Purpose: Transaction monitoring system set up by financial institutions is one of the most used ways to track money laundering and terrorist financing activities. While being effective to a large extent, the system generates very high false positives. With evolving patterns of financial...

Purpose: Good quality input data is critical to developing a robust machine learning model for identifying possible money laundering transactions. McKinsey, during one of the conferences of ACAMS, attributed data quality as one of the reasons for struggling artificial intelligence use cases in...

We investigate the effect of discrete sampling and asset price jumps on fair variance and volatility swap strikes. Fair discrete volatility strikes and fair discrete variance strikes are derived in different models of the underlying evolution of the asset price: the Black-Scholes model, the...

We consider the problem of pricing options on a leveraged ETF (LETF) and the underlying ETF in a consistent manner. We show that if the underlying ETF has Heston dynamics then the LETF also has Heston dynamics so that options on both the ETF and the LETF can be priced analytically using standard...

We use the recently proposed duality approach to study the performance of static, myopic and generalized buy-and-hold (GBH) trading strategies. Our interest in static and GBH strategies is motivated by the fact that these strategies are intuitive and straightforward to implement in practice. The...

We propose a multi-period model to value employee options allowing for the possibility that a risk-averse employee strategically exercises her options over time rather than at a single date. Our results describing the representative employee's option exercise behavior are broadly consistent with...