Saleem Bahaj, Angus Foulis, Gabor Pinter and Paolo Surico
Changes in interest rates affect different parts of the economy differently. In this post, building on a recent working paper, we consider how different types of firms respond to interest rate changes. We focus on firm level employment and ask which firms do the most hiring and firing when monetary policy adjusts. For instance, how important is the age of the firm, its balance sheet position or its size in determining the firm level response to interest rates? Furthermore, do these patterns of responses tell us something about how monetary policy affects the economy?
The great American baseball sage, Yogi Berra, is thought to have once remarked: ‘It’s tough to make predictions, especially about the future’. That is certainly true, but thankfully the accelerating development and deployment of machine learning methodologies in recent years is making prediction easier and easier. That is good news for many sectors and activities, including microprudential regulation. In this post, we show how machine learning can be applied to help regulators. In particular, we outline our recent research that develops an early warning system of bank distress, demonstrating the improved performance of machine learning techniques relative to traditional approaches.
Bruno Albuquerque, Knut Are Aastveit and André Anundsen
Housing supply elasticities – builders’ response to a change in house prices – help explain why house prices differ across location. As housing supply becomes more inelastic, the more rising demand translates to rising prices and the less to additional housebuilding. In a new paper, we use a rich US dataset and novel identification method to show that supply elasticities vary across cities and across time. We find that US housing supply has become less elastic since the crisis, with bigger declines in places where land-use regulation has tightened the most, and in areas that had larger price declines during the crisis. This new lower elasticity means US house prices should be more sensitive to changes in demand than before the crisis.
Speculative buying can drive cryptocurrency prices down. This is contrary to the usual laws of economics. Blockchain technology limits how quickly transactions can be settled. This constraint creates competition for priority between different users. The more speculative activity there is, the longer it takes to make a payment. But the future value of cryptocurrency depends on its usefulness as a means of payment. Speculation therefore affects price formation through a channel that does not exist for other asset classes. This can explain the high price volatility of cryptocurrencies, and is consistent with the low adoption rate so far.
Andreas Joseph, Christiane Kneer, Neeltje van Horen and Jumana Saleheen
Financial crises affect firm growth not only in the short-run, but even more so in the long-run. Some firms permanently gain while others lose and cash is a crucial asset to have when the credit cycle turns. As we show in a new Staff Working Paper, having cash at hand allows firms to continue to invest during the crisis while industry rivals without cash have to divest. This gives cash-rich firms an important competitive edge that not only benefits them during the crisis but that gives them an advantage that lasts way beyond the crisis years.
Children are expensive. Swings in families’ cash-flow can therefore move the dial on families’ decisions on whether and when to have a baby. For mortgaged families with an adjustable interest rate in 2008, the sharp fall in Bank Rate amounted to a windfall of around £1,000 per quarter in lower mortgage payments. In this post we show that people responded to this cash-flow boost by having more children. In total, we estimate that monetary policy increased the birth rate in the following three years by around 7.5%. That’s around 50,000 extra babies.
Meteorologists and insurers talk about the “1-in-100 year storm”. Should regulators do the same for financial crises? In this post, we argue that false confidence in people’s ability to calculate probabilities of rare events might end up worsening the crises regulators are trying to prevent.
Recent developments in digital technology fuel the notion that we are at an inflection point in human history, where fully automated robots are on their way to permanently replacing humans at work. To better understand the dynamics between automation and the demand for human labour, I undertook a case study on financial advice robots – colloquially known as roboadvisors. For the roboadvice firms examined, I found that human involvement is still crucial. Full automation is thus a myth, at least for now, in this industry. But roboadvisors do demonstrate that some cognitive ‘non-routine’ tasks can be automated. Previously, ‘non-routine’ tasks had been widely considered as non-automatable. Roboadvisors demonstrate how the frontier of potential automation is not limited to menial, routine tasks.
Marco Bardoscia, Gerardo Ferrara and Nicholas Vause
Participants in derivative markets collect collateral from their counterparties to help secure claims against them should they default. This practice has become more widespread since the 2007-08 financial crisis, making derivative markets safer. However, it increases potential ‘margin calls’ for counterparties to top up their collateral. If future calls exceed available liquid assets, counterparties would have to borrow. Could money markets meet this extra demand? In a recent paper, we simulate stress-scenario margin calls for many of the largest derivative-market participants and see if they could meet them – including because of payments from upstream counterparties – without borrowing. We compare the sum of any shortfalls with daily cash borrowing in international money markets.
The financial crisis exposed banks’ vulnerability to a type of risk associated with derivatives: credit valuation adjustment (CVA) risk. Despite being a major driver of losses – around $43 billion across 10 banks according to one estimate – there had been no capital requirement to cushion banks against these losses. New rules in 2014 changed this.