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RBA Glossary definition for SOEs

SOEs – state owned enterprises

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Identifying Interbank Loans from Payments Data

8 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
So these novel features may also be useful for identifying overnight interbank loans in other countries.
https://www.rba.gov.au/publications/rdp/2016/2016-11.html

The Algorithm

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
were actually loans but were instead randomly distributed (so we can't statistically rule out the possibility that they are all false positives). ... The horizontal axis uses a base 10 logarithm scale, so the principal values of the loans at 6 are $1
https://www.rba.gov.au/publications/rdp/2016/2016-11/algorithm.html

Appendix B: Detailed Description of the Algorithm

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
So only the loan set associated with the first match processed by the algorithm is kept (this choice is arbitrary). ... So the ‘potential outstanding’ array has identical dimensions to the array created in Step 1.
https://www.rba.gov.au/publications/rdp/2016/2016-11/appendix-b.html

Identifying Interbank Loans from Payments Data

1 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
So the 16.45 batch was not expected to further delay market activity. ... declined so dramatically since 2009 while non-rolled loans have remained broadly stable (Figure 9).
https://www.rba.gov.au/publications/rdp/2016/2016-11/full.html

Features of the Market

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
So the 16.45 batch was not expected to further delay market activity. ... So it may be a feature of rollovers that has led to the fall.
https://www.rba.gov.au/publications/rdp/2016/2016-11/features-of-the-market.html

Introduction

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
So this market represents the first step in the transmission of monetary policy to the rest of the economy, meaning its smooth operation is critical for the financial and economic system. ... So the novel features of our algorithm may also be useful for
https://www.rba.gov.au/publications/rdp/2016/2016-11/introduction.html

Appendix D: Derivation of the Model

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
So, in the limit, the OLS estimator of the constant net share (. ) will equal the mean of the net share plus a bias term caused by any correlation between non-rolled
https://www.rba.gov.au/publications/rdp/2016/2016-11/appendix-d.html

Appendix A: Literature Review

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
Moreover, some banks may transact on behalf of other client banks, so some of the false positives found by Armantier and Copeland may actually be loans but are assigned to the
https://www.rba.gov.au/publications/rdp/2016/2016-11/appendix-a.html

Accuracy of the Algorithm

15 Dec 2016 RDP 2016-11
Anthony Brassil, Helen Hughson and Mark McManus
With IBOC loans almost always occurring at the target cash rate, we are able to set our implied interest rate range to 0 basis points (around the target cash rate), so
https://www.rba.gov.au/publications/rdp/2016/2016-11/accuracy-of-the-algorithm.html