Macroprudential Policy: A Summary

The 2007 global financial crisis brought sharply into focus the need for macroprudential policy as a means of controlling systemic financial stability. This has become a focal point for policy-makers and numerous central banks, including the Bank of Canada, but it has its drawbacks, particularly here in Canada.As a counterbalance to microprudential policy, the idea of a macroprudential outlook reaches beyond the notion that as long as every banking institution is healthy, financial stability is assured. Macroprudential policy recognizes that all those financial institutions are linked, and that stability at the individual level may translate to fragility and uncertainty at the macro level.There are two approaches to macroprudential policy, and both come with downsides. One approach examines the network factor, in which banks are linked through their inter-connected financial transactions. A domino effect can thus be created; when one bank defaults, it causes a chain reaction down the line, creating instability in other banks in the network. The extent of this contagion of instability can be clearly observed through this model; unfortunately, it requires the use of detailed information typically available only to a limited circle of bank supervisors.The second approach gleans information from bank stock prices in a poorly performing market. This information is easily available and accessed, but the downside is the lack of clear understanding on how exactly these shocks travel through the complex links of the global banking system.Canada’s banking system is small and has only six major banks. However, it is important to understand how they are interconnected and how each individual bank can contribute to overall risk. Not only do banks need to be sufficiently capitalized in the normal business cycle, but it may be worthwhile for the sake of overall financial stability to create mechanisms, as regulators in some countries are doing, that require banks to hold more capital in good economic times so that they can use it as a buffer in case of a downturn. Another important macroprudential tool is to identify how much each bank contributes to systemic risk. This would entail identifying the banks that pose a greater threat to stability and having them hold extra capital. Assigning proper capital requirements is, however, not as straightforward as it may seem as the risk of the banking system changes when capital requirements change. One study has shown that when properly done such a requirement can reduce by one-quarter the probability of a financial crisis.Implementing macroprudential policy in Canada faces some challenges. With both housing prices and the level of Canadians’ personal debt high, sudden corrections to the financial system can create problems. Also, the interconnections between Canadian and foreign banks could result in the former being much more greatly influenced by financial-crisis spillover from the latter, something Canada generally avoided during the 2007 economic meltdown. There’s no consensus as yet on the objectives of macroprudential policy. However, it is a necessary complement to microprudential policy and provides a means of managing systemic risk with the goal of greater global financial stability

Macroprudential Policy: A Review

The severity and longevity of the recession caused by the 2007 financial crisis has highlighted the lack of a reliable macro-based financial regulation framework. As a consequence, addressing the link between the stability of the financial system as a whole and the performance of the overall economy has become a mandate for policymakers and scholars. Many countries have adopted macroprudential tools as policy responses for safeguarding the financial system. This paper provides a literature review of macroprudential policies, its objectives and the challenges that a macro-based framework needs to overcome, such as financial stability, procyclicality, and systemic risk

Spatial search via non-linear quantum walk

One approach to the development of quantum search algorithms is the quantum walk. A spatial search can be effected by the continuous-time evolution of a single quantum particle on a lattice or graph containing a marked site. In most conceivable physical applications, however, one would rather expect to have multiple interacting particles. In bosonic systems at zero temperature, the dynamics would be well-described by a discrete non-linear Schrodinger equation. In this thesis we investigate the role of non-linearity in determining the efficiency of the spatial search algorithm within the quantum walk model, for the complete graph. Our analytical results indicate that the search time for this non-linear quantum search scales with size of the database N like square root of N, equivalent to linear spatial search time. The analytical results will be compared with numerical calculations of multiple interacting quantum walkers

