arXiv subjects

Jozef Barunik

Publications and source records attributed to Jozef Barunik.

At least 19 recordsLinked to original sources

arXiv2026

Skewness Dispersion and Stock Market Returns

Cross-sectional dispersion in firm-level realized skewness is significantly and negatively related to future stock market returns. The predictive power of skewness dispersion is robust to in-sample and out-of-sample estimation and is incremental over a broad set of existing predictors, with only a few alternatives retaining independent explanatory ability. Skewness dispersion also delivers substantial economic gains in portfolio allocation. Its forecasting power is concentrated in months with monetary policy announcements, reflecting an information-based mechanism. The empirical evidence suggests that skewness dispersion captures the gradual incorporation of macro news into prices, which is driven by variation in aggregate risk and valuation adjustments.

q-fin.GN↗

arXiv2026

When David becomes Goliath: Repo dealer-driven bond mispricing

This paper studies the impact of funding market frictions on bond prices and market-wide liquidity. Using proprietary transaction-level data on all gilt-backed repo and reverse-repo trades, we demonstrate how the market power of individual dealers and their linkages generate frictions. Specifically, we show that frictions related to market power account for between 0.5 and 1.3 percentage points of bond yield deviation, while the transmission of heterogeneously persistent shocks between dealers accounts for between 2 and 4 percentage points of yield deviation.

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arXiv2025

Managing Portfolios Across the Return Distribution

We develop a dynamic portfolio-choice framework in which investors target the region of the payoff distribution that the portfolio is designed to improve. Out of sample, the estimated policies form an ordered frontier: the policy focused on the downside delivers the strongest left-tail protection and the highest Sharpe ratio, while the policy focused on the upper quantile earns the highest mean return. The gains over volatility-managed portfolios are concentrated in periods when downside-tail dispersion is high. Evidence from fund flows in income, growth and downside protection products supports the interpretation of the quantile index as a reduced-form mandate measure.

q-fin.GN↗

arXiv2024

Forecasting stock return distributions around the globe with quantile neural networks

We propose a novel machine learning approach for forecasting the distribution of stock returns using a rich set of firm-level and market predictors. Our method combines a two-stage quantile neural network with spline interpolation to construct smooth, flexible cumulative distribution functions without relying on restrictive parametric assumptions. This allows accurate modelling of non-Gaussian features such as fat tails and asymmetries. Furthermore, we show how to derive other statistics from the forecasted return distribution such as mean, variance, skewness, and kurtosis. The derived mean and variance forecasts offer significantly improved out-of-sample performance compared to standard models. We demonstrate the robustness of the method in US and international markets.

q-fin.GN↗

arXiv2024

Predicting the volatility of major energy commodity prices: the dynamic persistence model

Time variation and persistence are crucial properties of volatility that are often studied separately in energy volatility forecasting models. Here, we propose a novel approach that allows shocks with heterogeneous persistence to vary smoothly over time, and thus model the two together. We argue that this is important because such dynamics arise naturally from the dynamic nature of shocks in energy commodities. We identify such dynamics from the data using localised regressions and build a model that significantly improves volatility forecasts. Such forecasting models, based on a rich persistence structure that varies smoothly over time, outperform state-of-the-art benchmark models and are particularly useful for forecasting over longer horizons.

q-fin.GN↗

arXiv2024

Risks of heterogeneously persistent higher moments

Using intraday data for the cross-section of individual stocks, we show that both transitory and persistent fluctuations in realized market and average idiosyncratic volatility, skewness and kurtosis are differentially priced in the cross-section of asset returns, implying a heterogeneous persistence structure of different sources of higher moment risks. Specifically, we find that idiosyncratic transitory shocks to volatility as well as idiosyncratic persistent shocks to skewness contain strong commonalities that are relevant to investors.

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arXiv2023

Persistence in Financial Connectedness and Systemic Risk

This paper characterises dynamic linkages arising from shocks with heterogeneous degrees of persistence. Using frequency domain techniques, we introduce measures that identify smoothly varying links of a transitory and persistent nature. Our approach allows us to test for statistical differences in such dynamic links. We document substantial differences in transitory and persistent linkages among US financial industry volatilities, argue that they track heterogeneously persistent sources of systemic risk, and thus may serve as a useful tool for market participants.

econ.EM↗

arXiv2023

Learning the Probability Distributions of Day-Ahead Electricity Prices

We propose a novel machine learning approach for probabilistic forecasting of hourly day-ahead electricity prices. In contrast with the recent advances in data-rich probabilistic forecasting, which approximates distributions with few features (such as moments), our method is nonparametric and selects the distribution from all possible empirical distributions learned from the input data without the need for limiting assumptions. The model that we propose is a multioutput neural network that accounts for the temporal dynamics of the probabilities and controls for monotonicity using a penalty. Such a distributional neural network can precisely learn complex patterns from many relevant variables that affect electricity prices. We illustrate the capacity of the developed method on German hourly day-ahead electricity prices and predict their probability distribution via many variables, doing so more accurately than the state-of-the-art benchmarks can, thus revealing new valuable information in the data.

econ.GN↗

arXiv2023

Common Firm-level Investor Fears: Evidence from Equity Options

We identify a new type of risk, common firm-level investor fears, from commonalities within the cross-sectional distribution of individual stock options. We define firm-level fears that link with upward price movements as good fears, and those relating to downward price movements as bad fears. Such information is different to market fears that we extract from index options. Stocks with high sensitivities to common firm-level investor fears earn lower returns, with investors demanding a higher compensation for exposure to common bad fears relative to common good fears. Risk premium estimates for common bad fears range from -5.63% to -4.92% per annum.

