Risk aversion, information asymmetry and the correlation between different asset classes are the focus of this issue of The Journal of Risk.
The classic Markowitz mean–variance optimization setup has spawned rich academic and practical developments. While it maintains its role as a fundamental theoretical benchmark, its implementation remains challenging, especially in the face of nonstationary asset returns. In “Mostly prior-free asset allocation”, Sylvain Chassang addresses the conundrum faced by modelers who wish to account for past patterns of returns while recognizing that standard statistical approaches are inadequate for the Markowitz framework. The author proposes an alternative approach that instead focuses on the trade-off between the fear of loss and the fear of missing out, without any sophisticated beliefs about patterns of returns. He shows how this framework avoids earlier pitfalls of extreme allocation strategies and offers robust recommendations with very appealing worst-case drawdown guarantees.
Commodities have become a significant asset class for a large number of investors. They present unique features such as seasonality, storage and exposure to currency fluctuations. In “Dependence dynamics among exchange rates, commodities and the Brazilian stock market using the R-vine SCAR model”, Daniel Henrique Salgado and Osvaldo Candido illustrate the application of an R-vine copula in combination with a stochastic autoregressive copula (SCAR) to extract the dependence structure between commodities, equities and currencies. An advantage of this R-vine SCAR approach is that no a priori restrictions are imposed, whether on the dependence structure or on the multivariate distribution. Through their empirical study, the authors confirm the weighty influence of the US dollar on the Brazilian economy, as the latter is heavily dependent on major commodities.
Uninformed traders seek ways to counter the advantage that informed traders have over them. In extreme situations, their selling reaction results in so-called order flow toxicity, which leads to (flash) crashes. Rand Kwong Yew Low, Te Li and Terry Marsh set out an implementation of a particular measure (BV–VPIN) of this flow toxicity in “BV–VPIN: measuring the impact of order flow toxicity and liquidity on international equity markets”. The authors provide evidence of its efficacy in determining the likelihood of impending sharp market movements and in guiding hedging strategies based on futures contracts.
Real estate is another market in which asymmetric information is prevalent. In “Asymmetry herding behavior of real estate investment trusts: evidence from information demand", Wen-Yuan Lin, Ming-Hung Wu and Ming-Chi Chen show how uninformed investors disregard any limited information they may have and succumb to herding behavior in light of Google's search volume index, In particular, the authors provide evidence of herding behavior that is spurious in rising markets but more pronounced when volatility is extreme or after a recession
Warrington College of Business, University of Florida
This paper develops a prior-free version of Harry Markowitz’s efficient portfolio theory, which allows the decision maker to express their preferences with regard to risk and reward, even though they are unable to express a prior over potentially…
Dependence dynamics among exchange rates, commodities and the Brazilian stock market using the R-vine SCAR model
The objective of this paper is to assess the dependence dynamics among Brazilian real exchange rates, commodity prices and the Brazilian stock market using a regular vine copula combined with the stochastic autoregressive copula model.
The authors analyze the impact of different values of the VBS and sample size applied as inputs in a BV–VPIN model based on the US market in order to ascertain the optimal criteria for application across all other countries in our data set.
This paper investigates the effect of investor demand on herding behavior in the US real estate investment trusts (REITs) market by measuring investors’ information demand using Google’s search volume index.