Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Number of Results 48

WP(22/2026)516. When Risk Information Changes the Trip: Evidence from a Randomized Panel Combining Discrete Choice and Travel Cost Methods

Authors: Czajkowski Mikołaj, Zawadzki Wojciech, Skrzypek Katarzyna, Budziński Wiktor, Ščasnŷ Milan
Coastal bathing delivers large welfare benefits but exposes recreationists to low-probability, high-salience microbial risks that are likely to become more frequent under climate change. Because these risks are largely invisible, behaviour and welfar…

WP516. When Risk Information Changes the Trip: Evidence from a Randomized Panel Combining Discrete Choice and Travel Cost Methods

Coastal bathing delivers large welfare benefits but exposes recreationists to low-probability, high-salience microbial risks that are likely to become more frequent under climate change. Because these risks are largely invisible, behaviour and welfare depend on beliefs and the effectiveness of risk communication. We provide causal evidence on how pathogen-risk information affects preferences and recreation demand using a three-wave panel survey of users of the Gulf of Gdańsk and the Vistula Lagoon (Poland). A stratified national sample identified 3,312 active coastal users in Wave 1 (spring 2024); 2,588 respondents returned in Wave 2 (summer 2024), where they were randomly assigned to receive either minimal information or increasingly detailed pathogen-risk scripts, and then completed a repeated beach-site discrete choice experiment. Approximately one year later (spring 2025), 1,507 users completed a policy-referendum discrete choice experiment on programs combining water-quality improvements, monitoring frequency, and household costs, alongside a repeated travel-cost module capturing multi-day trips and beach outings. Information treatments significantly increased objective and self-assessed knowledge and selectively raised willingness to travel/pay for risk-relevant attributes – especially frequent water-quality monitoring and water-quality improvements – while leaving unrelated attributes largely unchanged. Travel-cost models indicate that information affects trip-taking behaviour, yet the marginal travel-cost sensitivity remains stable, consistent with a demand shift rather than a change in the “price” slope. The results imply that welfare estimates are information-dependent and that credible risk communication can function as a scalable, low-cost complement to traditional coastal health-risk management.


DOI: https://doi.org/10.33138/2957-0506.2026.22.516
Czajkowski Mikołaj, Zawadzki Wojciech, Skrzypek Katarzyna, Budziński Wiktor, Ščasnŷ Milan
Bathing water quality Pathogens Microbial contamination Risk communication Information treatments Discrete choice experiment (DCE) Travel cost method (TCM) Recreation demand Welfare measurement Consumer surplus Climate adaptation Coastal health risks Baltic Sea Poland

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

Authors: 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(4/2026)498. Behavioral Biases in Stated Preference Valuation of Mortality Risk Reductions: Cost Vector, Anchoring, and Scope Effects

Authors: Zawadzki Wojciech, Andersson Henrik, Czajkowski Mikołaj, Hole Arne Risa
This study investigates how behavioral biases influence stated preference valuation of mortality risk reductions, commonly summarized as the value of a statistical life (VSL). Using a discrete choice experiment (DCE) combined with a contingent valuat…

WP498. Behavioral Biases in Stated Preference Valuation of Mortality Risk Reductions: Cost Vector, Anchoring, and Scope Effects

This study investigates how behavioral biases influence stated preference valuation of mortality risk reductions, commonly summarized as the value of a statistical life (VSL). Using a discrete choice experiment (DCE) combined with a contingent valuation double-bounded dichotomous choice and an open-ended follow-up, we elicit individuals’ willingness to pay (WTP) for cardiovascular mortality risk reductions. In a randomized design, we varied the cost attribute across three cost range treatments and manipulated information disclosure and feedback to examine three behavioral phenomena: cost vector effects (whether the range of costs presented affects WTP), scope insensitivity (whether WTP scales appropriately with the magnitude of the risk reduction), and anchoring (whether initial cost cues affect subsequent responses). Our results show that mean VSL estimates can vary by up to ~25% between cost treatments. Furthermore, WTP responses exhibit partial scope insensitivity – larger risk reductions do not proportionally increase WTP – indicating a deviation from theoretical expectations. Importantly, we find no strong evidence of anchoring: neither revealing all attribute levels upfront, nor starting with extreme cost levels, nor providing feedback on quiz questions significantly affected respondents’ choices or WTP. Our findings underscore the need for careful survey design. Even if VSL distributions remain statistically similar across cost frames, substantial shifts in mean magnitudes could be consequential for policy. We call for standardized guidelines on cost attribute selection and survey protocols to mitigate bias, ensuring that stated preference methods yield reliable welfare estimates for health policy decisions.


