International Momentum Strategies

This paper presents a theoretically sound portfolio performance measure that takes into account higher moments of distribution. This measure is motivated by a study of the investor’s preferences to higher moments of distribution within Expected Utility Theory and an approximation analysis of the optimal capital allocation problem. We show that this performance measure justifies the notion of the Generalized Sharpe Ratio (GSR) introduced by Hodges (1998) . We present two methods of practical estimation of the GSR: nonparametric and parametric. For the implementation of the parametric method we derive a closed-form solution for the GSR where the higher moments are calibrated to the normal inverse Gaussian distribution. We illustrate how the GSR can mitigate the shortcomings of the Sharpe ratio in resolution of Sharpe ratio paradoxes and reveal the real performance of portfolios with manipulated Sharpe ratios. We also demonstrate the use of this measure in the performance evaluation of hedge funds.

Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance

Momentum in individual stock returns relates to momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of six basis points following a year of losses and 51 basis points following a positive year. We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum-neutral factors display more momentum. Momentum found in high-eigenvalue principal component factors subsumes most forms of individual stock momentum. Our results suggest that momentum is not a distinct risk factor—it times other factors. 

Factor Momentum and the Momentum Factor

In this paper, we introduce a new GARCH model with an infinitely divisible distributed innovation. This model, which we refer to as the rapidly decreasing tempered stable (RDTS) GARCH model, takes into account empirical facts that have been observed for stock and index returns, such as volatility clustering, non-zero skewness, and excess kurtosis for the residual distribution. We review the classical tempered stable (CTS) GARCH model, which has similar statistical properties. By considering a proper density transformation between infinitely divisible random variables, we can find the risk-neutral price process, thereby allowing application to option-pricing. We propose algorithms to generate scenarios based on GARCH models with CTS and RDTS innovations. To investigate the performance of these GARCH models, we report parameter estimates for the Dow Jones Industrial Average index and stocks included in this index. To demonstrate the advantages of the proposed model, we calculate option prices based on the index.

https://econpapers.wiwi.kit.edu/downloads/KITe_WP_28.pdf

Tempered stable and tempered infinitely divisible GARCH models

How should individual investors diversify? An empirical evaluation of alternative asset allocation policies

In this paper, we analyze momentum strategies that are based on reward–risk stock selection criteria in contrast to ordinary momentum strategies based on a cumulative return criterion. Reward–risk stock selection criteria include the standard Sharpe ratio with variance as a risk measure, and alternative reward–risk ratios with the expected shortfall as a risk measure. We investigate momentum strategies using 517 stocks in the S&P 500 universe in the period 1996–2003. Although the cumulative return criterion provides the highest average monthly momentum profits of 1.3% compared to the monthly profit of 0.86% for the best alternative criterion, the alternative ratios provide better risk-adjusted returns measured on an independent risk-adjusted performance measure. We also provide evidence on unique distributional properties of extreme momentum portfolios analyzed within the framework of general non-normal stable Paretian distributions. Specifically, for every stock selection criterion, loser portfolios have the lowest tail index and tail index of winner portfolios is lower than that of middle deciles. The lower tail index is associated with a lower mean strategy. The lowest tail index is obtained for the cumulative return strategy. Given our data-set, these findings indicate that the cumulative return strategy obtains higher profits with the acceptance of higher tail risk, while strategies based on reward–risk criteria obtain better risk-adjusted performance with the acceptance of the lower tail risk.

/pdf/momentum-strategies-based-on-reward-risk-stock-selection-2hr6acvp07.pdf

Momentum strategies based on reward-risk stock selection criteria

Time series in traded markets such as currencies and securities involve supply/demand interaction, so they might be expected to contain distinctive and identifiable structures in comparison with data based on natural phenomena such as river flows or sunspots. This paper tests this proposition using standard econometric tests including variance ratios, modified rescaled range (R/S) ratios and BDS statistics together with non-linear prediction models. Four time series of each type (market or natural) are subject to a battery of tests for random walk and non-linear dependence. Surprisingly, the tests provide no reliable discrimination between the two types of series or reveal any embedded specification differences.

Testing for efficiency and non-linearity in market and natural time series

In this paper, we introduce performance risk/return Ratios as new criteria for the construction of the winner and loser portfolios in momentum strategies. Our results show that momentum strategies based on novel risk/return Ratio criteria applied to previous 6-month or 12-month periods, generate positive returns over 6-month and 12-month holding periods and are more profitable than strategies based on usual cumulative or total return benchmarks. We compare strategies driven by different performance Ratios and cumulative return benchmark using independent performance measures based on a coherent risk measure (expected shortfall risk) and cumulative return over the holding period. Specifically, the R-Ratio emerges in our study as the best candidate for momentum portfolio construction. Finally, we model momentum profits in a GARCH-stable setting and validate our assumptions on the winner and loser return patterns.

Profitability of Momentum Strategies: Application of Novel Risk/Return Ratio Stock Selection Criteria

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Momentum strategies based on reward-risk stock selection criteria

Testing for efficiency and non-linearity in market and natural time series

Profitability of Momentum Strategies: Application of Novel Risk/Return Ratio Stock Selection Criteria