Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: eL_~4iaY#oRsnwlc}].W7/r!I^g]#[]8RjxXIJg`4fb4dRcf~ODoWuROT

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 3

WP(20/2026)514. Hedging Auction Volatility with Gap Call Options

Autorzy: Mbara Gilbert
We investigate the feasibility of hedging commodity price risk using gap call options within an auction-based market devoid of traditional derivatives. Using a novel, high-frequency dataset from the Nairobi Coffee Exchange (NCE), we model spot price …

WP514. Hedging Auction Volatility with Gap Call Options

We investigate the feasibility of hedging commodity price risk using gap call options within an auction-based market devoid of traditional derivatives. Using a novel, high-frequency dataset from the Nairobi Coffee Exchange (NCE), we model spot price dynamics by deriving a Geometric Brownian Motion process from the independent private values paradigm. The estimated model captures the unique microstructure of the NCE, where discrete weekly auctions generate prices characterized by extreme volatility (146.5% annualized). We utilize Monte Carlo simulation to price and evaluate the performance of gap call options for buyers seeking protection against catastrophic price spikes. Our results demonstrate that gap options – characterized by a trigger price higher than the strike – provide superior risk-adjusted returns compared to standard European calls. Our study offers a practical framework for developing tailored risk management instruments in emerging commodity exchanges, and provides empirical evidence for the viability of gap options as a cost-effective hedging tool in high-volatility, institutionally constrained markets.


DOI: https://doi.org/10.33138/2957-0506.2026.20.514
Mbara Gilbert
risk management hedging commodity price risk gap options Nairobi Coffee Exchange geometric Brownian motion Monte Carlo simulation

WP(28/2020)334. Value-at-risk — the comparison of state-of-the-art models on various assets

Autorzy: Kielak Karol, Ślepaczuk Robert
This paper compares different approaches to Value-at-Risk measurement based on parametric and non-parametric approaches. Three portfolios are taken into consideration — the first one containing only stocks from the London Stock Exchange, the se…

WP334. Value-at-risk — the comparison of state-of-the-art models on various assets

This paper compares different approaches to Value-at-Risk measurement based on parametric and non-parametric approaches. Three portfolios are taken into consideration — the first one containing only stocks from the London Stock Exchange, the second one based on different assets of various origins and the third one consisting of cryptocurrencies. Data used cover the period of more than 20y. In the empirical part of the study, parametric methods based on mean-variance framework are compared with GARCH(1,1) and EGARCH(1,1) models. Different assumptions concerning returns' distribution are taken into consideration. Adjustment for the fat tails effect is made by using Student t distribution in the analysis. One-day-ahead 95%VaR estimation is then calculated. Thereafter, models are validated using Kupiec and Christoffersen tests and Monte Carlo Simulation for reliable verification of the hypotheses. The overall goal of this paper is to establish if analyzed models accurately estimate Value-at-Risk measure, especially if we take into account assets with various returns distribution characteristics.


Kielak Karol, Ślepaczuk Robert
risk management Value-at-Risk GARCH models returns distribution Monte Carlo Simulation asset class cryptocurrencies

WP(32/2013)117. Robust estimation of the Pareto index: A Monte Carlo Analysis

Autorzy: Brzeziński Michał
The Pareto distribution is often used in many areas of economics to model the right tail of heavy-tailed distributions. However, the standard method of estimating the shape parameter (the Pareto index) of this distribution the maximum likelihood esti…

WP117. Robust estimation of the Pareto index: A Monte Carlo Analysis

The Pareto distribution is often used in many areas of economics to model the right tail of heavy-tailed distributions. However, the standard method of estimating the shape parameter (the Pareto index) of this distribution the maximum likelihood estimator (MLE) is non-robust, in the sense that it is very sensitive to extreme observations, data contamination or model deviation. In recent years, a number of robust estimators for the Pareto index have been proposed, which correct the deficiency of the MLE. However, little is known about the performance of these estimators in small-sample setting, which often occurs in practice. This paper investigates the small-sample properties of the most popular robust estimators for the Pareto index, including the optimal B-robust estimator (OBRE) (Victoria-Feser and Ronchetti, 1994, The Canadian Journal of Statistics 22: 247-258), the weighted maximum likelihood estimator (WMLE) (Dupuis and Victoria-Feser, 2006, Canadian Journal of Statistics 34: 639-658), the generalized median estimator (GME) (Brazauskas and Serfling, 2001a, Extremes 3, 231-249), the partial density component estimator (PDCE) (Vandewalle et al., 2007, Computational Statistics & Data Analysis 51: 6252-6268), and the probability integral transform statistic estimator (PITSE) (Finkelstein et al., 2006, North American Actuarial Journal 10, 1-10). Monte Carlo simulations show that the PITSE offers the desired compromise between ease of use and power to protect against outliers in the small-sample setting. 


Brzeziński Michał
Pareto distribution Pareto index power-law distribution robust estimation Monte Carlo simulation small-sample performance
  • 1