Maximizing an equity portfolio excess growth rate: a new form of smart beta strategy?

It has been claimed that, for dynamic investment strategies, the simple act of rebalancing a portfolio can be a source of additional performance, sometimes referred to as the volatility pumping effect or the diversification bonus because volatility and diversification turn out to be key drivers of the portfolio performance. Stochastic portfolio theory suggests that the portfolio excess growth rate, defined as the difference between the portfolio expected growth rate and the weighted-average expected growth rate of the assets in the portfolio, is an important component of this additional performance (see Fernholz [Stochastic Portfolio Theory, 2002 (Springer)]). In this context, one might wonder whether maximizing a portfolio excess growth rate would lead to an improvement in the portfolio performance or risk-adjusted performance. This paper provides a thorough empirical analysis of the maximization of an equity portfolio excess growth rate in a portfolio construction context for individual stocks. In out-of-sample empirical tests conducted on individual stocks from 4 different regions (US, UK, Eurozone and Japan), we find that portfolios that maximize the excess growth rate are characterized by a strong negative exposure to the low volatility factor and a higher than 1 exposure to the market factor, implying that such portfolios are attractive alternatives to competing smart portfolios in markets where the low volatility anomaly does not hold (e.g. in the UK, or in rising interest rate scenarios) or in bull market environments.     

Density estimation through quasi-analytic Monte-Carlo simulation: Options arbitrage with transactions costs

Discretely rebalanced options arbitrage strategies in the presence of transaction costs have path dependent returns that are difficult to model analytically. I instead use a quasi-analytic procedure that combines the computational efficiency of analytical solutions with the flexibility of simulations. The central feature is the estimation of the distribution of returns of the arbitrage strategy by mapping simulated returns percentiles and the input parameter set. Using the estimated density, I evaluate the tradeoff between transaction costs and risk exposure under generalized transaction costs structures that includes bid-ask spread and brokerage commission. I show that the optimal strategy depends on transaction costs, volatility, and option moneyness. Strategies such as rebalancing when the hedge ratio changes by 0.25, balances transaction costs and risk exposure, and can be optimal.

Mean–variance portfolio selection with ‘at-risk’ constraints and discrete distributions

We examine the impact of adding either a VaR or a CVaR constraint to the mean–variance model when security returns are assumed to have a discrete distribution with finitely many jump points. Three main results are obtained. First, portfolios on the VaR-constrained boundary exhibit (K + 2)-fund separation, where K is the number of states for which the portfolios suffer losses equal to the VaR bound. Second, portfolios on the CVaR-constrained boundary exhibit (K + 3)-fund separation, where K is the number of states for which the portfolios suffer losses equal to their VaRs. Third, an example illustrates that while the VaR of the CVaR-constrained optimal portfolio is close to that of the VaR-constrained optimal portfolio, the CVaR of the former is notably smaller than that of the latter. This result suggests that a CVaR constraint is more effective than a VaR constraint to curtail large losses in the mean–variance model.     

Diversified Portfolios with Jumps in a Benchmark Framework

This paper considers diversified portfolios in a sequence of jump diffusion market models. Conditions for the approximation of the growth optimal portfolio (GOP) by diversified portfolios are provided. Under realistic assumptions, it is shown that diversified portfolios approximate the GOP without requiring any major model specifications. This provides a basis for systematic use of diversified stock indices as proxies for the GOP in derivative pricing, risk management and portfolio optimization. 1991 Mathematics Subject Classification: primary 90A12; secondary 60G30; 62P20 JEL Classification: G10, G13     

Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?

This paper investigates the portfolio diversification potential of a pool of cryptocurrencies classified based on their degree of leadership. We employ the mean-variance and the higher-order moments optimization approaches to evaluate the diversification potential of centralized and decentralized cryptocurrencies across multiple frameworks. While theoretical implications of the mean-variance and the higher-order moments optimization approaches are similar, our results suggest that the latter provides a more precise portfolio allocation strategy because it considers investor risk-aversion for each moment. Furthermore, we find that extending the pool of cryptocurrencies achieves marginal diversification benefits due to considerable co-movements among the cryptocurrencies. Moreover, we find that decentralized cryptocurrencies offer greater diversification potential than centralized cryptocurrencies, although centralized cryptocurrencies carry some diversification potential during alt-seasons. In order of their weights, Bitcoin, Chainlink, and Ethereum (all decentralized) offer the highest contribution to portfolio diversification across most portfolio frameworks, while Ethereum offers greater diversification benefits during the alt-seasons.