Global In…ation Outlook · Stochastic Volatility and Outlier detection (UCSVO) introduced in...

Global In�ation Outlook

Global In�ation Outlook

April 6, 2018

This document contains a selection of charts that are the output of Fulcrum’s quantitativetoolkit for monitoring global inflation trends. All of our forecasts and projections are theoutput of formal econometric models that have been developed in the academic literature, andtherefore contain little input from direct judgement. All charts and models are up to date withavailable data up to April 6, 2018.a

Table of contents

In�ation 2

Consumer Prices In�ation Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Producer and Import Prices In�ation Forecasts . . . . . . . . . . . . . . . . . . . . . . 7

Stock and Watson Consumer Prices ‘Trend’ In�ation . . . . . . . . . . . . . . . . . . . 12

United States: PCE ‘Trend’ In�ation by Components . . . . . . . . . . . . . . . . . . . 15

United States: Wage Trend In�ation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Market Measures of In�ation Expectations . . . . . . . . . . . . . . . . . . . . . . . . 21

Global Oil Market 23

aMore details of the specific models, and intra-month updates, are available upon request.

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Global Macroeconomic Outlook

In�ation

Consumer Prices In�ation Forecasts

Fulcrum short-term inflation forecasting models

The following charts present the equal weighted average of the inflation forecasts esti-mated using two models: a Bayesian Vector Autoregressive Model (see following para-graph) and an Unobserved Components model (see next section).

For each country, the BVAR model includes: headline inflation, core inflation, the oilprice, the trade weighted exchange rate, import prices inflation, producer prices infla-tion and non-energy commodity prices inflation. We use Minnesota priors, as in Doan,Litterman, and Sims (1983), to shrink the coefficients of the BVAR towards a moreparsimonious specification.

When available, the charts present the market forecast as implied by the inflation swaps.For the UK and the US, inflation swaps are linked respectively to RPI and CPI inflation.Therefore, we compute market implied forecasts for CPI and PCE inflation conditioningon the paths for RPI and CPI implied by the inflation swaps.

How to read the graphs

The following charts display the predictive distribution of inflation for the next two yearsin the form of fan charts.

The solid red line represents the most recent median forecast of inflation and the darkand the light red areas around it represent respectively the 68% and the 90% confidencebands.

The dashed black line shows the market forecast.

The blue dots represent the forecasts published by the policy makers. Whenever theseare expressed as annual average forecasts, the comparable annual numbers produced byour models are displayed as red dots.

Notes and data definitions

• Headline inflation: USA: PCE deflator; Euro Area: HICP inflation; Canada: CPI;Japan: CPI excluding fresh food; Sweden: CPI; UK: CPI.

• Core inflation: USA: PCE excluding food and energy; Euro Area: HICP excludingEnergy, Food, Alcohol and Tobacco; Canada: CPI excluding food and energy;Japan: CPI excluding food, beverages and energy; Sweden: CPI with fixed interestrates excluding energy; UK: CPI excluding energy, food, alcoholic beverages andtobacco.

• The USA model includes CPI and core CPI inflation as additional indicators. TheUK model includes RPI inflation as an additional indicator.

• We exclude VAT increases by assuming full and immediate pass-through on themonth of implementation.

Sources: Fulcrum calculations based on Haver Analytics data.

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In�ation Forecasts from Bayesian Model Averaging (% 12 month change)

Note: Advanced economies is the PPP weighted average of US (PCE), Euro Area, UK, Japan, Canada,Sweden and Norway. World is the PPP weighted average of the former economies and China.

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Note: The blue dots represent the forecasts published by the policy makers. The dashed black lineshows the market forecast.

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Producer and Import Prices In�ation Forecastss

Fulcrum short-term inflation forecasting models

The following charts present the inflation forecasts estimated using the Bayesian VectorAutoregressive Model described in the previous section.


The following charts display the predictive distribution of inflation for the next two yearsin the form of fan charts.

The solid red line represents the most recent median forecast of inflation and the darkand the light red areas around it represent respectively the 68% and the 90% confidencebands.


April 6, 2018 7


In�ation Forecasts from a Bayesian Vector Autoregressive Model (% 12 month change)

Note: Advanced economies is the PPP weighted average of US, Euro Area, UK, Japan, Canada, Swedenand Norway. World is the PPP weighted average of the former economies and China.

