In this short paper we outline a number of challenges implementing macroprudential policies and tools to prevent or mitigate risks to financial stability[1]. Firstly, we start by exploring our understanding of financial stability predominantly referencing excellent work by Armour et.al. (2016)[i]. Secondly, given the nature of our enquiries we look at macroprudential tools as currently utilized across global financial systems. Then, thirdly, our focus shifts to topics and challenges as related specifically to central banks. Here we discuss the importance of liquidity and the ever-elusive nature of central bank’s credibility as the designated lender of last resort, to maintain functional and operational financial system, hence financial stability. Lastly, we add practical examples of recent events and link these events to discussed theoretical challenges. In conclusion we argue that, yes, it is challenging to maintain smooth operation of financial systems, without encountering market failures or lack and/or full withdrawal of liquidity, hard restart of real economic activity or even having one market counterparty a.k.a called Central Bank dictating pricing and valuations of all liquid assets, in the name of financial stability. However, we believe that given recent advances in computing capabilities allowing us to simulate possible outcomes of various complex events we can start the journey of understanding and potentially designing more appropriate intelligent complex systems capable of self-control allowing true prevention or mitigation of risks to financial stability.
Financial Risk
Let us first establish the notion that it is virtually impossible for any institution or government or international body to entirely eliminate financial risks. Based on Hudson (2013), any investment activity involves risks and from financial institution perspective profitability and wealth generation is conditional on a game of speculative hazard[ii]. Maintaining this line of thought, let us look back in time to previous financial instabilities. Specifically, without venturing too far to the past, let us look at market events unfolding during March 2020, COVID crisis[2]. Given the well documented and recent timeline, the core market events and tremors were felt during this timeframe. As a negative external event, financial markets responded in a negative fashion as a collection of human interactions each attempting to value future discounted valuations of each tradable assets. We have seen almost instantaneous decline in asset valuations, drain of liquidity, move to safety and increased stress to financial stability. Closing of international borders, shutting down entire economies is inevitably going to lead to perceived negative future cashflows as discounted by each market participant. As such, it is rational for buyers to become non-existent in this situation and sellers to be forced to sell and liquidate their exposures when and if overleveraged and exposed to loan covenants and contractual obligations. Now, hypothetically, imagine we do not have a globally reliable and credible institutions, or any macroprudential mechanisms for that matter in place, that are in a position to step in and provide required liquidity and assurances to the marketplace to maintain financial stability and prevent or mitigate full market failure. In this case, the likelihood of full market failure is rather high with immediate liquidity drain and assets valuation declines spilling into “real” economy by market participants and general population economic withdrawal. Bordo (1990) outlined historical perspective providing detailed narration of market failures and crashes.[iii] On one hand, naturally the outcome of these events is going to eliminate financial risk from the financial system by eliminating overleveraged market participants and hence clearing and creating new asset market clearing prices. As such, the next time around, it is supposed that present market participants would not allow extensive exposure or overleverage and would be prudential in their risk management. On the other hand, the “real” economy hard reset is going to be reflected in a drastic employment drain and lowering of economic activities, similar to market crash of 1930s[3].
Given the nature of this specific external risk to the financial system, in this instance global pandemic scenario, at the moment, it is virtually impossible for any institution or government or international body to entirely eliminate related and resulting financial risks. However, the implementation of a number of macroprudential policies, institutions and tools allows us to prevent and/or mitigate these financial risks and maintain financial stability and a sense of continuation. Based on Bordo (1990), it is the introduction of the lender of last resort, among other macroprudential policies, that “…prevented panics on numerous occasions…”
Macroprudential Policies and Tools
We can now proceed and introduce number of macroprudential tools that can and are being used to prevent and/or mitigate financial risks. Ultimately, we focus on the role of central bank as the lender of last resort. Following Cranston (2017), financial systems “…transforms the size, maturity and risk characteristics of economic assets…”.[iv] In short, financial system forms a role of a mediator between short-term perspective of the savers and long-term perspective of the borrowers. Hence, one of the core challenges of any macroprudential policy is the dilemma of this short-to-long term asset-liability dichotomy. As we have indicated in the previous section, in case of an external negative event, the financial system by nature and design is likely going to lead to drastic failure and liquidity drain. Liquidity, as explained by Cranston (2017), is “concerned with cost and speed with which economic agents can convert financial instruments into purchasing power at agreed prices”. Hence the introduction of the lender of last resort as one of the macroprudential policies or tools. Before discussing the role of central banks in the financial eco-system let us first outline few other macroprudential policies to indicate and depict the fact that, financial system, as a complex human-designed framework is dealing with a multitude of moving, contradictory and dynamic parts.