DOI: https://doi.org/10.33138/2957-0506.2026.4.498
Zawadzki Wojciech, Andersson Henrik, Czajkowski Mikołaj, Hole Arne Risa
value of statistical life (VSL) stated preference (SP) contingent valuation (CV) behavioral biases anchoring effect scope insensitivity discrete choice experiment (DCE) willingness to pay (WTP) mortality risk reduction cardiovascular diseases

WP(14/2024)450. Construction and Hedging of Equity Index Options Portfolios

Authors: Wysocki Maciej, Ślepaczuk Robert
This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma …

WP450. Construction and Hedging of Equity Index Options Portfolios

This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and different sizing methods based on delta and the VIX Index. The study employs 1-minute data of S&P500 index options and index quotes spanning from 2018 to 2023. The analysis benchmarks hedged strategies against buy-and-hold and naked option-writing strategies, with a focus on risk-adjusted performance metrics including transaction costs. Portfolio delta approximations are derived using implied volatility for the BSM model and market-calibrated parameters for the VG model. Key findings reveal that system atic option-writing strategies can potentially yield superior returns compared to buy-and-hold benchmarks. The BSM model generally provided better hedging outcomes than the VG model, although the VG model showed profitability in certain naked strategies as a tool for position sizing. In terms of rehedging frequency, we found that intraday heding in 130-minute intervals provided both reliable protection against adverse market movements and a satisfactory returns profile.


DOI: https://doi.org/10.33138/2957-0506.2024.14.450
Wysocki Maciej, Ślepaczuk Robert
S&P500 Index options Option Pricing Models Black-Scholes-Merton model Variance-Gamma model Implied Volatility Volatility Risk Premium Volatility Spreads Dynamic Hedging

WP(12/2024)448. Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data

Authors: Zuzanna Kostecka, Ślepaczuk Robert
The scope for the accurate calculation of the Loss Given Default (LGD) parameter is comprehensive in terms of financial data. In this research, we aim to explore methods for improving the approximation of realized LGD in conditions of limited access …

WP448. Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data

The scope for the accurate calculation of the Loss Given Default (LGD) parameter is comprehensive in terms of financial data. In this research, we aim to explore methods for improving the approximation of realized LGD in conditions of limited access to the cash-flow data. We enhance the performance of the method which relies on the differences between exposure values (delta outstanding approach) by employing the machine learning (ML) techniques. The research utilizes the data from the mortgage portfolio of one of the European countries and assumes the close resemblance for similar economic contexts. It incorporates non-financial variables and macroeconomic data related to the housing market, improving the accuracy of loss severity approximation. The proposed methodology attempts to mitigate the country-specific (related to the local legal) or portfolio-specific factors in aim to show the general advantage of applying ML techniques, rather than case-specific relation. We developed an XGBoost model that does not rely on cash-flow data yet enhances the accuracy of realized LGD estimation compared to results obtained with the delta outstanding approach. A novel aspect of our work is the detailed exploration of the delta outstanding approach and the methodology for addressing conditions of limited access to cash-flow data through machine learning models.


DOI: https://doi.org/10.33138/2957-0506.2024.12.448
Zuzanna Kostecka, Ślepaczuk Robert
LGD Credit risk Outstanding Machine Learning Missing data Mortgage loan financial statements macroeconomic data

WP(9/2024)445. Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market

Authors: Korniejczuk Adam, Ślepaczuk Robert
The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of …

WP445. Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market

The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase the immunity to transaction costs over existing approaches.  The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to the changes of some key parameters


DOI: https://doi.org/10.33138/2957-0506.2024.9.445
Korniejczuk Adam, Ślepaczuk Robert
graph clustering algorithms statistical arbitrage algorithmic investment strategies pair trading strategy Kelly criterion machine learning risk adjusted returns

