arXiv subjects

Luca Vincenzo Ballestra

Publications and source records attributed to Luca Vincenzo Ballestra.

5 recordsLinked to original sources

arXiv2026

Do Short Exposure and Systematic Risk Exposure Drive Asymmetries in the Disposition Effect?

This study examines the disposition effect in both long and short exposure positions in FTSE MIB tracking ETFs using a unique dataset of almost 9 million individual transactions. Building on the integrated framing approach, we extend the analysis to explicitly incorporate leverage and long short exposures, allowing us to assess how portfolio context and systematic risk exposure jointly are associated to investors realization behavior. Methodologically, we generalize Odean canonical Count and Total measures to wide and integrated framing, introduce a novel Value metric that captures the return thresholds required to realize gains versus losses, and implement these measures in dispositionEffect, an open-source R package for large-scale intraday data. We show that short positions exhibit a weaker disposition effect than long positions under narrow framing, but that this asymmetry reverses in positively performing portfolios under integrated framing. Systematic risk further amplifies these behavioral asymmetries across positions. Overall, our findings demonstrate that the disposition effect is not solely asset-specific, but is critically shaped by the interaction between portfolio context, position type, and systematic risk exposure. More broadly, the results are consistent with the joint predictions of Prospect Theory and Regret Theory, highlighting the central role of framing in investor decision-making.

q-fin.RM↗

arXiv2025

A multi-factor model for improved commodity pricing: Calibration and an application to the oil market

We present a new model for commodity pricing that enhances accuracy by integrating four distinct risk factors: spot price, stochastic volatility, convenience yield, and stochastic interest rates. While the influence of these four variables on commodity futures prices is well recognized, their combined effect has not been addressed in the existing literature. We fill this gap by proposing a model that effectively captures key stylized facts including a dynamic correlation structure and time-varying risk premiums. Using a Kalman filter-based framework, we achieve simultaneous estimation of parameters while filtering state variables through the joint term structure of futures prices and bond yields. We perform an empirical analysis focusing on crude oil futures, where we benchmark our model against established approaches. The results demonstrate that the proposed four-factor model effectively captures the complexities of futures term structures and outperforms existing models.

q-fin.ST↗

arXiv2024

GARCH option valuation with long-run and short-run volatility components: A novel framework ensuring positive variance

Christoffersen, Jacobs, Ornthanalai, and Wang (2008) (CJOW) proposed an improved Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for valuing European options, where the return volatility is comprised of two distinct components. Empirical studies indicate that the model developed by CJOW outperforms widely-used single-component GARCH models and provides a superior fit to options data than models that combine conditional heteroskedasticity with Poisson-normal jumps. However, a significant limitation of this model is that it allows the variance process to become negative. Oh and Park [2023] partially addressed this issue by developing a related model, yet the positivity of the volatility components is not guaranteed, both theoretically and empirically. In this paper we introduce a new GARCH model that improves upon the models by CJOW and Oh and Park [2023], ensuring the positivity of the return volatility. In comparison to the two earlier GARCH approaches, our novel methodology shows comparable in-sample performance on returns data and superior performance on S&P500 options data.

econ.EM↗

arXiv2024

A GARCH model with two volatility components and two driving factors

We introduce a novel GARCH model that integrates two sources of uncertainty to better capture the rich, multi-component dynamics often observed in the volatility of financial assets. This model provides a quasi closed-form representation of the characteristic function for future log-returns, from which semi-analytical formulas for option pricing can be derived. A theoretical analysis is conducted to establish sufficient conditions for strict stationarity and geometric ergodicity, while also obtaining the continuous-time diffusion limit of the model. Empirical evaluations, conducted both in-sample and out-of-sample using S\&P500 time series data, show that our model outperforms widely used single-factor models in predicting returns and option prices.

econ.EM↗

arXiv2021

Actuarial strategy for pricing Asian options under a mixed fractional Brownian motion with jumps

The mixed fractional Brownian motion ($mfBm$) has become quite popular in finance, since it allows one to model long-range dependence and self-similarity while remaining, for certain values of the Hurst parameter, arbitrage-free. In the present paper, we propose approximate closed-form solutions for pricing arithmetic Asian options on an underlying described by the $mfBm$. Specifically, we consider both arithmetic Asian options and arithmetic Asian power options, and we obtain analytical formulas for pricing them based on a convenient approximation of the strike price. Both the standard $mfBm$ and the $mfBm$ with Poisson log-normally distributed jumps are taken into account.

q-fin.PR↗