Global and Regional Sources of Risk in Equity Markets: Evidence from Factor Models with Time-Varying Conditional Skewness

We examine the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. The models allow for conditional heteroskedasticity and time-varying conditional skewness, and permit mean, variance and skewness spillovers to be measured. We find that the pattern of spillovers changed in the late 1990s. When spillovers are allowed to vary with the type of news arriving in a market, we find that local news reduces mean spillovers but increases variance spillovers. News about regional countries increases skewness spilloversAsymmetries, Skewness, Volatility, Spillover, Stock returns, News.

Global and Regional Sources of Risk in Equity Markets: Evidence from Factor Models with Time-Varying Conditional Skewness

This study examines the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. Besides conditional heteroskedasticity, the models allow shocks to have time-varying conditional skewness. The global factor appears less important for market volatility in models that permit time-varying conditional skewness. The influence of regional and global factors on risk is small in most of the markets, except in the late 1990s during which the regional factor accounted for a substantial portion of negative skewness in the markets' returns distribution.Asymmetries, Skewness, Volatility, Spillover, Stock returns

The importance of granularity in multiobjective optimization of mobile cloud hybrid applications

Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile cloud (MC) computing can meet the demands of even the most computationally and resource‐intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a trade‐off. To counter this problem, we propose a data‐driven technique that (1) does instrumentation by allowing class‐, method‐, and hybrid‐level configurations to be applied to the MC hybrid application and (2) measures, at runtime, how well the MC hybrid application meets these two objectives by generating data that are used to optimize the efficiency trade‐off. Our experimental evaluation considers two MC hybrid Android‐based applications. We modularized them first based on the granularity and the computationally intensive modules of the apps. They are then executed using a simple mobile cloud application framework while measuring the power and bandwidth consumption at runtime. Finally, the outcome is a set of configurations that consists of (1) statistically significant and nondominated configurations in collapsible sets and (2) noncollapsible configurations. The analysis of our results shows that from the measured data, Pareto‐efficient configurations, in terms of minimizing the two objectives, of different levels of granularity of the apps can be obtained. Furthermore, the reduction of battery power consumption with the cost of network bandwidth usage, by using this technique, in the two MC hybrid applications was (1) 63.71% less power consumption in joules with the cost of using 1.07 MB of network bandwidth and (2) 34.98% less power consumption in joules with the cost of using 3.73 kB of network bandwidth

Clinical Trials and Therapeutic Approaches for Healthcare Challenges in Pakistan

Pakistan faces tremendous challenges in providing healthcare due to a lack of consistent policymaking, increasing expenditure and exponential growth in population since its inception in 1947. These challenges are not just driven by politics, policy and allocation of resources but also by healthcare, environment and characteristics of the population biology. Clinical trials provide the best way to find population-specific, cost-effective treatments that do not merely mimic those used in wealthier nations. This article analyzes all clinical studies conducted with at least one site in Pakistan listed on ClinicalTrials.gov, combined with a short overview that considers new therapeutic approaches that can be investigated in future clinical trials. Therapies using repurposed medicines are of particular interest as they use affordable drugs that are already widely available

To Duckweeds (\u3cem\u3eLandoltia punctata\u3c/em\u3e), Nanoparticulate Copper Oxide is More Inhibitory than the Soluble Copper in the Bulk Solution

CuO nanoparticles (CuO-NP) were synthesized in a hydrogen diffusion flame. Particle size and morphology were characterized using scanning mobility particle sizing, Brunauer–Emmett–Teller analysis, dynamic light scattering, and transmission electron microscopy. The solubility of CuO-NP varied with both pH and presence of other ions. CuO-NP and comparable doses of soluble Cu were applied to duckweeds, Landoltia punctata. Growth was inhibited 50% by either 0.6 mg L−1 soluble copper or by 1.0 mg L−1 CuO-NP that released only 0.16 mg L−1 soluble Cu into growth medium. A significant decrease of chlorophyll was observed in plants stressed by 1.0 mg L−1 CuO-NP, but not in the comparable 0.2 mg L−1 soluble Cu treatment. The Cu content of fronds exposed to CuO-NP is four times higher than in fronds exposed to an equivalent dose of soluble copper, and this is enough to explain the inhibitory effects on growth and chlorophyll content

