A key step in the usual procedure for modeling a loss development pattern is to fit formulas to empirical age-to-age or age-to-ultimate factors. Having a fitted formula is useful because it provides an easy way to smooth the bumps found in most series of empirical factors. Also, if the fit is to age-to-ultimate factors, the formula usually provides a convenient way to interpolate the factors.
While the fitting is convenient and practical, it can hardly be said to have a substantive conceptual foundation. A formula is chosen because it is easy to compute and because it nicely fits the age-to-age factors. It is not derived from more basic assumptions in the sense that nothing is specifically built in to reflect that it is being fitted to data that represent ratios of loss for a particular exposure period as of given evaluation ages.
In traditional economic theory business cycles are usually described as stochastic fluctuations from a determined or a semi – stochastic trend. A leading indicator is normally the aggregate of several macroeconomic time series that separately or put together can give a pre-warning of possible developments and turning points in the economy, often represented by the gross domestic product (GDP) or industrial production (IP). One typical objective of a business cycle analysis is to search for time series that have a high degree of co-movement with the future cyclical behaviour in the macroeconomic activity. As an example, in the 1980s, the Norwegian Central Bank used a leading indicator containing approximately 20 underlying indicators to monitor and forecast the future behaviour of the domestic economy.
Economic growth is normally a necessary precondition for growth in demand for various dry bulk commodities; which again drives seaborne dry bulk demand. Therefore, the freight demand also has a tendency to fluctuate and show a cyclical behaviour. Analogously, we assume that traditional business cycle theory can be used to analyse these time series. For several years now, economic growth in Asia in general and in China, South Korea and Japan in particular, have been the prime drivers of global shipping demand. In this paper we use import volumes of iron ore and coal to these three economies as a proxy for dry bulk seaborne demand. We then test if there exists any correlation between specific economic indicators and the change in the imports.
Asian cities cannot finance the infrastructure investments they need without accessing private domestic savings. Urban growth has multiplied demand for investment in water systems, wastewater collection and treatment, roads, and other facilities. At the same time, decentralization strategies have shifted much of the responsibility for this investment to local governments. Private financing can be attracted to urban infrastructure in different ways including direct private investment in income earning facilities but perhaps the most critical avenue will be the local credit market. In a world of decentralized governance, domestic credit markets must be capable of generating long-term financing for cities and their infrastructure agencies.
Two models of municipal credit markets are considered here: (i) bank lending, which financed municipal investment in western Europe throughout most of the 20th century and is still the primary source of local credit financing there; and (ii) municipal bonds, which have been the foundation of municipal borrowing in North America. In designing local credit initiatives for Asia or other parts of the developing world, policy makers do not have to choose between these two systems, which are converging in their regions of origin. Countries now building or strengthening local credit markets would do well to select characteristics from both models and, even more, to encourage competition on a level playing field between bank lending and bond issuance.
