Risks

The One-X property, introduced by Zetocha in a 2023 paper, provides a novel stochastic order with direct implications for constructing arbitrage-free implied volatility surfaces. The current work revisits its theoretical foundations and explores its connections with classical stochastic orders, thereby offering a deeper understanding of its mathematical structure and practical significance in calendar-arbitrage-free modeling. We first present an explicit counterexample to a conjecture raised in Zetocha’s previous paper, and then provide a natural and valid enhancement of this conjecture. After discussing the inherent relations between the One-X property and properties such as $T{P}_2$, $R{P}_2$, and unimodality of density ratio (introcuded by Glasserman and Pirjol in their 2024 papers), we further explore some sufficient conditions to achieve the One-X property for random variables of certain mixture types that are frequently seen in applications.

We show that financial integration in emerging Asia is state-dependent in the sense that cross-market linkages vary systematically across regimes of global uncertainty and market stress. Focusing on Indonesia, Malaysia, Singapore, Thailand, and Vietnam, this study combines a time-varying parameter VAR (TVP–VAR) with a GARCH–MIDAS volatility model to link short-run transmission to long-run behavioural effects. We construct a regional investor-sentiment (IS) index from Google search data on five macro-financial topics using principal component analysis and analyse it together with global benchmarks (MSCI EM, S&P 500), gold, clean-energy equities, and macro-uncertainty indicators. The TVP–VAR maps dynamic spillovers among the ASEAN-5 and external nodes, while the GARCH–MIDAS relates the slow component of variance to investor attention. The evidence indicates that connectedness tightens in stress regimes, with global benchmarks and policy uncertainty acting as transmitters and ASEAN equities absorbing incoming shocks. In the volatility block, the Google-based IS factor exerts a negative and economically meaningful influence on the long-run component over and above global uncertainty, supporting the view that attention and uncertainty function as complementary channels of risk propagation. The integrated framework is parsimonious and replicable, and it offers actionable insights for regime-aware risk management, policy communication, and the timing of green-finance issuance in emerging markets.

The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and convert illegally obtained assets into untraceable commodities, seamlessly integrated into the financial system. Although new regulatory measures have been introduced, illicit actors continue to exploit various methods, from peer-to-peer exchanges to cryptocurrency mixing services, to obscure the origins of illegal funds. This study presents a parametric analysis of these methods, examining dimensions such as duration, number of actors, contextual requirements, operational difficulty, traceability, and costs across each stage of the money laundering process: placement, layering, and integration. The analysis indicates that, while more sophisticated techniques may provide a higher degree of anonymity, they simultaneously require specialised technical expertise and meticulous planning. Consequently, there is a trade-off between the level of privacy attainable and the operational complexity inherent to each method. By systematically comparing these strategies, this analysis aims to contribute to a deeper understanding of cryptocurrency-based money laundering techniques, providing insight for more effective prevention and mitigation measures for both regulatory authorities and the financial sector.