Monthly Archives: November 2015

Central bank credibility and political risk premium

Central bank credibility is arguably a function of how central bank is placed with the government organization and fiscal policy credibility (particularly when monetary and fiscal policy gets a bit blurred these days when some central banks purchase sovereign bond to suppress the yield curve), among other things.

A central bank rarely achieves full independence and hence it is subject to the tension of political power within the government organization. It means that central bank independence could be affected by political risk. In addition, it is well established that fiscal risk incorporates political risk premium. And therefore, central bank credibility should be affected by political risk and hence any assets that is affected by central bank credibility should require a certain level of political risk premium.


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When RMB becomes part of the SDR basket…

A non-event for the market.

And yet good news for the pro-reform camp as it means lots of steps in opening up the capital account and loosening the exchange rate control are irreversible. It could also be good news for the pro-growth camp who may take this as a sign of growing importance of China in the world stage – another boost of national pride – which then can be used to justified the pro-growth policy.

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Man vs Machine?

This is an out-of-date question.

Man vs Machine was a famous question when Kasparov was defeated in a chess game by IBM-developed Deep Blue. However, this question is outdated as a book I read today said that, rather than a match between Man and Machine, it is a match between two roles of Man: a man as performer – who has ego and emotion – and a man as a toolmaker – who builds a machine without ego or emotion, but yet can only think in orderly fashion.

I totally agree. It shouldn’t be man versus machine in today’s world, but rather man and machine. Afterwards, a machine should be a continuously-refined tool by man. The key question is how to develop a machine so that it imitative human thought process. To take the argument further, it is about how to handle uncertainty or even seemly disorder information and generate actions or actionable insights.

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The credibility of a central bank

Why are the Fed, BoE, ECB and BoJ able to purchase sovereign debt and push down interest rate across the curve, without worrying about capital outflow that could in theory push up interest rate?

What about fear of financial repression or money financing of fiscal deficit? Market usually penalizes an EM who attempts to print money to finance deficit – by asking for high risk premium – but why does it not apply to DMs?

Do all DMs have such privilege? Which EMs enjoy such privilege as well? Indeed, how to reclassify a country in a DM or an EM based on the ability of its central bank to do QE?

I don’t think I have the answer to all these questions. Inflation outlook should matter, but what if commodity price starts to pick up again and the world goes back into inflationary environment? Does the commodity price outlook affects a central bank’s ability to do QE? I don’t know.

Central bank independence certainly matters. But how to measure it? Doesn’t the fact that CB purchase of sovereign bond blur the boundary between fiscal and monetary policy?

Judiciary system or more broadly the stability and transparency of political institutions – does it affect central bank independence which then affects market perception of QE credibility?

Do we need to consider more factors, growth, indebtedness, demography…? It is important to assess this issue, otherwise one might have a feeling of unfairness, i.e. does the fact that it is the central banks of demographic developed countries that have done QE mean they are the only CBs in the world that are “allowed” (by the market) to do so?

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The 67 million baby girls

I’m interested in ultra-high (male-to-female) sex ratio at birth in China and hence I went to compute how many more baby girls could have been born between 1980 and 2015, assuming a “normal” sex ratio at birth. I will briefly explain my method based on the UN data, and then explain why the estimated figure has upward bias.

Based on the UN data, the sex ratio in China has steadily increased from 1.07 in first half of 1980s to 1.16 in the first half of 2000s. For the same period, the world sex ratio stabilized between 1.06 and 1.08, while the one for less developed regions (excluding least developed regions) increased moderately from 1.06 to 1.09.

Based on the UN data, over the past 35 years, around 363 million baby boys and 323 million baby girls were born in China, with average sex ratio at birth at 1.12.

What should be a “normal” sex ratio at birth in China? There probably won’t be an accurate answer, but let’s try to use make some assumption. It makes little sense to assume the ratio to be 1.0, given the world average is 1.07. It probably also does not make sense to compare China against DMs, as wealth level might influence sex ratio at birth. It may not be unreasonable to use the average of less developed countries (excluding least developed countries) as proxy, as China belongs to this group. Applying this proxy as the “normal” sex ratio at birth – which is lower than the actual ratio for China – onto the number of baby boys that were born in China, I have found that additional 67 million baby girls could have been born over the past 35 years, or average 1.9 million per year.

There are a few reasons why this figure has upward bias:

  • If Chinese parents had less gender bias, then maybe fewer boys would have been born, as fewer families would have tried to have a boy when their first child (or first few children) was a girl.
  • A baby girl may have been less likely to be registered to the local government than a baby boy does. Hence the reported UN figure may have under-reported the number of baby girls that were actually born.

Btw, please do not use this figure as an approximate number of baby girls killed via abortion. Even without abortion, gender bias of some Chinese parents would also result in a sex ratio at birth higher than other countries.

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How to react to unknown unknowns

Paris attacks, Russian warplane being shot by Turkey, and to some extent the Swiss central bank’s sudden depeg of CHF earlier this year…All of them were unknown unknowns ex ante. Investors can rarely preempt with actions ex ante – or the cost of doing so is relatively high given the great uncertainty about its timing – it is worthwhile asking whether/how investors should react once the event happens, i.e. actions beyond the simple knee-jet response.

Media likes those events – everyone rushes to report them. Investors, or sellside firms that serve investors, need to think clear what require actions versus letting go (treating it as something that adds short-term volatility but not really affects medium-term return). People hate negative surprise, and some just choose to cut their position/exposure when unknown unknown happens, for the sake of peace of mind. However, this actually creates opportunities for contrarian investors to pick bargains. As for sell-side firms who serve investors, it is important to differentiate themselves from news reporters/media. They should discourage the behavior bias of investors, rather than encouraging it like the media usually does. And there is usually no point of rush to be the first to comment on something – that does not add value to investors. It only adds noise.

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Capacity utilization

I recently learned that capacity utilization rate is probably one of the most useful explanatory factors of corporate bond spread.

It is indeed a useful concept that is rooted in microeconomics and yet has important macro and market implication. In other word, while this data has to be aggregated from firm level, it can be used for monetary/fiscal policy implication – as the measure of output gap is based on capacity utilization. In addition, it is an important driver of sector profitability, and hence it is relevant when one assesses equity/corporate bond risk.

And yet it is a measure for which it takes a lot of effort to collect statistics and estimate. Issues include how to select sample firms, how to survey them and how to aggregate across different industries, and most importantly, how to make sure firms report relatively accurate figures on full capacity – which is more a theoretical than a actual measure.

Indeed, whether or not a country publishes this measure, how often it gets published and how robust its methodology is in return reflects the quality of macro data as supplied by the country’s statistical bureau. I hope in the future there will be a database that ranks countries by their macro data quality/breadth and capacity utilization measure should certainly be one of the main data points to consider.

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