Essays on theory and computation in economics

The first chapter: I propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The curse of dimensionality is alleviated thanks to three techniques: (1) exploiting symmetry in the approximate law of motion and the policy function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; and (3) designing and training deep learning architectures that exploit symmetry and concentration of measure. As an application, I find a global solution of a multi-firm version of the classic Lucan and Prescott (1971) model of investment under uncertainty. First, I compare the solution against a linear-quadratic Gaussian version for benchmarking. Next, I solve the nonlinear version where no accurate or closed-form solution exists. Finally, I describe how this approach applies to a large class of models in economics. The second chapter: in the long run, we are all dead. Nonetheless, even when investigating short-run dynamics, models require boundary conditions on long-run, forward-looking behavior (e.g., transversality condition). In this chapter, in sequential setups, I show how deep learning approximations can automatically fulfill these conditions despite not directly calculating the steady state and balanced growth path. The main implication is that one can solve for transition dynamics with forward-looking agents, confident that long-run boundary conditions will implicitly discipline the short-run decisions, even converging towards the correct equilibria in cases with steady-state multiplicity. While this chapter analyzes benchmark models such as the neoclassical growth model, the results suggest deep learning may allow us to calculate accurate transition dynamics with high-dimensional state spaces, and without directly solving for long-run behavior. The third chapter: the sequential models studied in the previous chapter can be very useful to study deterministic setups and one-time shocks to economic variables. In this chapter I focus on the recursive setups. I consider the recursive version of the neoclassical growth model, which can be extended to study investment decisions under uncertainty. I show how deep learning approximations automatically fulfill these boundary conditions.Arts, Faculty ofVancouver School of EconomicsGraduat

Macroprudential Policy: A Review

The severity and longevity of the recession caused by the 2007 financial crisis has highlighted the lack of a reliable macro-based financial regulation framework. As a consequence, addressing the link between the stability of the financial system as a whole and the performance of the overall economy has become a mandate for policymakers and scholars. Many countries have adopted macroprudential tools as policy responses for safeguarding the financial system. This paper provides a literature review of macroprudential policies, its objectives and the challenges that a macro-based framework needs to overcome, such as financial stability, procyclicality, and systemic risk

Macroprudential Policy: A Summary

The 2007 global financial crisis brought sharply into focus the need for macroprudential policy as a means of controlling systemic financial stability. This has become a focal point for policy-makers and numerous central banks, including the Bank of Canada, but it has its drawbacks, particularly here in Canada. As a counterbalance to microprudential policy, the idea of a macroprudential outlook reaches beyond the notion that as long as every banking institution is healthy, financial stability is assured. Macroprudential policy recognizes that all those financial institutions are linked, and that stability at the individual level may translate to fragility and uncertainty at the macro level. There are two approaches to macroprudential policy, and both come with downsides. One approach examines the network factor, in which banks are linked through their inter-connected financial transactions. A domino effect can thus be created; when one bank defaults, it causes a chain reaction down the line, creating instability in other banks in the network. The extent of this contagion of instability can be clearly observed through this model; unfortunately, it requires the use of detailed information typically available only to a limited circle of bank supervisors. The second approach gleans information from bank stock prices in a poorly performing market. This information is easily available and accessed, but the downside is the lack of clear understanding on how exactly these shocks travel through the complex links of the global banking system. Canada’s banking system is small and has only six major banks. However, it is important to understand how they are interconnected and how each individual bank can contribute to overall risk. Not only do banks need to be sufficiently capitalized in the normal business cycle, but it may be worthwhile for the sake of overall financial stability to create mechanisms, as regulators in some countries are doing, that require banks to hold more capital in good economic times so that they can use it as a buffer in case of a downturn. Another important macroprudential tool is to identify how much each bank contributes to systemic risk. This would entail identifying the banks that pose a greater threat to stability and having them hold extra capital. Assigning proper capital requirements is, however, not as straightforward as it may seem as the risk of the banking system changes when capital requirements change. One study has shown that when properly done such a requirement can reduce by one-quarter the probability of a financial crisis. Implementing macroprudential policy in Canada faces some challenges. With both housing prices and the level of Canadians’ personal debt high, sudden corrections to the financial system can create problems. Also, the interconnections between Canadian and foreign banks could result in the former being much more greatly influenced by financial-crisis spillover from the latter, something Canada generally avoided during the 2007 economic meltdown. There’s no consensus as yet on the objectives of macroprudential policy. However, it is a necessary complement to microprudential policy and provides a means of managing systemic risk with the goal of greater global financial stability

Exploiting Symmetry in High-Dimensional Dynamic Programming

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The „curse of dimensionality“ is avoided due to four complementary techniques: (1) exploiting symmetry in the approximate law of motion and the value function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; (3) sampling methods to ensure the model fits along manifolds of interest; and (4) selecting the most generalizable over-parameterized deep learning approximation without calculating the stationary distribution or applying a transversality condition. As an application, we solve a global solution of a multi-firm version of the classic Lucas and Prescott (1971) model of „investment under uncertainty.“ First, we compare the solution against a linear-quadratic Gaussian version for validation and benchmarking. Next, we solve nonlinear versions with aggregate shocks. Finally, we describe how our approach applies to a large class of models in economics