scholarly journals Vulnerable Growth

2019 ◽  
Vol 109 (4) ◽  
pp. 1263-1289 ◽  
Author(s):  
Tobias Adrian ◽  
Nina Boyarchenko ◽  
Domenico Giannone
Keyword(s):  
Conditional Distribution ◽  
Financial Sector ◽  
Gdp Growth ◽  
Conditional Volatility ◽  
Conditional Mean ◽  
Financial Conditions ◽  
Over Time

We study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in the conditional volatility and a decline in the conditional mean of GDP growth, leading the lower quantiles of GDP growth to vary with financial conditions and the upper quantiles to be stable over time. Upside risks to GDP growth are low in most periods while downside risks increase as financial conditions become tighter. We argue that amplification mechanisms in the financial sector generate the observed growth vulnerability dynamics. (JEL C53, E23, E27, E32, E44)

UK Fiscal Squeezes over a Century

2017 ◽  
Author(s):  
Christopher Hood ◽  
Rozana Himaz
Keyword(s):  
Data Sources ◽  
Fiscal Consolidation ◽  
Financial Outcomes ◽  
Financial Conditions ◽  
Deficit Reduction ◽  
Before And After ◽  
The Uk ◽  
Changes Over Time ◽  
Spending Cuts ◽  
Over Time

This chapter draws on historical statistics reporting financial outcomes for spending, taxation, debt, and deficit for the UK over a century to (a) identify quantitatively and compare the main fiscal squeeze episodes (i.e. major revenue increases, spending cuts, or both) in terms of type (soft squeezes and hard squeezes, spending squeezes, and revenue squeezes), depth, and length; (b) compare these periods of austerity against measures of fiscal consolidation in terms of deficit reduction; and (c) identify economic and financial conditions before and after the various squeezes. It explores the extent to which the identification of squeeze episodes and their classification is sensitive to which thresholds are set and what data sources are used. The chapter identifies major changes over time that emerge from this analysis over the changing depth and types of squeeze.

scholarly journals COUNT AND DURATION TIME SERIES WITH EQUAL CONDITIONAL STOCHASTIC AND MEAN ORDERS

2020 ◽  
pp. 1-33
Author(s):  
Abdelhakim Aknouche ◽  
Christian Francq
Keyword(s):  
Time Series ◽  
Conditional Distribution ◽  
Stochastic Orders ◽  
Conditional Mean ◽  
Contraction Condition ◽  
Mixing Coefficients ◽  
The Mean ◽  
Quasi Maximum Likelihood ◽  
Geometric Decay ◽  
Mean Function

We consider a positive-valued time series whose conditional distribution has a time-varying mean, which may depend on exogenous variables. The main applications concern count or duration data. Under a contraction condition on the mean function, it is shown that stationarity and ergodicity hold when the mean and stochastic orders of the conditional distribution are the same. The latter condition holds for the exponential family parametrized by the mean, but also for many other distributions. We also provide conditions for the existence of marginal moments and for the geometric decay of the beta-mixing coefficients. We give conditions for consistency and asymptotic normality of the Exponential Quasi-Maximum Likelihood Estimator of the conditional mean parameters. Simulation experiments and illustrations on series of stock market volumes and of greenhouse gas concentrations show that the multiplicative-error form of usual duration models deserves to be relaxed, as allowed in this paper.

Establishing finance-growth linkage for India: a financial conditions index (FCI) approach

2019 ◽  
Vol 14 (5) ◽  
pp. 1032-1059 ◽  
Author(s):  
A.K. Giri ◽  
Deven Bansod
Keyword(s):  
Economic Growth ◽  
Global Financial Crisis ◽  
Gdp Growth ◽  
Data Sets ◽  
Content Type ◽  
Real Gdp ◽  
Financial Conditions ◽  
Multiple Markets ◽  
Long Run ◽  
The Global Financial Crisis

