Publikationen

Unsere Forschungergebnisse unterstützen die gesellschaftliche Debatte rund um aktuelle finanzökonomische Fragestellungen. Durch die Veröffentlichung der Arbeiten in internationalen Fachzeitschriften und unserer Working Paper Series sollen diese für einen möglichst breiten Adressatenkreis zugänglich werden.

HFRC Working Paper Series

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Publikationen von Tatjana Xenia Puhan

Bootstrapping and bias: The economic costs of misjudging downside risk

Hubert Dichtl, Wolfgang Drobetz, Tizian Otto, Tatjana Xenia Puhan
HFRC Working Paper Series | Version 03/2025
The maximum drawdown (MDD), the maximum peak-to-trough loss associated with a series of returns, is a simple but highly important measure for investors with a downside risk budget. This paper compares the performance of three bootstrap simulation methods to estimate the entire distribution of MDDs from various global stock-bond allocations, quantifying the economic costs of biased estimates for three realistic decision-making scenarios. Compared to its benchmarks, the stationary bootstrap of Politis and Romano (1994) leads to the most precise estimates for the MDD which, in turn, helps avoid costly investment errors in portfolio construction and dynamic risk control strategies.

Predicting corporate bond illiquidity via machine learning

Axel Cabrol, Wolfgang Drobetz, Tizian Otto, Tatjana Xenia Puhan
Financial Analysts Journal | 05/2024 | Forthcoming
This paper tests the predictive performance of machine learning methods in estimating the illiquidity of U.S. corporate bonds. Machine learning techniques outperform the historical illiquidity-based approach, the most commonly applied benchmark in practice, from both a statistical and an economic perspective. Tree-based models and neural networks outperform linear regressions, which incorporate the same set of covariates. Gradient boosted regression trees perform particularly well. Historical illiquidity is the most important single predictor variable, but several fundametal and return- as well as risk-based covariates also possess predictive power. Capturing interactions and nonlinear effects among these predictors further enhances predictive performance.

Drawdowns in stock and crypto markets. What is the best bootstrapping method?

Hubert Dichtl, Wolfgang Drobetz, Tizian Otto, Tatjana Xenia Puhan
HFRC Working Paper Series | Version 04/2024
This paper compares bootstrap simulation approaches in the context of the maximum drawdown (MDD) risk measure for stock market and cryptocurrency returns. Our comparisons are based on the complete distribution of the MDD using stochastic dominance tests. The standard Efron (1979) bootstrap severely underestimates the true MDD. The simulation results of the moving block bootstrap approach are reasonably good as long as the stationarity problem does not become striking. The stationary bootstrap approach of Politis and Romano (1994) provides the best results. Investment practitioners should choose the Politis and Romano (1994) method as their first choice to model MDD risk.

Cyclicality of growth opportunities and the value of cash holdings

Meike Ahrends, Wolfgang Drobetz, Tatjana Xenia Puhan
Journal of Financial Stability | 08/2018
We show that business cycle dynamics and, in particular, the cyclicality of a firm’s growth opportunities, determine the value of corporate cash holdings. An additional dollar of cash is more valuable for firms with less procyclical expansion opportunities. This valuation effect is strongest for low leverage and high R&D firms, but is independent of their financial status. Corporate cash holdings provide the flexibility to invest for firms that have expansion opportunities during crisis times with business cycle downturns and supply-side financial constraints. Cash holdings in firms with less procyclical growth opportunities are associated with higher investment and better operating performance.