Keller (2020) in his excellent treaty related to interdisciplinary perspective on macroprudential policy setting sets the scene for our clearer understanding of underlying challenges.[v] As per the author, there are three main sets of macroprudential tools used currently to address ever-present build-up of financial risks across global financial systems.[4] Namely, i) capital-based tools, ii) credit-related tools and iii) liquidity-related tools. In case of the capital-based tools, the focus is on addressing risks associated with credit boom and bust cycles. “These tools include, for instance, a countercyclical capital buffer (CCyB) which is designed to accumulate capital, when systemic risk builds up during the expansion phase of the financial cycle so that it can be released and used in the contraction phase when risks materialise.” Here credit-to-GDP measure is used as an indicator. In case of credit-related tools the focus is on risks associated with credit growth and asset price inflation. Few of the measures here are based on loan restrictions, such as caps on loan-to-value (LTV), debt-service-to-income (DSTI) or loan-to-income (LTI) ratios. Lastly, liquidity-related tools address systemic liquidity risks. Here, few measures being used as examples Liquidity Coverage Ratio (LCR) and caps on Loan-to-Deposit (LTD) ratios.
Given the defined macroprudential tools above, it is apparent that implementation of these policies and tools from a practical standpoint is hindered by data collection and aggregation across jurisdictions. Global financial institutions, and capital flows in general, are crossing these boundaries on a daily basis and hence the regulatory institutions are facing data collection and transparency challenges. Notwithstanding, the presence of these tools and for the sake of our argument, let us now assume that, yes, all of these measures and tools were in place during COVID crisis (March, 2020 and at present). In short, we postulate that global financial system was preforming well without any obvious accumulation of financial instabilities (derivatives-driven or otherwise) at this point. Given the severity of the shock to the financial and economic and health/insurance eco-systems we argue that it is the policies of central banks that introduced the solution, offering free cash and being the lender of last resort, as well as the “new” challenges. In short, we are yet to see the effects of the above policies in action as a result of loose monetary policies globally creating environment prone to high inflationary pressures with vastly overvalued economic assets, mostly driven by zero-interest rates based global monetary policies.
Central Bank Put Option
In the previous sections our argument led us to the notion that macroprudential policies and tools are critical in resolving post-effects of drastic market failure due to implicit, self-inflicted and/or explicit e.g. COVID pandemic events. We have argued that liquidity forms the core link between short term savers and long-term borrowers and it is the role of financial system to facilitate the provision of liquidity to enhance financial stability. At the same time we have noted that not having lender of last resort tool in place is likely leading the market to full failure by draining required liquidity from the system at the point when this is most required. Also, we have noted that there are technical challenges to the implementation of macroprudential policies and tools, such as transparency and accountability of each market participants to be able to provide relevant data aggregations and required measurements. However, we believe that it is the role of the largest market player, Central Bank, that is the core challenge in any macroprudential implementation.