WP(4/2024)440. Welfare and economic implications of universal child benefits

Authors: Kolasa Aleksandra
Universal child benefits are an important component of the social protection systems in many developed economies, particularly in Europe. When evaluating their impact, most studies tend to focus primarily on the empirical evidence and short-term effe…

WP440. Welfare and economic implications of universal child benefits

Universal child benefits are an important component of the social protection systems in many developed economies, particularly in Europe. When evaluating their impact, most studies tend to focus primarily on the empirical evidence and short-term effects. However, given their large-scale implementation, such programs can have sizable general equilibrium effects. The aim of this paper is to study the long-run implications of universal child benefits within a theoretical framework that can capture the complexities of household decisions regarding consumption, labor participation, and the timing of children. To this end, I develop an overlapping generations model with idiosyncratic earnings risk, infertility shocks, and endogenous temporal fertility. According to the model simulations, universal child benefits lead to a reduction in the spacing between children and, on average, lower maternal age at childbirth for all births. This, in turn, alleviates some of the negative aggregate effects typically associated with redistributive policies, but has a detrimental impact on the average quality of children. Finally, universal child benefits increase ex-ante welfare by 0.42% of lifetime adult consumption, significantly outperforming broad-based transfer policies not tied to the number of children.


Kolasa Aleksandra
universal child benefits infertility risk temporal fertility social welfare general equilibrium models

WP(3/2024)439. Supervised Autoencoder MLP for Financial Time Series Forecasting

Authors: Bieganowski Bartosz, Ślepaczuk Robert
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation…

WP439. Supervised Autoencoder MLP for Financial Time Series Forecasting

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.


Bieganowski Bartosz, Ślepaczuk Robert
machine learning algorithmic investment strategy supervised autoencoders financial time series trading strategy risk-adjusted return

WP(22/2023)429. Framing-induced emotions affect performance in simple cognitive tasks under risk

Authors: Rachubik Joanna
In this study, we investigated how performance in a number of puzzles (decisions under risk) depended on the framing. The puzzles, drawn and adapted from existing literature, were designed to expose well-established cognitive biases could lead respon…

WP429. Framing-induced emotions affect performance in simple cognitive tasks under risk

In this study, we investigated how performance in a number of puzzles (decisions under risk) depended on the framing. The puzzles, drawn and adapted from existing literature, were designed to expose well-established cognitive biases could lead respondents to select intuitive yet incorrect answer. Subjects were randomly assigned to one of three treatments: a third of the sample saw puzzles framed in terms of COVID-19, another third about a common cold, and the remaining group about unemployment. Across five continents, we collected over 8,000 observations. We found that framing of the puzzles affected performance, prompting questions regarding the external validity of these puzzles. Treatments associated with more severe threats, such as COVID and Unemployment, elicited stronger (negative) emotions compared to the common cold. Moreover, these emotional reactions were also linked to performance, and their levels correlated negatively with the number of correctly solved puzzles.


Rachubik Joanna
decision-making under risk framing emotions cognitive biases cognitive tasks COVID-19

WP(7/2023)414. The systemic risk approach based on implied and realized volatility

Authors: Sakowski Paweł, Sieradzki Rafał, Ślepaczuk Robert
We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2021 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (I…

WP414. The systemic risk approach based on implied and realized volatility

We propose a new measure of systemic risk to analyze the impact of the major financial market turmoils in the stock markets from 2000 to 2021 in the USA, Europe, Brazil, and Japan. Our Implied Volatility Realized Volatility Systemic Risk Indicator (IVRVSRI) shows that the reaction of stock markets varies across different geographical locations and the persistence of the shocks depends on the historical volatility and long-term average volatility level in a given market. The methodology applied is based on the logic “the simpler is always better than the more complex, if it leads to the same results”. Such an approach significantly limits the model risk and substantially decreases computational burden. Robustness checks show that IVRVSRI is a precise measure of the current systemic risk in the stock markets. Moreover, IVRVSRI seems to be a valid indication of current systemic risk in equity markets and it can be used for other types of assets and high-frequency data.


Sakowski Paweł, Sieradzki Rafał, Ślepaczuk Robert
systemic risk implied volatility realized volatility volatility indices equity index options market volatility