Essays in Finance
Date Issued
2015
Date
2015
Author(s)
Chang, Bi-Juan
Abstract
This dissertation is mainly focus on two empirical finance studies: the rollover effects in stock index futures contracts, and searching for the distressed firms in equity markets. For research of the rollover effects in stock index futures contracts, this dissertation proposes a new method for evaluating stock index futures contracts rolling returns with the rollover effect analysis. Due to the limited lifespan of futures contract, traditional return series construction may be distorted by the price jump and contract inconsistency problems. We amend it by replacing the rollover price of the nearest-to-maturity to the next-to-maturity price at the rollover day, and decomposing the total return to the rollover effect and the capital gain or loss. Three rolling points, the delivery day, the seventh day, and the first day of the expiration month, are considered. Differences between the actual and theoretical futures prices are also discussed. Convenience yields for the near close and next-to-maturity futures are also explored. We investigate S&P500, DAX, and TAIEX stock index futures, and find that with the risk-return and rollover effect analysis, rolling into the next contract on the delivery day is often a better choice. By the convergent paths of the convenience yields we observe that expiration day effects are very apparent in all three markets. The second topic is on the distressed firms research and prediction. Distressed firms in equity markets are like landmines in the battlefields due to their undetectability and devastating effects. This dissertation is concerned with distressed firms forecasting by the distance to default and rare event logit models via public available data. Comparing these two models by Cumulative Accuracy Profiles and Receiver Operating Characteristic curves, we conclude that the rare event logit model performs better than the distance to default model. The data contains U.S.-listed firms on the S&P 500 for the period January 1986 to December 2012, including 2,138 companies and 271,912 firm months, with 444 distressed firms. We set the dynamic thresholds as the last 6% of firms based on the historical cross-section distress rates. Upon Bayesian posterior probability examination, the rare event logit model shows about 40% to 60% affinity with S&P Domestic Long Term Issuer Credit Rating records on average, and the rate increases to 70% in some situations. We conclude that the rare event logit model can be a good warning indicator of distress in firms at least three years ahead.
Subjects
stock index futures (SIF)
rollover effect
expiration day effect
distance to default model (DTD)
rare event logit model (REL)
Type
thesis
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