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    A Theory of Debt Accumulation and Deficit Cycles

    I talked about my ideas in this paper to colleagues, professional economists, policymakers, and also in blogs. It’s time I begin present the paper before academic audiences. Today I start with a short presentation during the Swiss Finance Institute research days in Study Center Gerzensee.

    The issue: many advocate the thesis that a large government debt might hinder growth. For example, too much debt may signal upcoming tax increases, which discourages new investments. Do you remember Krugman v. Reinhart & Rogoff controversy? Reinhart & Rogoff theses seem to have influenced policymakers in the process of dealing with the European debt crisis of the early 2010s. In Reinhart and Rogoff own words:

    “Our 2010 paper found that, over the long term, growth is about 1 percentage point lower when debt is 90 percent or more of gross domestic product.”

    Carmen M. Reinhart and Kenneth S. Rogoff, April 26, 2013, The New York Times

    Do such numbers exist? Paul Krugman believes they don’t. More recently, Alberto Alesina, Carlo Favero and Francesco Giavazzi have shifted the debate on how to cure debt-sickness in their book on Austerity: When It Works and When It Doesn’t (2019, Princeton University Press). To cut a long story, they collect evidence and argue that austerity programs relying on cutting expenses are much less painful and, sometimes, even conducive to growth, than austerity plans relying on increasing taxes.

    I ask a related question. Too much debt might lead to default, and austerity plans might be unavoidable at some point. When? My answer is that such austerity plans might arrive too late to avert a crisis.

    As I explained in a previous post, I am not claiming primary deficits are necessarily wrong.

    Slides are here. Paper will be posted here before the end of the summer.

    Keywords: National debt, fiscal tipping points; austerity; credit spreads.

     
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    The art of art analytics

    Today I would like to talk about a topic that I occasionally find it very intriguing: How do we evaluate art? Can we use big data and artificial intelligence to price one of the “Tagli di Fontana” or a piece of Kusama? How do we determine whether a certain artist is overpriced? Do you like the picture below?

    Figure 1

    First things first. What is fundamental and what is irrational about asset evaluation? How do we identify whether an asset is in a bubble? When do bubbles burst? Financial economists have been debating these issues for decades. The outcome is a set of sophisticated analytical tools at the heart of algorithmic trading strategies. Discovering a nascent or a fading bubble has simple trading implications. Equally important is relative evaluation in a given asset class: which assets in this class are undervalued or overvalued? A long-short strategy (buy the cheap, sell the rich) would help investors regardless of the direction of the bubble. But, how do we know whether an asset is undervalued? Modeling relative value is an art, and the tools in this space may be quite sophisticated.

    We may make use of comparable analytics in the fascinating art space, a space governed by amazing regularities despite the obvious unpredictable traits that underlie creative work. First, one would need to identify general trends in market evaluations for a given artist. One would, then, need to collect transaction data on this artist and extract general trends in these data, in spite of the unavoidable heterogeneity in the artist’s creations. Admittedly, though, this task might be expensive. Data vendors might require hundreds of thousand dollars subscription per year for having access to their unstructured data.

    Figure 1 depicts an index of market evaluations that I have created for Yayoi Kusama, a celebrated Japanese artist. The index equals 100 in the first year for which we record transactions; the index, then, evolves over time, going up or down according to the average price direction in the universe of Kusama’s artworks over time.

    The method of index construction is instructive. The methodology enables one to extract common trends in the artist’s artworks while also supplying tentative predictions of the value of each single artwork in the index universe through an approach known as “hedonic.” Consider Figure 2. Its left panel depicts the index values of Figure 1. The right panel depicts, instead, the price of sold artworks (i.e., observed prices) against the price of artworks predicted by the model. Shown in this right panel is also the 45-degree line, in red.

    Figure 2

    If the model were able to predict prices without errors, the blue circles would all lie on the red line. Alternatively, and in an idealized world in which the model holds perfectly, the points below the red line identify artworks that are cheap. The model, then, delivers an assessment of relative value. If we were allowed to sell short, we could implement a long-short strategy, selling the artworks identified by the circles above the red line, and buying the artworks identified by the circles below the red line. In practice, the model is only an approximation of the subtle market mechanisms underlying Kusama’s creations, and such trades are risky. Still, the model provides us with a useful trading recommendation that may complement traditional advice in this space.

    Art analytics might truly help us understand art evaluations. An art-tech company might then have to resemble a fin-tech. Its dedicated team would have to aim at developing methods and processes to analyze artworks’ data for the purpose of indexing and pricing. Its data scientists, mathematicians and economists would have to dialogue with art experts and improve the provision of information and implement tasks such as:

    • Creation of a trading platform. Would need find regulatory clearance: not a trivial task.
    • Developing automatic index feeds and displays on the platform.
    • Provision of bespoke indexing and pricing services to UHNWI, such as portfolio selection, financial advice, trading recommendations.
    • Developing hedonic pricing algorithms comprising art expert categorical variables: the antecedent to big data analytics.
    • Designing financial products centered on artwork indices.
    • Contributing to mechanism design regarding pricing and trading protocols.
    • Designing listable products.

    At the moment, these tasks seem to belong to science-fiction or, to remain on this theme, art-fiction…

    Keywords: art pricing models, art-tech & fin-tech, artificial intelligence, big data analytics, bubbles, hedonic models, relative value trading.

     
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    Playing with fire (and national debt)

    Fun ways to play with fire … It may take a few bps prediction error on how deficits affect GDP growth for the markets to take off on a “diabolic” spread-debt loop. The graph below contains my simulations, taken from a working paper of mine (“A Theory of Debt Accumulation and Deficit Cycles”).

    Define the spread curve as the relation between the debt-to-GDP ratio of a country and the spread over a riskless security that the markets require to invest in the national debt issued by that country. The blue curve is the spread curve in an economy with low growth. The red curves are the spread curves in an economy with more deficits; the solid line results when a higher deficit improves economic growth; the dashed line results in the unlucky circumstance where more deficits are not followed by more growth. With a debt-to-GDP ratio at about 130%, a reform that goes the wrong way might rapidly lead a country to spiral to default.

    Does not mean that deficits lead to default. Means that when debt is high compared to GDP, extreme caution would need to be exercised while deciding upon the nature and extent of a deficit (if any).

    Keywords: Spread; parameter uncertainty; fiscal reforms; default.