Conference Agenda

We forecast future 1-day realized volatility (RV) of asset returns in the Chinese stock market by exploring the importance of main volatility features documented in the literature. Beginning with the benchmark Heterogeneous Autoregressive (HAR) model, we further examine major volatility features, including the leverage effect, measurement error, volatility spillover, jumps, and the volatility decomposition between negative and positive returns. Our contribution lies in balancing model specification and estimation to capture essential volatility features and enhance the performance of both in-sample and out-of-sample tests. Ultimately, we quantify the importance of the leverage effect, showcasing its predominance over other volatility features in out-of-sample 1-day volatility forecasting through various estimation methods.

This paper examines the dynamic and directional connectedness among currency option-implied volatilities and how it is shaped by geopolitical risk. Using a novel dataset of over the counter (OTC) option-implied volatilities for 20 developed and emerging market currencies between July 2004 and January 2023, we construct forward-looking currency volatility networks that reflect investor expectations of uncertainty over a 30-day horizon.

To analyse the evolving interdependence in global currency markets, we estimate a Time-Varying Parameter Vector Autoregression (TVP-VAR) model using the Quasi-Bayesian Local Likelihood (QBLL) approach. This methodology enables the construction of dynamic, directed, and horizon-specific adjacency matrices, capturing both aggregate and net directional connectedness. We also apply rolling window variance decompositions to monitor real-time transmission patterns of volatility shocks across currencies.

Our analysis incorporates multiple versions of the Geopolitical Risk (GPR) index, including overall risk (GPR), threats (GPRT), acts (GPRA), and country-specific measures (GPRC), as exogenous drivers in both time-series and panel regressions. The empirical results demonstrate that increases in geopolitical risk, particularly GPR and GPRA, lead to significant rises in total financial connectedness, with effects more pronounced at the daily frequency. Among control variables, the VIX index consistently exhibits a positive and significant relationship with connectedness, while exchange rates display a negative and stabilizing effect. Stock indices and crude oil prices have limited explanatory power in most specifications.

Panel regression analysis confirms that country-specific geopolitical risk (GPRC) has a positive and statistically significant effect on Aggregate Directional Connectedness (AGM) and TO connectedness, indicating that geopolitical shocks amplify both overall and outward spillovers in financial markets. However, the impact of GPRC on FROM connectedness is statistically insignificant.

Overall, this study emphasizes that geopolitical risk is a significant and asymmetric driver of systemic connectedness in currency volatility networks. The findings offer practical insights for policymakers, investors, and regulators by highlighting the role of GPR indices and macroeconomic conditions as early-warning indicators for financial contagion and interconnectedness dynamics.

We develop a signalling game model of investment to examine the implications of ambiguity aversion on corporate equilibrium strategies, investment dynamics, and financing decisions within incomplete markets marked by asymmetric information. Our analysis reveals that volatility ambiguity aversion exerts a comparable yet more pronounced influence than asymmetric information, leading to heightened financing costs, decreased investment probabilities and prompting corporates to adopt non-participation investment decisions. Notably, volatility ambiguity aversion exhibits an amplifier effect, magnifying financing costs, adverse selection costs, and distortion in investment choices under asymmetric information. This heightened ambiguity aversion escalates the likelihood of inefficient separating and pooling equilibria, ultimately resulting in a discernible welfare loss. The findings underscore the substantial impact of ambiguity aversion on strategic decision-making and equilibrium outcomes in the context of investment within environments characterized by information asymmetry and incomplete markets.

The logarithmic prices of financial assets are conventionally assumed to follow a drift-diffusion

process. While the drift term is typically ignored in infill asymptotic theory and its applications, the presence of nonzero drift is an undeniable reality. Our finite-sample theory, along

with extensive simulations, reveals the non-negligible impact of drift on the estimation precision of good and bad volatility. We also demonstrate that this poor estimation of good and

bad volatility leads to significant bias in signed jump estimation. As a solution, we propose

an alternative construction of good volatility, bad volatility, and signed jumps, which shows

a marked improvement in estimation accuracy in the presence of non-negligible drift. When

applying the modified estimators to forecast stock market volatility, we do not find evidence of

an asymmetric impact of good and bad volatility, nor a significant role for signed jumps. We

show that the asymmetric effects of good and bad volatility, as well as the role of signed jumps

reported in existing literature, may be largely attributed to biases in their estimators caused by

nonzero drift.

How is volatility transmitted between options and futures contracts, and can this information transmission be used to generate profitable trading strategies? We examine the bidirectional relationship in volatility between commodity options and futures markets for key commodities to learn about how each market influences the other. To this end, we estimate volatility forecasting models using random forests and we calculate connectedness and spillover measures. We find that futures volatility has a strong but short-lived impact on option volatility, while option volatility has a longer lasting effect on futures volatility, confirming a bidirectional volatility transmission. We further document important net spillovers from options to futures. Moreover, predictive analysis shows that option markets generally lead futures markets in terms of providing information that is relevant for trading strategies. We obtain more accurate futures volatility predictions and trading strategies generate superior economic gains.