Purpose The global financial crisis of 2008 emphasized the need for monetary policy authorities to have a more comprehensive view of the conditions prevailing in the economy before deciding their policy stance. The purpose of this paper is to outline the construction of a financial conditions index (FCI) and investigate the possible co-integrating relationship between the economic growth and FCI. Design/methodology/approach The study employs the PCA methodology, with appropriate augmentations to handle the unbalanced panel data-sets and constructs a FCI for India. It tests the growth-predicting power of FCI by applying the auto regressive distributed lags approach to co-integration and verifies if the FCI is co-integrated with real GDP growth. It also discusses construction of a financial development index (FDI) which tracks the financial markets through M3, market capitalization and credit amount to residents. Findings The constructed FCI has a quarterly frequency and is available starting 1998q2. The long-run coefficient of FCI while predicting the real GDP growth is significant at 10 percent. The results confirm that a more-broader index FCI outperforms a narrower index FDI in growth prediction. Research limitations/implications By showing that FCI is a better growth predictor than FDI, the study establishes the importance of including the foreign exchange markets, bond markets and stock markets while summarizing the conditions in the economy. The authors hope that the FCI would be helpful to the monetary authorities in their policy decisions. Originality/value The paper adds to the few existing studies studies dealing with FCI for Indian economy and constructs a more comprehensive index which tracks multiple markets simultaneously. It also fills the gap in literature by evaluating the correlating relationship between FCI and economic growth.

scholarly journals Time-varying impact of U.S. financial conditions on China's inflation: a perspective of different types of events

2021 ◽  
Vol 5 (4) ◽  
pp. 604-622
Author(s):  
Yanhong Feng ◽  
◽  
Shuanglian Chen ◽  
Wang Xuan ◽  
Tan Yong ◽  
...  
Keyword(s):  
Financial Market ◽  
Trade Flow ◽  
Time Varying ◽  
Vector Autoregressive ◽  
Financial Conditions ◽  
Time Varying Parameter ◽  
Different Types ◽  
Changes Over Time ◽  
Over Time ◽  
Time Point

<abstract> <p>In recent years, the frequency adjustment of U.S. monetary policy has a dynamic and global impact on other countries' economy. Based on the financial conditions index (FCI), the paper employs the time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV) and spillover index respectively to investigate the time-varying impact of U.S. financial conditions (UFCI) on China's inflation (CINF) and its impact mechanisms. Some results are achieved as follows: first, the impacts of UFCI on CINF vary greatly over time both in the dimension of action duration and time point. Second, the effects of UFCI on CINF directly relate to different types of major events, and they are heterogeneous in action duration, degree, direction as well as the trend and range of fluctuations. In addition, UFCI can work on CINF through trade flow and China's financial market, and the China's financial market plays a main conductive role, and its conductive effect changes over time.</p> </abstract>

Handle with Care: Regulatory Easing in Times of COVID-19

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fabián Valencia ◽  
Richard Varghese ◽  
Weijia Yao ◽  
Juan F. Yépez
Keyword(s):  
Stock Prices ◽  
Financial Sector ◽  
Capital Regulation ◽  
Policy Response ◽  
Bank Capital ◽  
Financial Conditions ◽  
Bank Capital Regulation ◽  
Advanced Economies ◽  
Key Driver ◽  
Liquidity Regulation

Abstract The policy response to the COVID-19 shock included regulatory easing across many jurisdictions to facilitate the flow of credit to the economy and mitigate a further amplification of the shock through tighter financial conditions. Using an intraday event study, this paper examines how stock prices – a key driver of financial conditions – reacted to regulatory easing announcements in a sample of 18 advanced economies and 8 emerging markets. It finds that regulatory easing announcements contributed to looser financial conditions but effects varied across sectors and tools. News about regulatory easing led to lower valuations for financial sector stocks, mainly in jurisdictions with relatively lower capital buffers. These results stand in stark contrast with valuations of non-financial sector stocks, which increased in response to regulatory relief announcements, particularly in industries that are more dependent on bank financing. The effects also differed across tools. Valuations declined and financial conditions tightened following announcements related to easier bank capital regulation while equity valuation rose and financial conditions loosened after those about liquidity regulation.

