In this paper, we examin the issues raised in preparing the returns data  for strategies with futures/options  (e.g. Contract specifications, margin requirement, contract size, leverage, mark-to-market (MTM) etc.). Finally, we define returns on margin and compute strategy's P&LProfit & Loss and max P&L draw throughout the holding period.

This paper discusses the back-testing process of the strategy; we demonstartes the steps, issues and assumptions made during the preparation of the data sample and the calculation of the hypothesized startegy's returns. Finally, we turn our attention to risk aspect of the strategy and application within an established trading environment with common risk management policies.

In the paper, we consider the daily log returns for the S&P 500, examine its statistical properties, construct an EGARCHⁱ model, examine several innovationsshocks or noise distribution models (e.g. gaussian, Student's t, GEDⁱ), and verify whether the models assumptions are satisfied. Finally, we forecast daily volatility for out-of-sample.

This paper analyze the realized returns of an active trading startegy in an attempt to uncover any time patterns in the time series, i.e. do returns have any predictive information.

In this paper, we construct a hypothetical strategy that buys at daily LOLowest price for the strategy/holding period and exists at daily HIHighest price for the same strategy/holding period (i.e. optimal strategy), compute and analyze its returns and construct an econometric model to capture time dynamics. Next, using the model, we forecast the strategy returns and examine a relationship between the forecasted returns and actual volatility for the same holding period. Although, the strategy can never be realized, it can help us forecast volatility using a new source of data (i.e. HI-LO).