Let us continue in our previous hypothetical narration, and now we assume we do have all macroprudential tools, as per Keller (2020) available, inclusive of the lender of last resort. We encounter COVID pandemic-driven external shock to the financial system and given market participants knowledge of financial stability pre-observing this drastic market shock and the knowledge that any liquidity will be provided and replaced by the lender of last resort, the rational step is to lean towards the sense of continuation. In brief, market participants knowledge of the fact that Central Banks will step in to avoid market crash and relevant flow-on effects to “real” economy precluded these market participants from taking dramatic buy/sell decision that would be forced upon them by the nature of individual loan contracts or additional covenants. It is here that we seen the core challenge, the independence, accountability and credibility of any Central Bank and their narration to the market setting up expectations in terms of how much the most liquid asset going to be valued at[5]. So called Central Bank Put Option[6] sets psychological floor to financial risk and can potentially add to overall financial instability on one side. On the other side of the coin, Central Bank as one of the largest market participants on the global markets and the only one player able to control and disseminated newly printed and minted cash is the core challenge to any macroprudential endeavours. We argue, that it is the abundance of liquidity that is the issue here, free cash to maintain the “real” economy at full speed that is the issue here. It is only now that we can start to observe the outcomes of i) capital-based tools, ii) credit-related tools and iii) liquidity-related tools of macroprudential policies.
Let us push our hypothetical scenario further and assert that it is the abundance of liquidity in the system by provision of zero cost of borrowing, that is actually causing the overall overheating and inflationary pressure with overflow into the real economy via asset pricing and valuation spikes. For example, recent events observed in real estate markets globally or for instance as seen recently in Australia[vi]. It would be constructive to assess credit-to-GDP or caps on loan-to-value (LTV), debt-service-to-income (DSTI) or loan-to-income (LTI) ratios to gauge how deep the level of potential financial instability goes.
In conclusion we argue that, yes, it is challenging to maintain smooth operation of financial systems, without encountering market failures or lack and/or full withdrawal of liquidity, hard restart of real economic activity. We agree with Bordo (1990) of the importance of having a lender of last resort known to the marketplace with the proviso that there are strict conditions and full transparency with financial institution bankruptcy resolutions on the table. However, we also believe that given recent advances in computing capabilities allowing us to simulate possible outcomes of various complex events we can start the journey of understanding and potentially designing more appropriate intelligent complex systems capable of self-control allowing true prevention or mitigation of risks to financial stability[7]. We are driven to ask whether Artificial Intelligence designed systems can add value in this journey? As per Keller (2020) part of the solution is in our ability to measure relevant outcomes and add to current qualitative narration more quantitative and measurable flavours. Perhaps, the approach of multidisciplinary studies across macroprudential spectrum is the way ahead.
References:
[1] Here we provide basic definition of financial stability: “Maintaining the stability of the financial system is a longstanding responsibility of the Reserve Bank. A stable financial system is one in which financial institutions, markets and market infrastructures facilitate the smooth flow of funds between savers and investors. This helps to promote growth in economic activity.” ref: https://www.rba.gov.au/fin-stability/ [2] COVID Timeline as per Hopkins Medicine Blog entries: https://www.hopkinsmedicine.org/news/articles/covid-19-timeline-for-johns-hopkins-medicine [3] Market crash of 1930s: https://www.britannica.com/event/stock-market-crash-of-1929 [4] Keller (2020), Chapter 6, pp.151-156. [5] Rather relevant CNBC interview with Mohamed El-Erian: The Fed should recognize it was caught 'massively offsides' - YouTube [6] Concise definition of Put Option: https://www.investopedia.com/terms/p/putoption.asp [7] AI designed neural network capable to simulate millions of combinations https://www.technologyreview.com/2020/05/05/1001142/ai-reinforcement-learning-simulate-economy-fairer-tax-policy-income-inequality-recession-pandemic/
[i] Armour J., Awrey D, Davies P, Enriques L, Gordon J.N., Mayer C, and Payne J., (2016), “Principles of Financial Regulation”, Oxford University Press [ii] Hudson A., (2013), “The Law of Finance”, Sweet & Maxwell [iii] Bordo M., D., (1990), “The Lender of Last Resort: Alternative Views and Historical Experience”, Economic Review, Jan/Feb [iv] Cranston R., (2017), “Principles of Banking Law”, Oxford University Press [v] Keller A., (2020), “Legal Foundations of Macroprudential Policy: An Interdisciplinary Approach”, Intersentia [vi] Sydney house price index
