Съдържанието не е налично на български език.
Robert F. Engle
- 11 October 2023
- WORKING PAPER SERIES - No. 2856Details
- Abstract
- The systemic risk measure (SRISK) by V-Lab provides a market view of the vulnerability of financial institutions to a sudden downturn in the economy. To overcome the shortcoming that it cannot be applied to non-listed banks, SRISK characteristics of listed banks are mapped on balance sheet information. Systemic risk tends to be higher for banks that are larger, less profitable and have lower equity funding. Balance sheet information provides a surprisingly good approximation of SRISK for non-listed banks, when compared with banks’ capital depletion from the EU-wide stress testing exercises in 2018 and 2021. The proposed methodology can usefully complement the more thorough overview provided by traditional stress tests, providing supervisors the option to evaluate the systemic risks of the banking system at a higher frequency and at a fraction of the costs.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
G1 : Financial Economics→General Financial Markets
- 2 June 2021
- WORKING PAPER SERIES - No. 2565Details
- Abstract
- Macro-prudential authorities need to assess medium-term downside risks to the real economy, caused by severe financial shocks. Before activating policy measures, they also need to consider their short-term negative impact. This gives rise to a risk management problem, an inter-temporal trade-off between expected growth and downside risk. Predictive distributions are estimated with structural quantile vector autoregressive models that relate economic growth to measures of financial stress and the financial cycle. An empirical study with euro area and U.S. data shows how to construct indicators of macro-prudential policy stance and to assess when interventions may be beneficial.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models - Network
- Research Task Force (RTF)
- 1 January 2003
- WORKING PAPER SERIES - No. 204Details
- Abstract
- This paper investigates the presence of asymmetric conditional second moments in international equity and bond returns. The analysis is carried out through an asymmetric version of the Dynamic Conditional Correlation model of Engle (2002). Widespread evidence is found that national equity index return series show strong asymmetries in conditional volatility, while little evidence is seen that bond index returns exhibit this behaviour. However, both bonds and equities exhibit asymmetry in conditional correlation. Worldwide linkages in the dynamics of volatility and correlation are examined. It is also found that beginning in January 1999, with the introduction of the Euro, there is significant evidence of a structural break in correlation, although not in volatility. The introduction of a fixed exchange rate regime leads to near perfect correlation among bond returns within EMU countries. However, equity return correlation both within and outside the EMU also increases after January 1999.
- JEL Code
- F3 : International Economics→International Finance
G1 : Financial Economics→General Financial Markets
C5 : Mathematical and Quantitative Methods→Econometric Modeling
- 1 August 2001
- WORKING PAPER SERIES - No. 75Details
- Abstract
- The main objective of this paper is to survey and evaluate the performance of the most popular univariate VaR methodologies, paying particular attention to their underlying assumptions and to their logical flaws. In the process, we show that the Historical Simulation method and its variants can be considered as special cases of the CAViaR framework developed by Engle and Manganelli (1999). We also provide two original methodological contributions. The first one introduces the extreme value theory into the CAViaR model. The second one concerns the estimation of the expected shortfall (the expected loss, given that the return exceeded the VaR) using a regression technique. The performance of the models surveyed in the paper is evaluated using a Monte Carlo simulation. We generate data using GARCH processes with different distributions and compare the estimated quantiles to the true ones. The results show that CAViaR models perform best with heavy-tailed DGP.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
G22 : Financial Economics→Financial Institutions and Services→Insurance, Insurance Companies, Actuarial Studies