed 37:89–103

    Article  Google Scholar 

  • Mountford A (2005) Leaning into the wind: a structural VAR investigation of UK monetary. Policy 67(5):597–621

    Google Scholar 

  • Mountford A, Uhlig H (2009) What are the effects of fiscal policy shocks? J Appl Econom 24:960–992

    Article  Google Scholar 

  • Pappa E (2009) The effects of fiscal shocks on employment and real wages. Int Econ Rev 50:217–244

    Article  Google Scholar 

  • Peersman G (2005) What caused the early millennium slowdown? Evidence based on vector autoregressions. J Appl Econom 20:185–207

    Article  Google Scholar 

  • Peersman G, Straub R (2006) Putting the new Keynesian model to a test. IMF Working Paper Series, International Monetary Fund

  • Peersman G, Straub R (2009) Technology shocks and robust sign restrictions in a Euro area SVAR. Int Econ Rev 50:727–750

    Article  Google Scholar 

  • Richter B, Schularick M, Shim I (2019) The costs of macroprudential policy. J Int Econ 118:263–282

    Article  Google Scholar 

  • Robstad O (2018) House prices, credit and the effect of monetary policy in Norway: evidence from structural VAR models. Empir Econom 54:461–483

    Article  Google Scholar 

  • Sá F, Towbin P, Wieladek T (2014) Low interest rates and housing booms: capital inflows, monetary policy and the effects of financial innovation. J Eur Econ Assoc 12(2):522–546

    Article  Google Scholar 

  • Schenkelberg H, Watzka S (2013) Real effects of quantitative easing at the zero lower bound: structural VAR-based evidence from Japan. J Int Money Financ 33:327–357

    Article  Google Scholar 

  • Shim I, Bogdanova B, Shek J, Subelyte A (2013) A database for policy actions on housing markets. BIS Quarterly Review September 2013, Bank for International Settlements

  • Sims CA (1980) Macroeconomics and reality. Econometrica 48:1–48

    Article  Google Scholar 

  • Sims CA (1992) Interpreting the macroeconomic time series facts: the effects of monetary policy. Eur Econ Rev 36(5):975–1000

    Article  Google Scholar 

  • Sims CA (1998) Comment on Glenn Rudebusch’s Do measures of monetary policy in a VAR make sense? Int Econ Rev 39(4):933–941

    Article  Google Scholar 

  • Sims CA, Zha T (1999) Error bands for impulse responses. Econometrica 67:1113–1155

    Article  Google Scholar 

  • Sonoda K, Sudo N (2015) Is macroprudential policy instrument blunt? Bank of Japan Working Paper Series No 15-E-11, Bank of Japan

  • Tillmann P (2015) Estimating the effects of macroprudential policy shocks: a Qual VAR approach. Econ Lett 135:1–4

    Article  Google Scholar 

  • Uhlig H (2005) What are the effects of monetary policy on output? Results from an agnostic identification. J Monet Econ 52:381–419

    Article  Google Scholar 

  • Uhlig H (2017) Shocks, signs, restrictions, and identification. In: Honor B, Pakes A, Piazzesi M, Samuelson L (eds) Advances in economics and econometrics, vol 1 and 2. Eleventh World Congress. Cambridge University Press, Cambridge, pp 95–127

    Chapter  Google Scholar 

  • Ume E (2018) The impact of monetary policy on housing market activity: an assessment using sign restrictions. Econ Model 68:23–31

    Article  Google Scholar 

  • Vargas-Silva C (2008) Monetary policy and the US housing market: a VAR analysis imposing sign restrictions. J Macroecon 39:977–990

    Article  Google Scholar 

  • Zha T (1997) Identifying monetary policy: a primer. Fed Reserv Bank Atlanta Econ Rev 82(2):26–43

    Google Scholar 

Download references

Acknowledgements

The author would like to thank the Coordinating Editor and two anonymous referees for comments and suggestions received as well as participants to the High-Level Seminar for Directors of Research and Monetary Policy of SEACEN member central banks held on 4–5 July 2019 in Bali, Indonesia, and the conference on Macroeconomic Stabilization in the Digital Age jointly sponsored by the Sim Kee Boon Institute for Financial Economics, Singapore Management University (SMU) and the Asian Development Bank (ADB) Institute held on 16–17 October 2019 in Singapore, where earlier versions of this study were presented. The author would also like to thank Seow Yun Yee for help in editing the manuscript as well as Ole Rummel and Ozer Karagedikli for suggestions received on an earlier version of this study. The views expressed herein are solely those of the author and do not necessarily reflect the views of CAMA-ANU, Globalization Institute, The SEACEN Centre and its member central banks.

Author information

Authors and Affiliations

  1. The South East Asian Central Banks (SEACEN) Research and Training Centre, Kuala Lumpur, Malaysia

    Victor Pontines

  2. Centre for Applied Macroeconomic Analysis (CAMA), Australian National University (ANU), Canberra, Australia

    Victor Pontines

  3. Globalization Institute, Federal Reserve Bank of Dallas, Dallas, TX, USA

    Victor Pontines

Authors
  1. Victor Pontines
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Victor Pontines.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Human and animals participants

This article does not contain any studies with human participants or animals performed by the author.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Calculation of the sacrifice ratio

Appendix: Calculation of the sacrifice ratio

Based on the modal model in Sect. 6.1.1, an estimate of the sacrifice ratio can be computed similar to Cecchetti and Rich (2001). Take for instance, the calculation of the cumulative output loss from a 1% reduction in bank credit achieved through a contractionary monetary policy (mp). We need two effects to calculate this ratio. The first effect measures the cumulative effect on output resulting from a contractionary monetary policy shock. This forms the numerator of the ratio. The other effect measures the effect of a contractionary monetary policy shock on bank credit. This then forms the denominator of this ratio. Taken together, the relative impact of monetary policy on output and bank credit, that is our “sacrifice ratio”, over a certain time horizon τ is just the ratio of these two effects which is expressed as:

$$ {\text{SR}}_{{\varepsilon^{mp} }} (t) = \left( {\mathop \sum \limits_{j = 0}^{\tau } \left( {\frac{{\partial y_{t + j} }}{{\partial \varepsilon_{t}^{mp} }}} \right)} \right)/\left( {\frac{{\partial {\text{credit}}_{t + j} }}{{\partial \varepsilon_{t}^{mp} }}} \right) $$

where y is real output (in log) and credit is real bank credit (also in log). The sacrifice ratio from a contractionary LTV ratio shock can be computed in a similar way. We also calculate in Sect. 6.2.2, the cumulative output loss corresponding to a 1% reduction in house price for a horizon of one year arising from a contractionary monetary policy shock and a contractionary LTV ratio shock.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pontines, V. The real effects of loan-to-value limits: empirical evidence from Korea. Empir Econ 61, 1311–1350 (2021). https://doi.org/10.1007/s00181-020-01908-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-020-01908-1

Keywords

JEL Classification

Search

Navigation