The first price, the repo rate, is the interest rate the borrower must pay, depending on the amount of the loan. For the monetary repo rate, Dawra (2014) argues that the repo rate is the cost of a central bank borrowing money from the domestic financial system. The second price is a haircut, a percentage difference between the money received and the market value (eg, the pure price of a bond) of the exchanged securities (Baklanova et al., 2019).
The annual trading volumes of government bonds, reverse repos and the outstanding ratio of government bonds to reverse repos have increased over the years. 2 The expected shortfall is the average loss value that depends on the loss exceeding VaR (Chebotarev, 2021). 4 A bond's capital gain is the gain or loss of its clean price/net present value (NPV).
The variable EXRATE is the US dollar spot rate against the local currency, which reflects the flow of capital flows. There are two simulations, "HR" and "non-HR." the simulation shows the shear differences aftershocks between "HR" and "non-HR". The central bank (lender) sets the α-percentile, in which the bank (borrower) sacrifices part of the loan, which the central bank holds as a HK.
The central bank can choose a haircut depending on the data tightness and the most prominent historical loss.
RESULTS AND DISCUSSION
This table shows the collateral, their issuers, the number of bond series and the issuer class. Source: Bloomberg. This table shows the GARCH(1,1) regression output for repo maturities consisting of one week, two weeks, one month, and three months. The event related to the Malaysian entity occurs on the one week maturity in maturity bucket 7_10.
Hong Kong has an alternative event (see section 3.3.2) with three-month maturity and maturity bucket 0_1. This study revealed an alternative adverse event in the normal and high-risk condition of the Indonesian entity. In the first robustness test, we compare the regression coefficients for normal (“non-HR”) and high-risk (“HR”) economic conditions.
The AR(1) interaction with 'HR' and 'non-HR' shows significant results with 19 results during 'non-HR' and seven regressions during. In the final test, equation 3 is robust because the haircut models of PLN Indonesia and Hong Kong government bonds are similar to those of the governments of Indonesia, Thailand and Malaysia, but without EXRATE determinants. The table shows the haircuts under high-risk (“HR”), normal (“non-HR” conditions, in square brackets), and the spread between high-risk and normal conditions (with braces).
We attach a cross sign (†) when the shearing motion under normal conditions ("non-HR") is higher than that under high risk ("HR"). The interaction behavior is consistent with Amato (2005) and Lizarazo (2013) that risk-averse investors make liquid bonds only during “non-HR”. In contrast, the table also shows that the interaction of EXRATE and "HR" during "non-HR" yields higher haircuts than "HR". This behavior is consistent with Rafi and Ramachandran (2018), Grigorian (2019) and Dou and Verdelhan (2015). 5% shocks to long-memory returns/capital gains and exchange rates cause a small difference, less than ±1%, in the haircuts between high and non-high risk.
This table shows the results of the implied risk hypothesis test for all maturity bands. Horizontally, at α=1%, 5% and 10%, the longer the maturity segment or repo maturity, the higher the haircut (hypothesis test in Tables 5 and 6). Most government haircuts increase as the maturity segment and maturity of repos increase, but corporate haircuts do not have this phenomenon of repo maturity.
CONCLUSION AND IMPLICATIONS
The vertical relationship within an issuer; the smaller the α, the higher the haircut or collateral loss (see Figure 2). The haircut for all government bonds is 12.55% and for corporate bonds it is 9.929%, meaning the haircut is the only absorption of market risk. In the first method, the haircut regressions show that long-term returns/capital gains, liquidity and exchange rate are typically critical in repo haircuts in Indonesia, Malaysia, Thailand and Hong Kong.
We demonstrate that the negative-returns model is robust and given a 5% shock to long-memory returns and exchange rates, they change the haircut by less than one percent. In the second method, we establish the haircut model that the α percentile shares a borrower's risks if the collateral price falls; the lender bears the remainder if the borrower cannot repay. We prove that the historical and parametric VaR method can help assign the central bank's implicit risk tolerance (α) and establish haircuts of sovereign and corporate securities.
Testing long memory in exchange rates and its implications for the adaptive market hypothesis. This table shows the metadata of Indonesia, Malaysia, Thailand, Hong Kong and PLN fixed income securities and CDS (Source: Bloomberg). The virtual collateral frameworks of Bank Indonesia, Bank Negara Malaysia, Bank of Thailand and Hong Kong Monetary Authority.
The fixed haircuts across "security class" and "security maturity". The announcements of haircuts were from Bank Indonesia 2018, Bank Negara Malaysia 2019, Bank of Thailand 2017 and Hong Kong Monetary Authority 2020. RMB, USD and EUR-denominated debt instruments issued in offshore markets by (i) People's Bank of. China (CMOF); and (iii) the Policy Banks of the People's Republic of China (China Policy Banks), namely.