The Relationship between Market structure and innovation in industry equilibrium: a case study of the global automobile industry

We specify and estimate a dynamic game to study the equilibrium relationship between market structure and innovation in the automobile industry. The quality of each firm’s product for the average consumer, the key state variable, is modeled as stochastically increasing in innovation,the dynamic control, which is proxied by patent applications. Equilibrium innovation is a function of market structure, the vector of quality levels of all active firms, and the cost of R&D. Our main findings are as follows:(a) optimal innovation has an inverted-U shape in own quality; (b) holding own quality constant, innovation is declining in average rival quality but increasing in quality dispersion; and (c) following entry, each incumbent’s innovation declines, but aggregate innovation increases in most market structures. These findings are broadly consistent with the Schumpeterian hypothesis that market power leads to more innovation.status: publishe

ICA and Sparse ICA for Biomedical Signals

Biomedical signs or bio signals are a wide range of signals obtained from the human body that can be at the cell organ or sub-atomic level Electromyogram refers to electrical activity from muscle sound signals electroencephalogram refers to electrical activity from the encephalon electrocardiogram refers to electrical activity from the heart electroretinogram refers to electrical activity from the eye and so on Monitoring and observing changes in these signals assist physicians whose work is related to this branch of medicine in covering predicting and curing various diseases It can also assist physicians in examining prognosticating and curing numerous condition

Entry- and Sunk-Cost Spillovers from the Rival: Evidence from Entry and Expansion of KFC and McDonald\u27s in Chinese Cities

We model the entry and expansion of KFC and McDonald\u27s in Chinese cities as a dynamic game. We assume that the observed entry and expansion decisions are equilibrium outcomes. This allows us to recover the structural parameters of the game without solving for equilibrium. We use the estimated model to study the entry- and sunk-cost spillovers from the rival. Our estimates suggest substantial spillovers to the cost of entering a new city. For example, if the rival is not present in the city in which the chain is entering and the distance from the nearest city where the rival is present decreases by 100 kilometers, the cost of entry into the city decreases by 1.22 standard deviations for KFC and 1.52 standard deviations for McDonald\u27s. If the rival is already present in the city, a one-unit increase in the number of rival\u27s outlets decreases the cost of entry by 0.59 standard deviations for McDonald\u27s but increases it by 1.49 standard deviations for KFC. We also find that the spillovers to the sunk cost of opening a new outlet are much smaller. Hence the expansion within a city is not as much influenced by the presence of the rival as is the entry into a new city

Optimization of Process Parameters for CNC Turning using Taguchi Methods for EN24 Alloy Steel with Coated/Uncoated Tool Inserts

Coated and uncoated tool inserts offers certain degrees of control on the desired rate of tool wear and surface roughness to an extent. This work pursues the quest for realizing the optimal values for the significant process parameters that bears an influence on the response parameters. Experiments were conducted on the samples of EN 24 alloy steel material with the help of PVD coated TiAlN insert and uncoated carbide insert. The experimental runs carried out with proper variation in the levels. Levels are selected with the help of manufacturing catalogue and by pilot experimentation and results are recorded for further analysis. For this study, 9 runs designed using L9 orthogonal array of Taguchi Design of Experiment. Surface roughness was measured using a Mitutoyo surface tester at test lab and material removal rate is calculated by mathematical equation. The data was compiled into Minitab 17 software for analysis. The relationship between the machining parameters and the response variables were analyzed using the Taguchi Method. Optimization of process parameters is carried out by Grey Relational Analysis method (GRA). GRA method is a powerful and most versatile tool which can manipulate the input data as per requirement and comes with results that can be used to have best multi-objective in respective concerns