Has The Accuracy of Macroeconomic Forecasts for Germany Improved?

2013 ◽  
Vol 14 (2) ◽  
pp. 235-253 ◽  
Author(s):  
Ullrich Heilemann ◽  
Herman O. Stekler
Keyword(s):  
Gdp Growth ◽  
Real Gdp ◽  
Macroeconometric Model ◽  
Ex Post ◽  
One Year ◽  
The U.S ◽  
Over Time

Abstract This study asks whether the accuracy of macroeconomic forecasts for Germany has improved over time. We examine one-year-ahead forecasts of rates of real GDP growth and inflation for the years 1967-2010, by three major German forecasters and the OECD. We find that overall error levels are high but not much different from those of the U.S. and U.K. In the 1980s and 1990s accuracy improved somewhat, but has now returned to its 1970s level, indicating that it reflects the variance of growth and inflation. Benchmark comparisons with these predictions with ex post forecasts of a macroeconometric model indicate that accuracy can be improved, but it will be difficult to achieve.

scholarly journals Conditional expectation estimation through attributable components

2018 ◽  
Vol 7 (4) ◽  
pp. 727-754 ◽  
Author(s):  
Esteban G Tabak ◽  
Giulio Trigila
Keyword(s):  
Conditional Distribution ◽  
General Framework ◽  
Optimal Transport ◽  
Data Driven ◽  
Categorical Variables ◽  
Order Structure ◽  
One Dimensional ◽  
Conditional Mean ◽  
Full Conditional Distribution

Abstract A general methodology is proposed for the explanation of variability in a quantity of interest x in terms of covariates z = (z1, …, zL). It provides the conditional mean $\bar{x}(z)$ as a sum of components, where each component is represented as a product of non-parametric one-dimensional functions of each covariate zl that are computed through an alternating projection procedure. Both x and the zl can be real or categorical variables; in addition, some or all values of each zl can be unknown, providing a general framework for multi-clustering, classification and covariate imputation in the presence of confounding factors. The procedure can be considered as a preconditioning step for the more general determination of the full conditional distribution $\boldsymbol{\rho}(x|z) $ through a data-driven optimal-transport barycenter problem. In particular, just iterating the procedure once yields the second order structure (i.e. the covariance) of $\boldsymbol{\rho}(x|z) $. The methodology is illustrated through examples that include the explanation of variability of ground temperature across the continental United States and the prediction of book preference among potential readers.

What Explains FDI Inflows to ASEAN Countries? Evidence from Quantile Regressions

2021 ◽  
pp. 263168462098277
Author(s):  
Jagadish Prasad Sahu ◽  
Santosh Kumar Dash
Keyword(s):  
Natural Resources ◽  
Multinational Corporations ◽  
Conditional Distribution ◽  
Fixed Effects ◽  
Financial Sector ◽  
Political Regime ◽  
Quantile Regressions ◽  
Fdi Inflows ◽  
Financial Sector Development ◽  
Asean Countries

This article examines the determinants of foreign direct investment (FDI) inflows to five economies of the Association of Southeast Asian Nations (ASEAN) for the period 1985–2017. Using the fixed effects quantile regression (QR) method, we identify the differential effects of the covariates across the conditional distribution of FDI inflows. The results suggest that financial sector development and natural resources in the host country are the most significant determinants of FDI inflows to the ASEAN region. Financial sector development is significant at both higher and lower quantiles of the distribution of FDI inflows, whereas natural resources and political regime are significant at the upper quantiles. The findings suggest that while financial sector development is an important factor for attracting FDI, natural resources and political regime matter for large inflows of FDI to the ASEAN countries. The article concludes that the resource-seeking and the efficiency-seeking motives of the multinational corporations are the primary reasons of large inflows of FDI to the ASEAN countries.

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