多重优势锻造超短期融资券业务

2010年末,银行间债券市场推出了超短期融资券这一新的短期货币市场工具。在制度和流程方面,超短期融资券业务较先前的短期融资券业务更为简化。在我国提高直接融资比重、加快转变经济发展方式的今天,超短期融资券业务有利于改变我国债券市场超短期产品长期缺失的状况,完善债券市场信用产品线,进一步丰富市场的利率期限结构、风险结构及流动性结构,有利于形成短、中、长期兼备的市场化收益率曲线,成为我国利率市场化改革的重要组成部分。     

TREASURY BILL FUTURES AS A HEDGING TOOL: A RISK-RETURN APPROACH

The primary purpose of this study is to measure the hedging performance of Treasury Bill Futures on a risk-return basis. A theoretical model is presented and hedging effectiveness is tested using T-Bill cash and futures data. Successful hedging depends critically upon the ability to determine the optimal hedge ratio. The results also indicate that the traditional one-to-one hedge outperforms the more sophisticated hedge ratio models; however, even here the risk-return benefits of hedging are minimal.     

RISK-RETURN SEASONALITY AND MACROECONOMIC VARIABLES

In this paper I examine seasonality in the pricing of the Chen, Roll, and Ross (1986) macroeconomic variables. January seasonality is observed in the risk premia from 1932 to 1990. Examination of the 1932–57 period indicates nonstationary seasonals in the risk premia. The results cannot be attributed to the previously documented firm size effect. Residual risk is priced, and a strong January seasonality is observed in the residual risk premia. The market portfolio is not priced in the presence of the other variables. Trading volume is not priced in the individual months, but appears significant when all months are considered together.     

A NEW APPROACH TO CONTROLLING FOR THIN TRADING

Recent research has shown that thin trading can seriously bias beta estimates. Present techniques for controlling this bias in research designs involve the adjustment of OLS betas. This paper presents a new methodology that controls for this bias by forming portfolios where the level of thin trading is held constant, while the difference of another variable, pertinent to a specific research design, is maximized across the portfolios. Directly controlling the level of thin trading avoids reliance on beta adjustment techniques. Further, the linear programming model permits the control of the mean and higher moments of additional variables across portfolios.