PrefBlog

OSFI has republished a puff-piece written for Central Banking magazine, titled Lessons for Banking Reform: A Canadian Perspective, by Carol Ann Northcott & Graydon Paulin of the Bank of Canada and Mark White of … OSFI.

Credit for Canada’s performance throughout the crisis is given to:

• High levels of capital
• A rational mortgage market
  • relatively low Loan-to-Value
  • Recourse to borrower
  • Non-deductability of interest
• Assets-to-Capital (ACM) multiple control
• lack of competition from shadow banks
• The wise and beneficient supervision of those sadly underpaid geniuses (genii?) at OSFI

Not much meat on these bones, frankly. I would have been much more interested in a solid analysis of just WHY we were so lucky. Why weren’t the banks up to their necks in sub-prime paper, like everybody else? Was it the ACM? Was it because Canadian banking is such a profitable rent-extraction machine that banks didn’t need to lever up on Sub-prime at LIBOR+50? I find the idea that “Canadian Bankers are Smart” rather difficult to swallow. We nearly went bust in the MBA crisis of the 1980’s … we’ll find something else soon, don’t fret.

And why are we so highly capitalized, anyway? It has been very useful in the downturn, there’s no denying that … but what are the net, through-the-cycle cost/benefits of tying up a lot of capital in the banking system?

Gareth Campbell writes an interesting essay on VoxEU, The railway mania: Not so great expectations?, in which he uses the British Railway Mania of the 1840’s to consider the problem of deciding ex ante whether there is a bubble in asset prices:

Can financial crises be averted by identifying and dealing with overpriced assets before they cause instability? This column argues that during the British Railway Mania of the 1840s, railway shares were not obviously overpriced, even at the market peak, but prices still fell dramatically. This suggests that extreme asset price reversals can be difficult to forecast and prevent ex ante, and the financial system always needs to be prepared for substantial price declines.

Assiduous Readers will be well aware of my view that market timing is difficult to do … really, really, really difficult to do … impossible. Comparing one share in a bank to another share in another bank is relatively easy. Comparing one share in a bank to cash … can’t be done.

This concept has become important in that there is a move afoot to give central bankers a mandate to time the markets:

The instability that has followed the bursting of the housing bubble has led to a renewed discussion about what can be done to prevent the recurrence of financial crises. Cecchetti et. al (2000) have suggested that monetary policy should be tightened when regulators believe assets are overpriced, in an attempt to deflate a suspected bubble before it bursts. However, Bernanke (2002) and Mishkin (2008) have argued that this proposal is not feasible, partly because mispricing is difficult to identify ex ante. Several pieces of academic research have provided a justification for this position by suggesting that assets were not obviously mispriced prior to market crashes in certain historical episodes, such as the Tulip Mania of 1636 (Garber, 2001), the German stock market boom of 1927 (Voth, 2003), and before the Wall Street Crash of 1929 (Donaldson and Kamstra, 1996).

It’s an interesting piece and ties in with my fears regarding the too-popular dividend growth magic formula, that I fear will increasingly lead to unwise decisions being made regarding dividend payouts and lead to very crowded trades when the hard decisions finally become effective.

First he shows the market indices for All Railways, Established Railways and Non-Railways:

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Then he shows how dividends rose and fell through the period:

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Then he compares the dividend yield on railways with non-railways

This involved performing a regression for each week of the sample using the dividend yield of a company as the dependent variable and using a dummy variable, which equalled 1 if a company was a railway, as the independent variable. The coefficient of the railway dummy in each week is plotted with ±1.96 standard errors in Figure 3.

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and finally:

I have also extended the analysis of the overpricing or underpricing of railway shares by including as independent variables the dividend growth that the company went on to experience during the next three years. After controlling for this growth, the apparent overpricing of the railways during the boom in prices is almost entirely eliminated. The railways appear to have had a significantly lower dividend yield, after accounting for short-term dividend growth, on just two weeks during the entire period, as shown in Figure 4.

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He concludes:

Regulators may be able to effectively intervene if they have greater foresight than other market participants but, as Bernanke (2002) has argued, this is a very questionable assumption. Without perfect foresight by regulators, which would allow restrictions to be imposed only when necessary, there would have to be tighter regulation in all periods, and the costs of such restrictions would need to be carefully weighed against the potential benefits.

