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Significant second-moment transmission effects and obvious time-varying patterns of correlation coefficients among major equity and currency markets in the US, Japan and the UK are found to exist. Such observations inspire the time-varying setting of dynamic conditional correlation coefficients in MGARCH models. On the other hand, the multivariate Student-t distribution is suitable for analysing the visible leptokurtosis that is common in financial markets. Both are important for international portfolio risk management. Thus, a comparison on the hedging efficiency of hypothetical portfolios consisting of stock and currency future positions is conducted in order to justify the multivariate Student-t distribution based on the DCC-MGARCH model.

This study compares efficiencies of five Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models in terms of value at risk (VaR) backtesting on the number of prediction failures and the average deviation between VaR and realized return series. Unlike the previous literature which presumes constant correlation coefficients, a new model proposed by Engle (2002, the DCC model) is applied to highlight time-varying conditional correlations amongst positions, which is essential for portfolio risk management. From the empirical studies of exchange rates data including the US Dollar to British Pound, Japanese Yen and Euro Dollar, we find that the DCC model produces least prediction failures.

This article applies the dynamic conditional correlation model of Engle (2002) with error correction terms in order to investigate the optimal hedge ratios of British and Japanese currency futures markets. For a comparison, the estimates of three other models -- traditional generalized autoregressive conditional heteroskedasticity (GARCH), ordinary least square (OLS) and error correction model (ECM) -- are also reported. Results show that the dynamic conditional correlation model yields the best hedging performance in both futures markets. Nonetheless, the traditional multivariate GARCH model (which exhibits constant conditional correlations and time-varying hedge ratios) performs the worst hedging effectiveness, even inferior to the time-invariant hedging methods (OLS and ECM). The inclusion of dynamic conditional correlations in the GARCH model can therefore better capture the frequent fluctuations in futures markets.

This paper explores optimal international asset allocation policies subjected to the equity holding constraint within an intertemporal framework. To deal with the co-existent realities of agents’ heterogeneous preferences and international market friction, the perturbation method is employed to derive approximate analytic solutions.

Hsin-Hung Chen,1 Wen-Chia Huang,2 Wen-Hsuan Chiang,2 Te-I Liu,2 Ming-Yin Shen,2,3 Yuan-Hung Hsu,4 Sung-Chyr Lin,1 Hsin-Cheng Chiu2 1Department of Chemical Engineering, National Chung Hsing University, Taichung, 2Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, 3Department of Surgery, National Taiwan University Hospital-Hsinchu Branch, 4Pharmaceutical Optimization Technology Division, Biomedical Technology and Device Research Laboratory, Industrial Technology Research Institute, Hsinchu, Taiwan Abstract: In this study, a novel pH-responsive cholesterol-PEG adduct-coated solid lipid nanoparticles (C-PEG-SLNs) carrying doxorubicin (DOX) capable of overcoming multidrug resistance (MDR) breast cancer cells is presented. The DOX-loaded SLNs have a mean hydrodynamic diameter of ~100nm and a low polydispersity index (under 0.20) with a high drug-loading efficiency ranging from 80.8% to 90.6%. The in vitro drug release profiles show that the DOX-loaded SLNs exhibit a pH-controlled drug release behavior with the maximum and minimum unloading percentages of 63.4% at pH 4.7 and 25.2% at pH 7.4, respectively. The DOX-loaded C-PEG-SLNs displayed a superior ability in inhibiting the proliferation of MCF-7/MDR cells. At a DOX concentration of 80 µM, the cell viabilities treated with C-PEG-SLNs were approximately one-third of the group treated with free DOX. The inhibition activity of C-PEG-SLNs could be attributed to the transport of C-PEG to cell membrane, leading to the change of the composition of the cell membrane and thus the inhibition of permeability glycoprotein activity. This hypothesis is supported by the confocal images showing the accumulation of DOX in the nuclei of cancer cells and the localization of C-PEG on the cell membranes. The results of in vivo study further demonstrated that the DOX delivered by the SLNs accumulates predominantly in tumor via enhanced permeability and retention effect, the enhanced passive tumor accumulation due to the loose intercellular junctions of endothelial cells lining inside blood vessels at tumor site, and the lack of lymphatic drainage. The growth of MCF-7/MDR xenografted tumor on Balb/c nude mice was inhibited to ~400mm3 in volume as compared with the free DOX treatment group, 1,140mm3, and the group treated with 1,2 distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)] solid lipid nanoparticles, 820mm3. Analysis of the body weight of nude mice and the histology of organs and tumor after the administration of DOX-loaded SLNs show that the SLNs have no observable side effects. These results indicate that the C-PEG-SLN is a promising platform for the delivery of therapeutic agents for MDR cancer chemotherapy. Keywords: pH-responsive, solid lipid nanoparticles, multidrug resistance, permeability glycoprotein