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Stock and Watson Consumer Prices ‘Trend’ In�ation

Fulcrum trend inflation models

The following charts present our estimates of ‘trend inflation’. The model splits inflationinto a persistent component (‘trend’) and a purely transitory component (‘noise’), whichreverts within one month. Therefore, the trend captures long-term as well as cyclicalinfluences and can be thought of as a measure of ‘core inflation’ derived from the timeseries behaviour of inflation.

The estimates are produced using a version of the Unobserved Components model withStochastic Volatility and Outlier detection (UCSVO) introduced in Stock and Watson(2007, 2016). The main difference with Stock and Watson (2016) lies in the modellingof outliers. Rather than using discrete probabilities of outliers, we use a fat-tailed distri-bution for the variance of the noise component, in line with Jacquier et al. (2004).

When possible, we use the multivariate version of the model (M-UCSVT), which modelsthe main subcomponents of inflation independently and aggregates the trends using theofficial weights. Otherwise, we use the univariate model (UCSVT), which models directlythe headline inflation series.


In the following graphs, the grey line represents the monthly inflation rate (% MoMAnn.). The solid red line represents the estimate of trend inflation. The red arearepresents the 68% confidence band around it.

Data definition

USA: PCE deflator; Euro Area: HICP inflation; China: CPI; Japan: CPI excluding freshfood; UK: CPI; Canada: CPI; Sweden: CPI; Norway: CPI; Switzerland: CPI.


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Stock and Watson ‘trend’ in�ation

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Stock and Watson ‘trend’ in�ation

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United States: PCE ‘Trend’ In�ation by Components

Fulcrum trend inflation models

The following charts present our estimates of ‘trend inflation’ for the four main subcom-ponents of PCE inflation in the United States.

The estimates are produced using the statistical model described in the previous section(UCSVT).


In the following graphs, the grey line represents the monthly inflation rate (% MoMAnn.). The solid red line represents the estimate of trend inflation. The red arearepresents the 68% confidence band around it.


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US PCE ‘trend’ in�ation

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United States: Wage Trend In�ation

Fulcrum wage inflation model

The following charts present our estimates of wage ‘trend inflation’ in the United States.

The estimates are produced using the statistical model described in the previous section(UCSVT).


In the following graphs, the grey line represents the monthly wage inflation rate (%MoM Ann.). The solid red line represents the estimate of wage trend inflation. Thered area represents the 68% confidence band around it.


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US Wage Trend In�ation

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Market Measures of In�ation Expectations

Definitions

Five year inflation-linked swap ratesThe swap rates are linked to CPI inflation in the US, HICP inflation in the Euro Areaand RPI inflation in the UK. The horizontal dashed line represents the policy maker’starget. In the case of the US and the UK we have added 0.5% and 0.6% to reflect theaverage bias of US CPI and UK RPI over the inflation measures targeted by the FederalReserve and the Bank of England (respectively PCE inflation and CPI inflation).

Inflation protectionThe inflation protection is an annual inflation cap of 4% over 5 years.

Deflation protectionThe deflation protection is an annual inflation floor of 0% over 5 years.

Developments should be interpreted with caution due to limited market liquidity.

Sources: Fulcrum calculations based on Bloomberg data.

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Market In�ation Expectations (derived from swaps)

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Global Oil Market

The following charts present forecasts of the oil price and its drivers estimated using amodified version of the VAR model of Kilian and Murphy (2014). This VAR uses fourvariables: the real price of oil, global crude oil production, global crude oil inventoriesand the index of real economic activity developed in Kilian (2009). For our version, weuse Minnesota priors as in Doan et al. (1983) to shrink the coefficients of the BVARtowards a more parsimonious specification.

How to read the graphs:

The first chart shows the predictive distribution of Brent crude oil in the form of a fanchart. The solid black line represents the actual and forecasted Brent crude oil price.The dark and the light grey areas around it represent respectively the 68% and the 90%confidence bands. We also show the evolution of WTI crude oil prices and the Brent-WTIspread. The dashed lines show the futures prices.

The remaining charts show the evolution of the three additional variables included in themodel.a

Notes and data definitions:

• Crude oil prices: Nominal spot Brent crude oil prices. Prior to 1985 we usespot oil price for West Texas Intermediate (WTI). To deflate the nominal spot oilprice, we use the U.S. consumer price index for all urban consumers. Whenevernecessary, we use the forecast of the CPI from the previous section.

