scholarly journals The Role of the Skewed Distributions in the Framework of Extreme Value Theory (EVT)

2017 ◽  
Vol 10 (11) ◽  
pp. 88
Author(s):  
Sonia Benito Muela ◽  
Carmen López-Martín ◽  
Mª Ángeles Navarro
Keyword(s):  
Extreme Value Theory ◽  
Market Risk ◽  
Heavy Tail ◽  
Capital Requirements ◽  
Value Theory ◽  
Extreme Value ◽  
Skewed Distribution ◽  
Symmetric Distribution ◽  
Volatility Model

In this paper, we analyze the role of the heavy tail and skewed distribution in market risk estimation (Value at Risk (VaR)). In particular, we are interested in knowing if in the framework of the conditional extreme value theory, the estimation of the volatility model below heavy tail and skewed distribution contributes to improve the VaR estimation respect to these obtained from a symmetric distribution. The study has been carried out for six individual assets belonging to the digital sector: ADP, Amazon, Cerner, Apple, Microsoft and Telefonica. The analysis period runs from January 1st, 2008 to the end of December 2013. Although the evidence found is a little bit weak, the results obtained seem to indicate that the heavy tail and skewed distribution outperforms the symmetric distribution both in terms of accuracy VaR estimations as in terms of firm’s loss function. Furthermore, the market risk capital requirements fixed on the base of the VaR estimations are also lowest below a skewed distribution.

Extreme market risk and extreme value theory

2013 ◽  
Vol 94 ◽  
pp. 310-328 ◽  
Author(s):  
Abhay K. Singh ◽  
David E. Allen ◽  
Powell J. Robert
Keyword(s):  
Extreme Value Theory ◽  
Market Risk ◽  
Value Theory ◽  
Extreme Value

scholarly journals ANALISIS RISIKO PORTOFOLIO LQ45 MENGGUNAKAN PENDEKATAN VALUE AT RISK BLOCK MAXIMA-GENERALIZED EXTREME VALUE

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Nanda Ayuni, Setyo Wira Rizki, Hendra Perdana
Keyword(s):  
At Risk ◽  
Extreme Value Theory ◽  
Value At Risk ◽  
Heavy Tail ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Block Maxima

Setiap bentuk investasi memiliki risiko yang dapat menyebabkan kerugian bagi investor. Semakin tinggi hasil yang diharapkan dari investasi tersebut, maka semakin tinggi juga tingkat risikonya. Dengan demikian, investor perlu mengetahui besar risiko yang akan dihadapinya, sehingga dapat melakukan tindakan pencegahan agar bisa mengantisipasi risiko tersebut. Metode yang dapat digunakan untuk mengukur risiko adalah value at risk (VaR). Extreme value theory (EVT) merupakan metode yang digunakan untuk mengukur risiko pada data runtun waktu yang memiliki distribusi ekor gemuk. Distribusi ekor gemuk memiliki kecenderungan lebih besar terjadinya kejadian ekstrem dibandingkan dengan distribusi normal. Umumnya, hal ini ditandai oleh nilai kurtosis yang positif. Salah satu metode EVT adalah block maxima yang mengikuti distribusi GEV (generalized extreme value). Perhitungan VaR yang akurat pada data runtun waktu finansial dapat menggunakan VaR dengan metode block maxima-GEV. Penelitian ini menggunakan data harga saham penutupan harian pada indeks LQ45 periode 1 Januari sampai 31 Desember 2018. Saham yang digunakan untuk pembentukan portofolio ada lima yaitu PTBA, ANTM, PGAS, BBCA, dan ICBP, yang mana saham-saham tersebut dipilih berdasarkan nilai mean return tertinggi. Berdasarkan hasil analisis, diperoleh nilai VaR dengan metode block maxima-GEV dengan tingkat kesalahan 5% adalah sebesar 2,555% dari total nilai investasinya. Misalnya, jika investor berinvestasi sebesar Rp100.000.000,00,- maka investor tersebut mempunyai risiko sebesar Rp2.555.000,00. Kata Kunci : investasi, VaR, extreme value theory, heavy tail

scholarly journals USING EXTREME VALUE THEORY TO IDENTIFY RAILCAR ASYMMETRIC WHEEL WEAR AND ITS BENEFIT ANALYSIS

Transport ◽  
2019 ◽  
Vol 34 (5) ◽  
pp. 569-578
Author(s):  
Yu Cui ◽  
Qing He ◽  
Zhenhua Zhang ◽  
Zhiguo Li
Keyword(s):  
Extreme Value Theory ◽  
Extreme Values ◽  
Early Stage ◽  
Cost Effective ◽  
Value Theory ◽  
Extreme Value ◽  
Skewed Distribution ◽  
Wheel Wear ◽  
Monetary Benefit ◽  
Elliptically Contoured

Railcar asymmetric wheel wear leads to severe wear on one wheel but mild wear on the other wheel. The consequences of the asymmetric wheel include accelerated wear, mechanical failure and downtime, and high financial penalties. Therefore, identifying the asymmetric wheel wear is critical not only for cost effective maintenance but also for safe operations. Fortunately, the increasing amount of various wayside detectors is instrumented along the railway that can monitor the health of railcar components and log plenty of detailed information about railroad operations. One can use this information to identify the asymmetric wheel wear in the early stage. However, most elliptically contoured distributions are effective in describing normal events but not in dealing with the outliers, which mainly locate in the tails of the distribution. Asymmetric wheel wear requires effective anomaly detection that mainly focuses on the extreme values in the tail of a right-skewed distribution. In this paper, we employ the Extreme Value Theory (EVT), which handles the unusually high or low data in the distribution, to derive an extreme value score to identify asymmetric wheel wear. Experiment results show that identification of asymmetric wheel wear can generate huge monetary benefit in terms of reducing average maintenance times of railcars.

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