We implement momentum strategies using reward-risk measures as ranking criteria based on classical tempered stable distribution. Performances and risk characteristics for the alternative portfolios are obtained in various asset classes and markets. The reward-risk momentum strategies with lower volatility levels outperform the traditional momentum strategy regardless of asset class and market. Additionally, the alternative portfolios are not only less riskier in risk measures such as VaR, CVaR and maximum drawdown but also characterized by thinner downside tails. Similar patterns in performance and risk profile are also found at the level of each ranking basket in the reward-risk portfolios. Higher factor-neutral returns achieved by the reward-risk momentum strategies are statistically significant and large portions of the performances are not explained by the Carhart four-factor model.

Statistical arbitrage strategies are typically based on models of returns. We introduce a new statistical arbitrage strategy based on dynamic factor models of prices. Our objective in this paper is to exploit the mean-reverting properties of prices reported in the literature. We do so because, to capture the same information using a return-based factor model, a much larger number of lags would be needed, leading to inaccurate parameter estimation. To empirically test the relative performance of return-based and price-based models, we construct portfolios (long-short, long-only, and equally weighted) based on the forecasts generated by two dynamic factor models. Using the stock of companies included in the S&P 500 index for constructing portfolios, the empirical analysis statistically tests the relative forecasting performance using the Diebold–Mariano framework and performing the test for statistical arbitrage proposed by Hogan et al. (2004). Our results show that prices allow for significantly more accurate forecasts than returns and pass the test for statistical arbitrage. We attribute this finding to the mean-reverting properties of stock prices. The high level of forecasting accuracy using price-based factor models has important theoretical and practical implications.

There appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling. More specifically, it is alleged that current risk models have failed to properly assess the risks associated with large adverse stock price behavior. In this paper, we first discuss the limitations of classical time series models for forecasting financial market meltdowns. Then we set forth a framework capable of forecasting both extreme events and highly volatile markets. Based on the empirical evidence presented in this paper, our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods.