Kin Lam

Recently, there has been a considerable interest in the Bayesian approach for explaining investors' behaviorial biases by incorporating conservative and representative heuristics when making financial decisions, (see, for example, Barberis, Shleifer and Vishny (1998)). To establish a quantitative link between some important market anomalies and investors' behaviorial biases, Lam, Liu, and Wong (2010) introduced a pseudo-Bayesian approach for developing properties of stock returns, where weights induced by investors' conservative and representative heuristics are assigned to observations of the earning shocks and stock prices. In response to the recent global financial crisis, we introduce a new pseudo-Bayesian model to incorporate the impact of a financial crisis. Properties of stock returns during the financial crisis and recovery from the crisis are established. The proposed model can be applied to investigate some important market anomalies including short-term underreaction, long-term overreaction, and excess volatility during financial crisis. We also explain in some detail the linkage between these market anomalies and investors' behavioral biases during financial crisis.

In a communication network, one might attempt to route calls from an origin to a destination through n paths that will be tried one by one, each having a success probability p_i ∈ (0,1), i = 1,2,…,n. The order of trying is controlled by a routing table. The number of attempts made is defined as the cost of the routing table. Move-forward self-organizing rules are applied to the routing table and comparisons of expected equilibrium costs are performed when p₂ = p₃ = … = p_n. Stationary distributions for a subset of f-swap rules are obtained for general p_i's.

The margin system is the first line of defense against the default risk of a clearinghouse. From the perspectives of a clearinghouse, the utmost concern is to have a prudential system to control the default exposure. Once the level of prudentiality is set, the next concern will be the opportunity cost of the investors, because high opportunity cost discourages people from hedging futures, and thus defeats the function of a futures market. In this article, we first develop different measures of prudentiality and opportunity cost. We then formulate a statistical framework to evaluate different margin‐setting methodologies, all of which strike a balance between prudentiality and opportunity cost. Three margin‐setting methodologies, namely, (1) using simple moving averages; (2) using exponentially weighted moving averages; (3) using a GARCH approach, are applied to the Hang Seng Index futures. Keeping the same prudentiality level, it is shown that the one using a GARCH approach by and large gives the lowest average overcharge. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:117–145, 2004

This article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor's 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and the medium-volatility states. The high-volatility regime is able to capture the 1987 crash and overlap considerably with four U.S. economic recession periods.