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Item type:Publication, Evaluating The Self-Defeating Fiscal Austerity Hypothesis for a Dollarized Economy: The Peruvian Case(Pontificia Universidad Católica del Perú. Departamento de Economía, 2026-03)This paper tests the hypothesis of self-defeating fiscal austerity (Attinasi and Metelli, 2017; Cherif and Hasanov, 2018), using a time-varying parameter vector autoregression with stochastic volatility (TVP-VAR-SV) model estimated on Peruvian data for 2000Q2–2024Q2. The central objective is to assess whether, following a transitory fiscal austerity shock, the debt-to-GDP ratio declines in the long run. If debt fails to decrease—or rises—over time, fiscal policy would be deemed self-defeating for the period under study, reflecting the contraction in economic activity that typically accompanies fiscal tightening. The paper also examines the role of exchange rate dynamics in fiscal consolidation—an aspect largely unexplored in the literature. A family of models with time-varying parameters and stochastic volatility is estimated to evaluate whether these features are essential for an adequate model fit. The results provide evidence against the self-defeating austerity hypothesis: the long-run cumulative response of the debt-to-GDP ratio stabilizes roughly 0.6 percentage points of GDP below its no-shock path. The findings underscore the importance of the exchange rate channel in enhancing the effectiveness of austerity shocks. However, fiscal stabilization becomes ineffective when achieved through expenditure cuts rather than revenue measures, as spending-based austerity dampens GDP growth and exhibits limited persistence, weakening its long-term effect. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forecasting value at risk and expected shortfall in equity markets of high-income and Latin American countries(Pontificia Universidad Católica del Perú. Departamento de Economía, 2026-02)Using daily equity market data for Latin American (Latam) and high-income (HI) countries over 2008-2023, this paper estimates GARCH and GJR models to forecast Value at Risk (VaR) and Expected Shortfall (ES). The performance of a broad set of heavy-tailed and asymmetric distributions is evaluated, including the Normal (N), Skewed Normal (skN), Student’s t (S), skewed S (skS), generalized hyperbolic skS (GHskS), normal inverse Gaussian (NIG), skewed NIG (skNIG), normal reciprocal inverse Gaussian (NRIG), and skewed NRIG (skNRIG). The key findings can be summarized as follows: (i) for VaR forecasting, asymmetric distributionsare preferred at both confidence levels, and at the 99% level heavy tails are also required; (ii) for ES forecasting, at both confidence levels the selected models rely on asymmetric heavy-tailed distributions, with GHskS emerging as the dominant specification; (iii) for VaR forecasting, modeling leverage effects is necessary for most HI countries, whereas this is required for only about half of the Latam countries; and (iv) for ES forecasting, volatility specification plays a more limited role than in VaR forecasting.