Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.

A frontier functions approach to optimal scales of sustainable production

This paper translates the concepts of sustainable production to three dimensions of economic, environmental and ecological sustainability to analyze optimal production scales by solving optimizing problems. Economic optimization seeks input-output combinations to maximize profits. Environmental optimization searches for input-output combinations that minimize the polluting effects of materials balance on the surrounding environment. Ecological optimization looks for input-output combinations that minimize the cumulative destruction of the entire ecosystem. Using an aggregate space, the framework illustrates that these optimal scales are often not identical because markets fail to account for all negative externalities. Profit-maximizing firms normally operate at the scales which are larger than optimal scales from the viewpoints of environmental and ecological sustainability; hence policy interventions are favoured. The framework offers a useful tool for efficiency studies and policy implication analysis. The paper provides an empirical investigation using a data set of rice farms in South Korea.

Analysis of resource efficiency : a production frontier approach

This article integrates the material/energy flow analysis into a production frontier framework to quantify resource efficiency (RE). The emergy content of natural resources instead of their mass content is used to construct aggregate inputs. Using the production frontier approach, aggregate inputs will be optimised relative to given output quantities to derive RE measures. This framework is superior to existing RE indicators currently used in the literature. Using the exergy/emergy content in constructing aggregate material or energy flows overcomes a criticism that mass content cannot be used to capture different quality of differing types of resources. Derived RE measures are both ‘qualitative’ and ‘quantitative’, whereas existing RE indicators are only qualitative. An empirical examination into the RE of 116 economies was undertaken to illustrate the practical applicability of the new framework. The results showed that economies, on average, could reduce the consumption of resources by more than 30% without any reduction in per capita gross domestic product (GDP). This calculation occurred after adjustments for differences in the purchasing power of national currencies. The existence of high variations in RE across economies was found to be positively correlated with participation of people in labour force, population density, urbanisation, and GDP growth over the past five years. The results also showed that economies of a higher income group achieved higher RE, and those economies that are more dependent on imports and primary industries would have lower RE performance.

Stochastic output feedback model predictive control

This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law

Stochastic ranking method for thermostatically controllable appliances to provide regulation services

Demand response can be used for providing regulation services in the electricity markets. The retailers can bid in a day-ahead market and respond to real-time regulation signal by load control. This paper proposes a new stochastic ranking method to provide regulation services via demand response. A pool of thermostatically controllable appliances (TCAs) such as air conditioners and water heaters are adjusted using direct load control method. The selection of appliances is based on a probabilistic ranking technique utilizing attributes such as temperature variation and statuses of TCAs. These attributes are stochastically forecasted for the next time step using day-ahead information. System performance is analyzed with a sample regulation signal. Network capability to provide regulation services under various seasons is analyzed. The effect of network size on the regulation services is also investigated.

Introduction - There's crime out there, but not as we know it: Rural criminology - the last frontier

The idea that crime is a predominantly urban phenomenon has been pervasive in criminology, so much so that Australian criminology textbooks do not recognise rural crime as a distinct phenomenon worthy of scholarly attention (see Chappell & Wilson, 2000; Goldsmith et al, 2003; White & Haines, 2004; White & Habibis, 2005). There are no chapters or sections in Australian texts which specifically examine rural crime, despite the inclusion of a range of topics that appear to provide a broad coverage of crime in its many temporal and spatial dimensions. Nor is there so much as an index reference to "rural" issues in criminology textbooks. The standardised syllabus for crime texts provides coverage of topics such as violent crime, public crime, delinquency, race and crime, gender and crime, and crime and social class. This canon is mirrored in international texts, most of which also fail to address the issue of rural crime, but make abundant reference to crime in various urban contexts (see Carrabine et al, 2004; Conklin, 2004). This is not to suggest that Australian texts fail to localise their subject matter.

Stochastic analysis of transit route segments' passenger load variation for capacity and quality of service assessment

This paper investigates stochastic analysis of transit segment hourly passenger load factor variation for transit capacity and quality of service (QoS) analysis using Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia. It compares stochastic analysis to traditional peak hour factor (PHF) analysis to gain further insight into variability of transit route segments’ passenger loading during a study hour. It demonstrates that hourly design load factor is a useful method of modeling a route segment’s capacity and QoS time history across the study weekday. This analysis method is readily adaptable to different passenger load standards by adjusting design percentile, reflecting either a more relaxed or more stringent condition. This paper also considers hourly coefficient of variation of load factor as a capacity and QoS assessment measure, in particular through its relationships with hourly average and design load factors. Smaller value reflects uniform passenger loading, which is generally indicative of well dispersed passenger boarding demands and good schedule maintenance. Conversely, higher value may be indicative of pulsed or uneven passenger boarding demands, poor schedule maintenance, and/or bus bunching. An assessment table based on hourly coefficient of variation of load factor is developed and applied to this case study. Inferences are drawn for a selection of study hours across the weekday studied.

Stochastic analysis of transit route segments' passenger load variation for capacity & quality of service assessment

This study uses weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia •Stochastic analysis is compared to peak hour factor (PHF) analysis for insight into passenger loading variability •Hourly design load factor (e.g. 88th percentile) is found to be a useful method of modeling a segment’s passenger demand time-history across a study weekday, for capacity and QoS assessment •Hourly coefficient of variation of load factor is found to be a useful QoS and operational assessment measure, particularly through its relationship with hourly average load factor, and with design load factor •An assessment table based on hourly coefficient of variation of load factor is developed from the case study