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Scenario analysis with the DD-PD mapping approach : stock market shocks and U.S. corporate default risk

Chan-Lau, Jorge A. - 2021

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms' DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative...

Persistent link: https://www.econbiz.de/10012613371

Comovements in National Stock Market Returns : Evidence of Predictability But Not Cointegration

Richards, Anthony J. - 1996

This paper is a response to the literature that tests for cointegration between national stock market indices. It argues that apparent findings of cointegration in other studies may often be due to the use of asymptotic, rather than small-sample, critical values. In fact, economic theory...

Persistent link: https://www.econbiz.de/10014398637

Forecasting social unrest : a machine learning approach

Redl, Chris; Hlatshwayo, Sandile - 2021

We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial,...

Persistent link: https://www.econbiz.de/10012796240

Energy transition metals

Boer, Lukas; Pescatori, Andrea; Stuermer, Martin - 2021

Persistent link: https://www.econbiz.de/10012796674

Authorities' fiscal forecasts in Latin America : are they optimistic?

Hadzi-Vaskov, Metodij; Ricci, Luca Antonio; Werner, … - 2021

documents of the following years to understand the evolution of fiscal forecast errors. Our findings suggest that: (i) for most … terms of drivers, we show that forecast errors for the fiscal balance-to-GDP ratio are positively correlated with GDP growth … and terms of trade changes and negatively with GDP deflator surprises; (iv) forecast errors for public debt-to-GDP ratios …

Persistent link: https://www.econbiz.de/10012612338

Predicting fiscal crises : a machine learning approach

Hellwig, Klaus-Peter - 2021

In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random...

Persistent link: https://www.econbiz.de/10012612343

Improving the short-term forecast of world trade during the Covid-19 pandemic using SWIFT data on letters of credit

Carton, Benjamin; Hu, Nan; Mongardini, Joannes; Moriya, Kei - 2020

An essential element of the work of the Fund is to monitor and forecast international trade. This paper uses SWIFT … (PMI), to improve the short-term forecast of international trade. A horse race between linear regressions and machine …

Persistent link: https://www.econbiz.de/10012392595

How to improve inflation forecasting in Canada

Matheson, Troy - 2019

contributors to overall inflation forecast errors. Perhaps more importantly, relatively small core inflation forecast errors appear …

Persistent link: https://www.econbiz.de/10012103690

How well do economists forecast recessions?

An, Zidong; Jalles, João Tovar; Loungani, Prakash - 2018

-recession years are revised slowly; in recession years, the pace of revision picks up but not sufficiently to avoid large forecast …

Persistent link: https://www.econbiz.de/10012112326

Predicting fiscal crises

Cerovic, Svetlana; Gerling, Kerstin; Hodge, Andrew; … - 2018

This paper identifies leading indicators of fiscal crises based on a large sample of countries at different stages of development over 1970-2015. Our results are robust to different methodologies and sample periods. Previous literature on early warning sistems (EWS) for fiscal crises is scarce...

Persistent link: https://www.econbiz.de/10011905898

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