It may be better to focus efforts on ensuring stability when the next asset price bust does occur. More attention should be directed to the consequences of sustained declines in asset prices, as capital requirements which are based on short-term market risk may provide inadequate protection against persistent and longer-term falls. It may be useful to introduce some long-term stress testing, which would examine the consequences if the largest peak-to-trough asset price movements in history were to be repeated. If the financial system could not endure some of these historical experiences, then it may need to embrace tighter regulation or restructuring.

I continue to like the idea of dynamic provisioning, in which a bank’s “recent” assets would carry a higher risk weight than “old” assets, penalizing recent growth to a predictable degree (in addition to the unrelated idea of penalizing excessive size, inter alia). Dynamic provisioning has, to some extent of equivalency, been implemented by the FDIC.

I like those ideas a LOT more than telling Bernanke that the non-bonusable nature of his position makes him an infallible market timer.

Mikhail Chernov & Philippe Mueller: The Term Structure of Inflation Expectations:

The ten-year inflation premium declines from six to zero per cent during the post-monetary-experiment period. This decline suggests that long-run inflation expectations became more stable over time. Further, we reestimate our model every quarter and find that the long-run expectations have declined over time from 6% to 2%. The inflation persistence declined and the term structure of inflation expectations became flat over time. This evidence suggests that monetary policy became better anchored.

One implication of anchored inflation expectations is that it should be easier to forecast inflation and yields. Consistent with this prediction, we find that the model that incorporates both yields nd surveys dominates in out-of-sample forecasting of both inflation and yields. These results lead us to conclude that information in surveys is extremely important for establishing the links between inflation expectations and yields.

Figure 4 shows the time-series of the inflation expectations at multiple horizons. These expectations are computed from AO. In contrast to the survey forecasts in Figure 1, these objective, or marginal, expectations can be computed each period at any horizon.

The term structure effects are pronounced. The inflation curve becomes inverted in 1973, right before the recession, and continues to be inverted until early 1982. This period coincides with the unstable period of monetary policy during the Burns and Miller chairmanship of the US Federal Bank and the monetary policy experiment under Volcker’s chairmanship. The curve became inverted again briefly in the early part of Greenspan’s tenure from 1987 to 1991. afterwards, it had a normal, nearly flat, shape.

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The out-of-sample analysis of the model suggests that monetary policy became more effective over time. The long-run expectations are anchored at about 2%. The term structure of inflation expectations has flattened out over time. This suggests that the arrival of new data does not affect long-run expectations much, perhaps because the monetary policy is expected to address all short term fluctuations successfully.

Christopher Reid, Frédéric Dion and Ian Christensen, Real Return Bonds: Monetary Policy Credibility and Short-Term Inflation Forecasting, Bank of Canada Review, Autumn 2004:

Chart 5 shows two proxies of long-run inflation uncertainty. The first is a measure of the disagreement among forecasters who responded to the Watson Wyatt survey, calculated as the difference between the upper and lower quartiles of reported inflation expectations at the 4- to 14-year horizon. The second measure is inflation uncertainty over a 5-year forecast horizon derived from a GARCH model developed by Crawford and Kasumovich (1996).

Côté et al. (1996) suggest that the increase in the BEIR [Break-Even Inflation Rate, Nominals less RRBs] in 1994, which was not accompanied by a similar move in survey measures, may reflect an increase in the inflation-risk premium. If changes in the premium for inflation uncertainty are an important factor in explaining movements in the BEIR, then sharp movements in these proxies should be associated with similar movements in the BEIR. Yet both measures fail to indicate a rise in inflation uncertainty in 1994 or a significant decline in 1997. Crawford and Kasumovich’s measure of inflation uncertainty fell dramatically during the 1980s but has been relatively stable since 1992. Similarly, survey disagreement fell between 1991 and 1994 but was relatively stable afterwards. The simplest explanation is that deviations of the BEIR from survey measures of inflation expectations are the result of some phenomenon other than changes in uncertainty regarding inflation.