New Delhi [India], January 08: The global macro financial risks appear to be more challenging as flagged by the RBI’s December 2024 Financial Stability Report (FSR’Dec’24), which highlights three major challenges- high and rising levels of public debt, asset valuations and volatility and risks emanating from the artificial intelligence (AI) for the financial sector. The global public debt remains significantly at high level, which is estimated to go beyond US$ 100 trillion (i.e., 93 per cent of global GDP) by the end of 2024. The world’s two largest economies (viz., the U.S. and China) are the main drivers of this gush (this is expected to further outdo 100 per cent of GDP by 2030).
The RBI report emphasised further the risks of the “Unidentified Debt” that consists of “materialisation of contingent liabilities” and “fiscal risks” which stem largely from losses of state-owned enterprises as well as from bank recapitalisations and loan guarantees. These are typical features during banking crises and periods of financial stress. Other important sources include arrears, recognition of debt from institutional changes in the perimeter of government, and extrabudgetary spending.
Fiscal risk premia could increase severely leading to a spike in the cost of government debt and instability in government bond markets as witnessed in the September 2022 turmoil in the UK. The UK government’s “mini budget” announcement on September 23, 2022, resulted yields on UK government bonds soaring at a daily rate not seen since November 1988, drove the value of the pound to all-time lows. This crisis further forced some mortgage providers to suspend lending and lead the UK pension system to a liquidity crisis.
It is also worrisome that equity valuations remain high along with lower credit spread. High equity valuations and low credit spreads could be a source of weakness to financial stability, specifically when market expectations turn volatile. For instance, in Aug’24, global financial markets witnessed an unwind of leveraged carry trades, which were primarily funded using the Japanese Yen (JPY).
Another major challenge is stemming from the volatility of the crypto assets which rallied after the President-elect Donald Trump coming up with more power who is a staunch supporter of bitcoin and such assets. Bitcoin more than doubled during this calendar year and hit a record high of US$ 108,316 on December 17, 2024.
The IMF-FSB (2023), “IMF-FSB Synthesis Paper: Policies for Crypto-Assets”, September, highlighted that widespread adoption of crypto assets in countries could undermine the following:
- Effectiveness of monetary policy,
- Circumvent capital flow management measures,
- Exacerbate fiscal risks,
- Divert resources from financing the real economy.
The paper has further emphasised the following:
“The rapid growth of stablecoins denominated in foreign currencies in many emerging economies requires a careful understanding of risks. For example, many emerging economies lack effective legal and regulatory oversight. These economies also lack legal provisions for “bankruptcy remoteness,” meaning reserve assets can be commingled and not be secured if the issuer or its affiliates fail. This creates a situation where reserve assets tend to be managed by custodians located in advanced economies.”
The Financial Stability Board (FSB) in their October 2024 report has highlighted that the distributed ledger technology (DLT) based tokenisation can disrupt the financial stability in future. There can be various financial stability vulnerabilities associated with DLT-based tokenisation, which relate to liquidity and maturity mismatch; leverage; asset price and quality; interconnectedness; and operational fragilities.
The RBI Financial Stability Report (Dec’24) has emphasised also the potential challenges of the artificial intelligence (AI) regarding the financial stability. Since AI depends profoundly on the data, the inability to explain how these systems work could result in models using biased or less related data. These issues are particularly pertinent in the financial sector, especially in the banking industry, in which adoption of AI is rapidly growing.
