Dissertation•
An empirical investigation of technical analysis in fixed income markets
01 Jan 2006-
TL;DR: In this paper, the effectiveness of technical analytic indicators in the fixed income markets is evaluated, and the results provide mixed conclusions on the profitability of technical trading systems in the bond markets, since the results vary across different futures markets and adjusting for data snooping effects and transaction costs.
Abstract: The aim of this thesis is to evaluate the effectiveness of technical analytic indicators in the fixed income markets. Technical analysis is a widely used methodology by investors in the equity and foreign exchange markets, but the empirical evidence on the profltability of technical trading systems in the bond markets is sparse. Therefore, this thesis serves as a coherent and systematic examination of technical trading systems in the government bond futures and bond yield markets. We investigate three aspects of technical analysis. First, we evaluate the profitability of 7,991 technical trading systems in eight bond futures contracts. Our results provide mixed conclusions on the profitability these technical systems, since the results vary across different futures markets, even adjusting for data snooping effects and transaction costs. In addition, we find the profitability of the trading systems has declined in recent periods. Second, we examine the informativeness of technical chart patterns in the government benchmark bond yield and yield spread markets. We apply the nonparametric regression methodology, including the Nadaraya-Watson and local polynomial regression, to identify twelve chart patterns commonly taught by chartists. The empirical results show no incremental information are contained within these chart patterns that investors can systematically exploit to earn excess returns. Furthermore, we find that bond yield spreads are fundamentally different to price series such as equity prices or currencies. Lastly, we categorize and evaluate five type of price gaps in the financial markets for the first time. We apply our price gap categorisation to twenty-eight futures contracts. Our results support the Gap- Fill hypothesis and find that some price gaps may provide additional information to investors by exhibiting returns that are statistically different to the unconditional returns over a short period of time. ՝In conclusion, this thesis provides empirical evidence that broadly support the usage of technical analysis in the financial markets.
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01 Jan 1986
TL;DR: In this article, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Abstract: A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.
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TL;DR: In this article, the authors used the Dow Jones Index from 1897 to 1986 to test two of the simplest and most popular trading rules (moving average and trading range break) by utilizing the bootstrap techniques.
Abstract: This paper tests two of the simplest and most popular trading rules—moving average and trading range break—by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, our results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models.
2,236 citations
Posted Content•
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