This PhD dissertation research primarily aims to empirically investigate into two financial topics using annual and monthly data sets of market-capitalization based size portfolio returns from the US stock market for the period 1925 to 2012. Using size-based portfolio returns is a pioneering effort for both topics. The first empirical research using annual data is on short and long horizon stock return predictability using three widely selected ratios in terms of price-output, price-earnings and price-dividend. Using univariate and multivariate predictive regressions for horizons from one year to fifteen years for the full sample and three different sub-samples for comparison reasons with the previous research using aggregate stock market data, it is reported that both short and long horizon return predictability exists albeit with different predictive ability for different horizons. Among the three selected ratios, overall the price-output ratio is empirically favoured as a superior predictor of stock returns. The empirical findings refer to that this is robust across the three sub-samples investigated. It is empirically shown that size significantly matters in terms of return predictability. The second empirical research using monthly data is on the analysis of impact of macroeconomic volatility in terms of inflation and industrial production growth on asymmetric time-varying volatility of stock returns. Using a two-stage econometric methodology, first, based on estimation of asymmetric conditional volatilities of stock returns and macroeconomic variables, and then employing a vector autoregression methodology; it is reported that volatility of size-based portfolio returns are, in general, not significantly dependent on macroeconomic volatility. It is also shown that stock return volatility is more responsive to its own previous shocks as shown by the variance decomposition. It is also found that size does not matter in this specific case.

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains v, 95 p. : ill. (some col.). Includes abstract. Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2005. / Vita. Includes bibliographical references.

本篇論文擴充Ang et al. (2007)之基本架構,分別建構台灣各式月資料與季資料的物價指數預測模型,並進行預測以及實證分析。我們用以衡量通貨膨脹率的指標為 CPI 年增率與核心CPI 年增率。我們比較貨幣模型、成本加成模型、6 種不同設定的菲力浦曲線模型、3 種期限結構模型、隨機漫步模型、 AO 模型、ARIMA 模型、VAR 模型、主計處(DGBAS)、中經院(CIER) 及台經院(TIER) 之預測。藉由此研究,我們可以完整評估出文獻上常用之各式月資料及季資料預測模型的優劣。 我們實證結果顯示,在月資料預測模型樣本外預測績效表現方面, ARIMA 模 型對 2 種通貨膨脹率指標的樣本外預測能力表現最好。至於季資料預測模型樣本外預測績效表現, ARIMA 模型對未來核心 CPI 年增率的樣本外預測能力表現最好; 然而,對於 CPI 年增率為預測目標的預測模型則不存在最佳的模型。此外,實證分析中我們也發現本研究所建構的模型預測表現仍遜於主計處的預測,但部份模型的樣本外預測能力表現則比中經院與台經院的預測為佳。 / This paper compares the forecasting performance of inflation in Taiwan. We conduct various inflation forecasting methods (models) for two inflation measures(CPI growth rate and core-CPI growth rate) by using monthly and quarterly data. Besides the models of Ang et al. (2007), we also consider some macroeconomic models for comparison. We compare some Monetary models, Mark-up models, six variants of Phillips curve models, three variants of term structure models, a Random walk model, an AO model, an ARIMA model, and a VAR model. We also compare the forecast ability of these model with three different survey forecasts (the DGBAS, CIER, and TIER surveys). We summarized our findings as follows. The best monthly forecasting model for both inflation measures is ARIMA model. For quarterly core-CPI inflation, ARIMA model is also the best model; however, when comparing the quarterly forecasts for CPI inflation, there does not exist the best one. Besides, we also found that the DGBAS survey outperforms all of our forecasting methods/models, but some of our forecasting models are better than the CIER and TIER surveys in terms of MAE.