Technical paper/Covid
Forecasting India’s foreign trade dynamics: evaluation of alternative forecasting models in the post-pandemic period
The authors aim to determine how India's foreign trade will change following Covid-19 and the Russia-Ukraine conflict, comparing several forecasting models and identifying that which performs best.
Does investors’ sentiment influence stock market volatility? Evidence from India during pre- and post-Covid-19 periods
A study of China’s financial market risks in the context of Covid-19, based on a rolling generalized autoregressive score model using the asymmetric Laplace distribution
Dynamic connectedness between energy markets and cryptocurrencies: evidence from the Covid-19 pandemic
Target-date funds: lessons learned?
The authors return to the topic of their 2011 paper and investigate the maturation of target-date funds and their performance during the Covid-19 pandemic, finding that the funds have largely achieved their designation.
The impacts of financial and macroeconomic factors on financial stability in emerging countries: evidence from Turkey’s nonperforming loans
The authors assess the impacts of financial and macroeconomic factors on financial stability in emerging economies, using Turkey's banking sector in the period 2005 Q1 to 2020 Q3 as their example.
Scenario design for macrofinancial stress testing
The author presents an empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables and compares this approach with a historical scenario approach.
The Compliance Index: a behavioral approach to compliance risk management in the (post-) Covid-19 era
This paper proposes the Compliance Index - a behavioral measurement system for controlling and monitoring the effectiveness of compliance programs to mitigate compliance risk - designed in response to the shift to remote working during the Covid-19…
How does the pandemic change operational risk? Evidence from textual risk disclosures in financial reports
The authors investigate changes in operational risk profiles of the financial industry following the Covid-19 pandemic.
Modeling systemic operational risk in the Covid-19 pandemic
This paper introduces existing and novel epidemiology models and investigates how government responses to the Covid-19 pandemic impacted these models.
Changes in operational risk and its determinants under Covid-19
The authors investigate the operational risk impact of the Covid-19 pandemic on Chinese commercial banks and the moderating effect of bank size, business diversification and regulatory records.
Oil value-at-risk forecasts: a filtered semiparametric approach
This paper proposes the GARCH model combined with the Cornish–Fisher expansion for the oil VaR forecast.
Modeling credit risk in the presence of central bank and government intervention
In this paper a simple approach for including central bank and government intervention in credit models is developed and illustrated using the Fed’s data for the CCAR 2021 stress test.
Severe but plausible – or not?
In this paper, the authors apply a measure of statistical unusualness, called the Mahalanobis distance, to assess the plausibility of the scenarios used in the Federal Reserve's stress tests.
Covid-19 and the credit cycle: 2020 revisited and 2021 outlook
This study continues the author’s examination and forecasts as to the impact of Covid-19 on the US credit cycle after one and a half years since the pandemic first began.
Fighting Covid-19 in countries and operational risk in banks: similarities in risk management processes
This paper shows how banks managing operational risk and countries tackling Covid-19 could learn from each other to overcome obstacles in effectively mitigating major risks.
Credit default swap market retrospective: observations from the 2008–9 financial crisis and the onset of the Covid-19 pandemic
In this paper credit market fluctuations, measured by the levels of the main and most heavily traded index instruments, are analyzed and compared with the analogous index realizations during the 2008–9 financial crisis.
NLP and transformer models for credit risk
News feeds are factored into models to predict credit events