We propose a nonparametric framework for estimating the extremal index that captures the persistence of extreme observations. The framework provides unified and simple procedures for verifying the well-known local dependence condition D(ᵈ) (un), which characterizes the extremal index yet is...

This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

Extreme outcomes depend not only on shock tails but also on heterogeneity in how many opportunities agents get to sample. In the mixed-Poisson search framework, a randomly drawn agent's normalized maximum converges to Hγ,F (x) = P0 (vγ (x)) , a Laplace-transform mixture of a classical...

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA-GJR-GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework...

This paper examines novel survey evidence on firms’ beliefs about macroeconomic tail risk and their role in investment decisions. In a large survey of German firms, I elicit (i) the subjective probability of a severe macroeconomic downturn and (ii) firms’ exposure to such an event. I...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

Accurate estimation of tail dependence is difficult due to model misspecification and data contamination. This paper introduces a class of minimum f-divergence estimators for the tail dependence coefficient that unifies robust estimation with extreme value theory. I establish strong consistency...

This paper proposes feasible inference methods for extremal expected shortfall (ES) regressions. While standard ES regressions consider a fixed probability level, in extremal ES regressions the probability level becomes more extreme as a function of the sample size. We show that in extremal ES...

The maximum drawdown of a stochastic process is the largest peak-to-trough decline observed over a given horizon [0, T]. Using arguments from extreme value theory, we derive the limiting distribution of the maximum drawdown for a Brownian motion with positive drift as T → ∞. We show that,...

This paper presents a Machine Learning approach adopted at the Statistics Department of the Central Bank of Malta to detect outliers in the Maltese Pension Schemes and Insurance datasets, which are collected by the Bank, at micro-level. The motive behind this study is to develop an outlier...

Studying the extreme value theory (EVT) involves multiple main objectives, among them the estimation of the tail index parameter. Some estimation methods are used to estimate the tail index parameter like maximum likelihood estimation (MLE). Additionally, the Hill estimator is one type of...

This paper presents a Machine Learning approach adopted at the Statistics Department of the Central Bank of Malta to detect outliers in the Maltese Pension Schemes and Insurance datasets, which are collected by the Bank, at micro-level. The motive behind this study is to develop an outlier...

I introduce a simple model which endogenously generates a Pareto distribution in top earnings. Workers inhabit different niches, and the earnings of a worker is determined by the niche-specific supply of labor and a downward-sloping labor demand curve. The highest paid workers are the ones that...

This study investigates the simplicity and adequacy of tail-based risk measures-valueat-risk (VaR) and expected shortfall (ES)-when applied to tail targeting of the extreme value (EV) model. We implement Lévy-VaR and ES risk measures as full density-based alternatives to the generalized Pareto...

This paper explores the financial stability implications of acute physical climate change risks using a novel approach focusing on a severe season associated with a series of tropical cyclone and flood events. Our approach was recently applied to study physical risks in the Mexican financial...

In this study, we use daily gold log-returns to analyse the quality of forecasting expected shortfalls (ES) using volatility and models based on the extreme value theory (EVT). ES forecasts were calculated for conditional APARCH models formed on the entire distribution of returns, as well as for...

We use extreme value theory to study idiosyncratic tail risk for a large panel of US stocks. Surprisingly, calls and puts contain important information about the lower and upper tails, respectively. Furthermore, the direction of this information is often wrong: Over prolonged periods of time,...

This article contributes to the quantification of systemic risk within the Moroccan banking system, focusing on listed banks. We utilize indicators derived from Tail Value at Risk and expectiles risk measures, as introduced by El qalli and Said (2013) (El Qalli & Said, 2023), to measure the...

Cryptocurrencies are said to be very risky, and so are the currencies of emerging economies, including the South African rand. The steady rise in the movement of South Africans' investments between the rand and BitCoin warrants an investigation as to which of the two currencies is riskier. In...

Financial market data are abundant with outliers, and the search for an appropriate extreme value theory (EVT) approach to apply is an endless debate in the statistics of extremes research. This paper uses EVT methods to model the five-year daily all-share total return index (ALSTRI) and the...

In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a rally thereafter. We also introduce a...