Yufeng Han, Ting Hu, Jian Yang
Journal of Banking & Finance,Vol. 70, Pages: 214-234.
We provide evidence that a simple moving average timing strategy, when applied to portfolios of commodity futures, can generate superior performance to the buy-and-hold strategy. The outperformance is very robust. It can survive the transaction costs in the futures markets, it is not concentrated in a particular subperiod, and is robust to short-sale constraints, alternative specifications of the moving average lag length, alternative construction of the continuous time-series of futures prices, and impact from data mining. …
Yufeng Han, Ke Yang and Guofu Zhou
Journal of Financial and Quantitative Analysis, Volume 48 Issue 05, October 2013, pp 1433-1461.
In this paper, we document that an application of a moving average timing strategy of technical analysis to portfolios sorted by volatility generates investment timing portfolios that substantially outperform the buy-and-hold strategy. For high-volatility portfolios, the abnormal returns, relative to the capital asset pricing model (CAPM) and the Fama-French 3-factor models, are of great economic significance, and are greater than those from the well-known momentum strategy. Moreover, they cannot be explained by market timing ability, investor sentiment, default, and liquidity risks. Similar results also hold if the portfolios are sorted based on other proxies of information uncertainty.
Journal of Banking & Finance, Vol 36, Issue 9, Sept 2012, Pages 2575–2592
We propose a novel Bayesian framework to incorporate uncertainty about the state of the market. Among others, one advantage of the framework is the ability to model a large collection of time-varying parameters simultaneously. When we apply the framework to estimate the cost of equity we find economically significant effects of state uncertainty. A state-independent pricing model overestimates the cost of equity by about 4% per annum for a utility firm and by as much as 3% for industries. We also observe that the expected return, volatility, risk loading, and pricing error all display state-dependent dynamics that coincide with the business cycle. More interestingly, the forecasted market and Fama–French factor risk premiums can predict the future real GDP growth rate even though the model does not use any macroeconomic variables, which suggests that the proposed Bayesian framework captures the state-dependent dynamics well.
Xu, Tracy, Han, Yufeng and Jiang, Yang
Real Estate Economics, Vol. 40 Issue 3, September 2012, pp.
This paper examines how the U.S. monetary policy surprises impact the mortgage rates in the nation and across five regions from 1990 to 2008. Regression analysis based on bootstrapping shows that surprises in the target federal funds rate (the target factor) have a significantly positive impact on the 1-year adjustable-rate mortgage (ARM) rate within the week of the FOMC announcements and the positive impact lasts up to one week after the announcements. Surprises in the future direction of the Federal Reserve monetary policy (the path factor) have significantly positive impacts on both the 1-year ARM rate and the 30-year fixed mortgage rates in the first week after the announcement. Furthermore, the responses of mortgage rates are asymmetric and affected by the size of monetary policy surprises, the stage of the business cycle and whether the monetary policy is tightening or loosening. There also exists heterogeneity in the mortgage rate pass-through process across regions and monetary policy surprises have differential impacts on the regional mortgage rates. The cross-region variations are mainly correlated with the regional housing market conditions, such as home vacancy and rental vacancy rates.
Pisun Xu, Yufeng Han, Jian Yang
Real Estate Economics,Vol. 40, Issue 3, Pages: 461-507.
This article examines how the US monetary policy surprises impact the mortgage rates in the nation and across five regions from 1990 to 2008. Regression analysis based on bootstrapping shows that surprises in the target federal funds rate (the target factor) have a significantly positive impact on the 1-year adjustable-rate mortgage (ARM) rate within the week of the Federal Open Market Committee announcements and the positive impact lasts up to 1 week after the announcements. Surprises in the future direction of the Federal …
Yufeng Han and David Lesmond
Review of Financial Studies Vol. 24, Issue 5 Pp. 1590-1629
We model a microstructure effect on daily security returns, embodied by zero returns and the bid-ask spread, and derive a closed-form solution for the resulting bias in the estimated idiosyncratic volatility. Our empirical tests show that controlling for the bias eliminates the ability of idiosyncratic volatility estimates to predict future returns. We also find a significant reduction in the pricing ability of idiosyncratic volatility after exogenous shocks to liquidity evidenced in the 1997 reduction in the quotes to sixteenths and the 2001 decimalization. Finally, minimizing liquidity’s influence on the estimated idiosyncratic volatility, by orthogonalizing the percentage of zero-return and spread effects on the estimated idiosyncratic volatility, demonstrates that the resulting idiosyncratic volatility estimate has little pricing ability.
ANNALS OF ECONOMICS AND FINANCE Vol. 11 Issue 1, Pages: 1–33
Recent studies provide strong statistical evidence challenging the existence of out-of-sample return predictability. The economic significance of return
predictability is also controversial. In this paper, we find significant economic gains for dynamic trading strategies based on return predictability when ap-
propriate portfolio constraints are imposed. We findthat imposingappropriate portfolio constraints is critical for obtaining economic profits, which seems to
explain the contradictory findings about economic significance in the literature. We also compare the performance of several predictive models including
the VAR, the VAR-GARCH, and the (semi)nonparametric models and find that the simple VAR model performs similarly to other more complex models.