• Crude oil production: Monthly world oil production data measured in thou-sands of barrels of oil per day were obtained from the U.S. Energy InformationAdministration’s (EIA) Monthly Energy Review. For the figure, we plot petroleum(i.e. crude oil and refined products) production from EIA, Energy Intelligence, andOPEC.

• Crude oil inventories: We obtain an estimate for global inventories as in Kilianand Murphy (2012) by multiplying the U.S. crude oil inventories by the ratio ofOECD inventories of crude petroleum and petroleum products to U.S. inventoriesof petroleum and petroleum products.

• Index of real economic activity: Cost of international shipping deflated by theU.S. CPI and then reported in deviations from a linear trend, as in Kilian (2009).b

Sources: Fulcrum calculations based on Haver Analytics and Bloomberg data.

aWe actually show various estimates of global petroleum production, as opposed to global crude oilproduction.

bThis is downloaded from Lutz Kilian’s website: http://www-personal.umich.edu/~lkilian/paperlinks.html. To nowcast the most recent values, we deflateand de-trend the Baltic Dry Index.

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http://www-personal.umich.edu/~lkilian/paperlinks.html

http://www-personal.umich.edu/~lkilian/paperlinks.html


Oil Market

Note: The dashed lines in the first chart show the futures prices.

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References

Doan, T., R. B. Litterman, and C. A. Sims (1983): “Forecasting and Conditional ProjectionUsing Realistic Prior Distributions,” NBER Working Papers 1202, National Bureau of EconomicResearch, Inc.

Jacquier, E., N. G. Polson, and P. E. Rossi (2004): “Bayesian analysis of stochastic volatilitymodels with fat-tails and correlated errors,” Journal of Econometrics, 122, 185–212.

Kilian, L. (2009): “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocksin the Crude Oil Market,” American Economic Review, 99, 1053–69.

Kilian, L. and D. P. Murphy (2014): “The Role Of Inventories And Speculative Trading In TheGlobal Market For Crude Oil,” Journal of Applied Econometrics, 29, 454–478.

Stock, J. H. and M. W. Watson (2007): “Why Has U.S. Inflation Become Harder to Forecast?”Journal of Money, Credit and Banking, 39, 3–33.

——— (2016): “Core Inflation and Trend Inflation,” Review of Economics and Statistics, 98, 770–784.

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DisclaimerThis material is for your information only and is not intended to be used by anyone other than you. It isdirected at professional clients and eligible counterparties only and is not intended for retail clients. Theinformation contained herein should not be regarded as an offer to sell or as a solicitation of an offer to buyany financial products, including an interest in a fund, or an official confirmation of any transaction. Anysuch offer or solicitation will be made to qualified investors only by means of an offering memorandum andrelated subscription agreement. The material is intended only to facilitate your discussions with FulcrumAsset Management as to the opportunities available to our clients. The given material is subject to changeand, although based upon information which we consider reliable, it is not guaranteed as to accuracy orcompleteness and it should not be relied upon as such. The material is not intended to be used as a generalguide to investing, or as a source of any specific investment recommendations, and makes no implied orexpress recommendations concerning the manner in which any client’s account should or would be handled,as appropriate investment strategies depend upon client’s investment objectives.Funds managed by Fulcrum Asset Management LLP are in general managed using quantitative models though,where this is the case, Fulcrum Asset Management LLP can and do make discretionary decisions on a frequentbasis and reserves the right to do so at any point. Past performance is not a guide to future performance.Future returns are not guaranteed and a loss of principal may occur. Fulcrum Asset Management LLP isauthorised and regulated by the Financial Conduct Authority of the United Kingdom (No: 230683) andincorporated as a Limited Liability Partnership in England and Wales (No: OC306401) with its registeredoffice at Marble Arch House, 66 Seymour Street, London, W1H 5BT. Fulcrum Asset Management LP is awholly owned subsidiary of Fulcrum Asset Management LLP incorporated in the State of Delaware, operatingfrom 350 Park Avenue, 13th Floor New York, NY 10022.© 2018 Fulcrum Asset Management LLP. All rights reserved.

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