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arXiv2023

The Dynamic Persistence of Economic Shocks

We propose a novel framework for modeling time-varying persistence in economic time series, allowing for smoothly evolving heterogeneity in shock dynamics. We leverage localized regression techniques to flexibly identify changes in persistence over time, offering a data-driven alternative to traditional parametric models. We applied this methodology to U.S. inflation and stock market volatility data and found substantial persistence variations that align with key macroeconomic events and market conditions. The results reveal previously undetected pockets of predictability and provide significant increases in out-of-sample forecast accuracy. These findings have important implications for economic modeling, forecasting, and policy analysis.

q-fin.GN↗

arXiv2022

Common Idiosyncratic Quantile Factors and Asset Prices

We investigate whether the tails of firm-level idiosyncratic return distributions are driven by common shocks. We use quantile factor analysis to extract such common idiosyncratic quantile factors with asymmetric pricing effects and we find a significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year. This premium remains significant even when controlling for standard factors, idiosyncratic volatility and tail-risk measures. The downside factor strengthens when intermediary capital is weak and market liquidity is low, and it predicts aggregate market excess returns.

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arXiv2022

Learning Probability Distributions in Macroeconomics and Finance

We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes. Specifically, it is informative to agents facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the proposed approach on the two distinct datasets where a machine learns the pattern from data. First, we construct macroeconomic fan charts that reflect information from high-dimensional data set. Second, we illustrate gains in prediction of stock return distributions which are heavy tailed, asymmetric and suffer from low signal-to-noise ratio.

econ.GN↗

arXiv2021

Dynamic industry uncertainty networks and the business cycle

We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover an uncertainty hub role for the communications, industrials and information technology sectors, while shocks to materials, real estate and utilities do not create strong linkages in the network. Moreover, we find that this ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is based on uncertainty hubs. The industry uncertainty network behaves counter-cyclically in that a tighter network tends to associate with future business cycle contractions.

econ.GN↗

arXiv2021

Volatility Shocks and Currency Returns

This paper examines how shocks to currency volatilities predict exchange rates. Using option-implied volatilities, we construct a dynamic, directed network of volatility connections. Currencies that transmit more volatility shocks, which control for common correlation, earn lower excess returns. Buying the weakest and selling the strongest transmitters delivers high risk-adjusted performance, driven by spot exchange rate movements and not explained by standard factors. A general equilibrium model shows that volatility transmission related to idiosyncratic shocks proxies for priced country-specific risk. Assuming a monotonic amplification of domestic idiosyncratic risk, volatility transmission forecasts negatively future excess returns, consistent with the empirical evidence.

q-fin.GN↗

arXiv2020

Asymmetric Network Connectedness of Fears

This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put traded option prices of the main U.S. banks contains valuable information for predicting macroeconomic conditions and economic uncertainty, and it can serve as a tool for forward-looking systemic risk monitoring.

q-fin.GN↗

arXiv2020

Deep Learning, Predictability, and Optimal Portfolio Returns

We study the dynamic portfolio selection of an investor who uses deep learning methods to forecast stock market excess returns. In a two-asset allocation problem, deep neural networks -- both feedforward and long short-term memory (LSTM) recurrent architectures -- deliver economically significant gains in terms of certainty equivalent returns and Sharpe ratios relative to linear predictive regressions. These gains are robust to alternative performance measures, the inclusion of transaction costs, borrowing and short-selling constraints, different rebalancing horizons, and subsample splits, and are particularly pronounced during NBER recessions and periods with large return swings. Within the class of neural networks we consider, economic performance is broadly similar across architectures, with the recurrent LSTM specification providing incremental benefits with more frequent rebalancing. Overall, our evidence suggests that exploiting the time-series structure of standard predictor variables via deep learning can generate meaningful portfolio improvements for investors beyond those obtained from linear models.

q-fin.GN↗

arXiv2020

Dynamic Network Risk

This paper examines the pricing of short-term and long-term dynamic network risk in the cross-section of stock returns. Stocks with high sensitivities to dynamic network risk earn lower returns. We rationalize our finding with economic theory that allows the stochastic discount factor to load on network risk through the precautionary savings channel. A one-standard deviation increase in long-term (short-term) network risk loadings associate with a 7.66% (6.71%) drop in annualized expected returns.

q-fin.GN↗

arXiv2020

Investment Disputes and Abnormal Volatility of Stocks

Dramatic growth of investment disputes between foreign investors and host states rises serious questions about the impact of those disputes on investors. This paper is the first to explain increased uncertainty of investors about the outcome of arbitration, which may or may not lead to compensation for damages claimed by the investor. We find robust evidence that investment disputes lead to abnormal share fluctuations of companies involved in disputes with host countries. Importantly, while a positive outcome for an investor decreases uncertainty back to original levels, we document strong increase in the volatility of companies with negative outcome for the investor. We find that several variables including size of the award, political instability, location of arbitration, country of origin of investor or public policy considerations in host country explain large portion of the investor's uncertainty.

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