Generalized Extreme Value Distribution and Extreme Economic Value at Risk (EE-VaR)

Alentorn, Amadeo; Markose, Sheri M

doi:10.1007/978-3-540-77958-2_3

Cited by 8 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the presence of non-stationarity in the time series leads to the use of numerical optimisation techniques, which allow the best estimation of the return level, such as maximum likelihood, which shows good results in non-stationary series in the literature [11]. The GEV method is used for the prediction of extreme events for each climatic factor, such as precipitation, temperature, wind and all those climatic factors, which could lead to hazards with their extreme manifestation [12,13]; however, the GEV is also widely used in economics, especially aimed at predicting rather negative economic scenarios or risks and in engineering [14,15]. In any case, the most widespread application of the GEV method is the definition of maximum rainfall return values due to the reliability of the forecast, although it sometimes seems to be penalised, as some research has shown, by estimates based on short time series because they make it difficult to estimate the shape parameter and also due to possible errors in the measurements [16].…”

Section: Aim Of the Study And State Of The Artmentioning

confidence: 99%

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

Gentilucci,

Rossi,

Pelagagge

et al. 2023

Sustainability

View full text Add to dashboard Cite

The extreme rainfall events of recent years in central Italy are producing an increase in hydrogeological risk, with disastrous flooding in terms of human lives and economic losses, as well as triggering landslide phenomena in correspondence with these events. A correct prediction of 100-year return levels could encourage better land planning, sizing works correctly according to the expected extreme events and managing emergencies more consciously through real-time alerts. In the recent period, it has been observed that the return levels predicted by the main forecasting methods for extreme rainfall events have turned out to be lower than observed within a few years. In this context, a model widely used in the literature, the generalised extreme value (GEV) with the “block maxima” approach, was used to assess the dependence of this model on the length of the collected precipitation time series and the possible addition of years with extreme events of great intensity. A total of 131 rainfall time series were collected from the Adriatic slope in central Italy comparing two periods: one characterised by 70 years of observations (1951–2020), the other by only 30 years (1991–2020). At the same time, a decision was made to analyse what the effect might be—in terms of the 100-year return level—of introducing an additional extreme event to the 1991–2020 historical series, in this case an event that actually occurred in the area on 15 September 2022. The results obtained were rather surprising, with a clear indication that the values of the 100-year return level calculated by GEV vary according to the length of the historical series examined. In particular, the shorter time series 1991–2020 provided higher return level values than those obtained from the 1951–2020 period; furthermore, the addition of the extreme event of 2022 generated even higher return level values. It follows that, as shown by the extreme precipitation events that have occurred in recent years, it is more appropriate to consider a rather short period because the ongoing climate change does not allow true estimates to be obtained using longer time series, which are preferred in the scientific literature, or possibly questioning the real reliability of the GEV model.

show abstract

Section: Aim Of the Study And State Of The Artmentioning

confidence: 99%

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

Gentilucci,

Rossi,

Pelagagge

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Furthermore, we present a nonparametric method to compute risk‐neutral tail risk quantities. Many methods have been proposed for extracting risk‐neutral distributions from the cross‐section of option prices (Ait‐Sahalia & Lo, 2000; Alentorn & Markose, 2008; Figlewski, 2010; Panigirtzoglou & Skiadopoulos, 2004). We adapt a nonparametric method to the proposed option‐implied tail risk indexes.…”

Section: Introductionmentioning

confidence: 99%

Stock market tail risk, tail risk premia, and return predictability

Suh

Yoo²,

Yoon

2021

Journal of Futures Markets

View full text Add to dashboard Cite

In this study, we use the S&P 500 options prices to derive various tail risk indexes. We then decompose the option‐implied tail risk indexes into the conditional tail risk of stock returns and equity tail risk premia. We examine the predictive power of the conditional tail risks and equity tail risk premia for various stock portfolio returns. The results demonstrate that the tail risk indicators possess additional predictive power for stock returns in the presence of extant risk indicators and other return predictor variables.

show abstract

“…We conclude this introduction by discussing the relation of our work to the existing literature. There is already a significant literature on methods to extract implied PDFs as well as their potential applications to policy-making (see e.g., Söderlind and Svensson (1997)), option pricing and risk management (Ait-Sahalia and Lo (2000), Panigirtzoglou and Skiadopoulos (2004), Alentorn and Markose (2008)) and forecasting the future value of the underlying asset (Bliss and Panigirtzoglou (2004), Anagnou-Basioudis, Bedendo, Hodges and Tompkins (2005), Kang and Kim (2006), and Liu, Shackleton, Taylor and Xu (2007)). Jackwerth (2004) also provides an excellent review of the applications of implied distributions.…”

mentioning

confidence: 99%

Market Timing with Option-Implied Distributions: A Forward-Looking Approach

2011

View full text Add to dashboard Cite

We address the empirical implementation of the static asset allocation problem by developing a forward-looking approach that uses information from market option prices. To this end, we extract constant maturity S&P 500 implied distributions and transform them to the corresponding risk-adjusted ones. Then we form optimal portfolios consisting of a risky and a risk-free asset and evaluate their out-of-sample performance. We find that the use of risk-adjusted implied distributions times the market and makes the investor better off than if she uses historical returns' distributions to calculate her optimal strategy. The results hold under a number of evaluation metrics and utility functions and carry through even when transaction costs are taken into account. Not surprisingly, the reported market timing ability deteriorated during the recent subprime crisis. An extension of the approach to a dynamic asset allocation setting is also presented. This paper was accepted by Wei Xiong, finance.asset allocation, option-implied distributions, market timing, performance evaluation, portfolio choice, risk aversion

show abstract

Generalized Extreme Value Distribution and Extreme Economic Value at Risk (EE-VaR)

Cited by 8 publications

References 24 publications

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy

Stock market tail risk, tail risk premia, and return predictability

Market Timing with Option-Implied Distributions: A Forward-Looking Approach

Contact Info

Product

Resources

About