1. International Funding Cost, Mortgage Rates and Cash Rate Cycle Relationship: Evidence in the Context of Australia Quynh Chau Pham Benjamin Liu Eduardo Roca Griffith Business School Department of Accounting Finance and Economics November 11, 2014 1

2. Contents: Introduction Related Literature & Hypotheses Data & Econometric Methods Result discussion Conclusion 2

3. 1. Introduction Mortgage rates Cash rate Domestic funding costs Offshore funding costs 3

4. Source: Retrieved from Leon Berkelmans and Andrew Duong (2014) 4

5. Source: Retrieved from Steward, Robertson and Heath (2013) 1.Introduction Banks’ wholesale Debt Share of total funding, 2011 Source: Retrieved from Leon Berkelmans and Andrew Duong (2014) 5

6. 1. Introduction Gap in the Literature In Australia, numerous studies on cash rate pass-through on mortgage rates (Karamujic, 2011; Lim, 2001; Smales, 2012; Valadkhani, 2013, 2014; Valadkhani & Anwar, 2012). Recently, several authors have analysed the trends in the funding and lending behaviour of Australian banks (Bailey et al. (2012), Deans & Stewart (2012), Stewart, Robertson & Heath (2013) and Berkelman & Duong (2014)). None of them has attempted to incorporate the impact of offshore funding costs on mortgage rates 6

7. 1 Lenders’ funding cycle differs from the cash rate cycle 2 Proliferated attention of policymakers, publicity towards the cash rate cycle; and lenders’ pricing behaviour, 3 The heavy reliance of Australian lenders on international financial markets, 4 The limitation in the scarce literature on influence of offshore funding costs on mortgage rates in Australian market. 1. Introduction Aim of the Study In particular, this study aims to answer two key questions: Whether international funding costs have affected mortgage rates? Has the effect of the cash-rate cycle on mortgage rates reduced by the increasing presence of offshore funding? Motivations of the study 7

8. Contributions Contributions of the study 1- the offshore funding cost influence on mortgage rate 2. Using both nominal & effective mortgage rates 3-ARDL approach application 1. Introduction 8

9. 2. Related Literature Cash rate pass- through An incomplete pass-through of base rate on mortgage rate in UK (Hofmann and Mizen (2004)), in Hong Kong and Singapore (Chong, 2010; Chong, Liu, & Shrestha, 2006), in NZ (Liu et al., 2008) In U.S, both complete and incomplete pass-through of FFR ((Payne, 2006a; Payne, 2006b, 2007) Cash rate pass- through Lowe (1995) find incomplete pass-through on mortgage rates Valadkhani & Anwar (2012), Valadkhani (2013) & (2014); Valadkhani & Bollen (2013) show complete make-up from cash rate to mortgage rates. Offshore funding cost impact Concerns on the increasing trend of offshore funding: e.g. Herald Sun, 2014). Bailey et al. (2012), Deans & Stewart (2012), Stewart, Robertson & Heath (2013) and Berkelman & Duong (2014) 9

10. 2. Research Hypotheses H01: The changes in cash rate still directly affect our sampled rates of the standard adjusted mortgages in both the short-term and the long term. H02: There are positive responses of the mortgage rates of the banks to changes in international funding costs H03: The effect of the cash-rate cycle on mortgage rates has been reduced by the increasing presence of offshore funding. 10

11. 3. Data and Econometric Methods 3.1 Sample and Data SampleNo. of Obs. Observations of standard adjustable mortgages6,850 No. of groups: 1. Whole banking sector 2. Major group 3. Foreign group 4. Regional group 4 Types of VariablesSources Dependent variables: Mortgage interest rates: 1. Nominal rate (WRATE) 2. Effective rate (WAAPR) Cannex’s survey of Australian lenders Independent variables: 1. Cash rate (CR)Table F01 of RBA database 2. International funding costs (AUDLIBOR) Australian spot exchange rate DataStream 11

12. 3 Data and Econometric Methods 3.2 Estimation methods – Unit root tests: ADF, PP and KPSS – The Autoregressive Distributed Lag (ARDL) model developed by Pesaran et al. (2001) – The Granger causality tests carried out under the VECM & VAR – The impulse response analysis (IRA) of VAR – The Dynamic Conditional Correlation (DCC) model developed by Engle (2002) 12

13. 3. Data and Econometric Methods 3.3 Empirical Models Notes: Y 1t is nominal mortgage rate (WRATE) Y 2t is effective mortgage rate (WAAPR) X 1 is cash rate (CR) X 2 is offshore funding costs (AUDLIBOR) 13

14. 3. Data and Econometric Methods Figure 1. Cash Rate, Mortgage Rate and AUDLIBOR Movements 14

15. 3. Data and Econometric Methods Table 2: Unconditional Correlations 15

16. 3. Data and Econometric Methods Table 3: Unit Root Test Result 16

17. 4. Empirical results 4.1. Long-term relationship Table 4: The ARDL test for Cointegration under bivariate system 17

18. 4. Empirical results 4.1. Long-term relationship Table 5: Granger noncausality test for a long-term relationship 18

19. 4. Empirical results 4.2. Short-term relationship Table 6: Granger noncausality test for a short-term relationship Equations Short-run causality (F-statistic) Causality decision X1X1 X2X2 Panel A: Whole sector Y18.018*** Y ← X 1 Y6.061*** Y ← X 2 Panel B: Major group Y2.648** Y ← X 1 Y3.009*** Y ← X 2 Panel C: Foreign group Y3.899*** Y ← X 1 Y0.244No Panel D: Regional group Y19.336***Y← X 1 Y6.111***Y← X 2 19

20. 4. Empirical results 4.3. Time Varying Relationships Figure 2. Dynamic Conditional Correlations of the whole banking sector 20

21. 5. Conclusion The ADRL, VEC & VAR granger causality, IRA test results determine: The cash rate still continues to drive mortgage rates (Hypothesis 1); International funding costs significantly affect mortgage rates both in long & short term (Hypothesis 2); The DCC test result confirms hypothesis 3: the linkage between the cash rate and mortgage rates has indeed weakened since 2006 21

22. Research Feedback Q & A 22