We develop statistical methods to detect informed trading in options markets. We apply these methods to 31 companies from various sectors over 14 years analyzing approximately 9.6 million option prices. We find that option informed trading tends to cluster prior to certain events, takes place...

This appendix extends the empirical results in Chesney, Crameri, and Mancini (2011). Informed trading activities on put and call options are analyzed for 19 companies in the banking and insurance sectors from January 1996 to September 2009. Our empirical findings suggest that certain events such...

We clarify that the widely used estimation method for the LOT liquidity model in the market microstructure literature is improper in the sense of econometric inference. Based on an extensive simulation study and a real data analysis, we show that this method not only overestimates the true...

This paper identifies a clear tradeoff between tracking error — performance differences relative to a targeted asset allocation — and turnover—a proxy for rebalancing costs — that can help guide investors’ rebalancing choices. We find that calendar-based approaches, while convenient,...

This paper examines how volatility positions can be optimally constructed by modeling the selection process as a linear discrete ill-posed problem with box constraints. We show how this framework allows for a priori investor expectations and risk parameters to be applied in the optimization...

We consider the problem faced by an investor who must liquidate a given basket of assets over a finite time horizon. The investor's goal is to maximize the expected utility of the sales revenues over a class of adaptive strategies. We assume that the investor's utility has constant absolute risk...

Using data from the S&P 500 stocks from 1990 to 2015, we address the uncertainty of distribution of assets' returns in Conditional Value-at-Risk (CVaR) minimization model by applying multidimensional mixed Archimedean copula function and obtaining its robust counterpart. We implement a dynamic...

When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a...

In this article, the authors propose an order flow simulator for meta orders such as those originating from the trading activity of buy-side firms. The simulator is designed with three key goals in mind. First, it should be simple to use and integrate into different applications. Second, it must...