Seasonal Studies on Primary Production and Associated Environmental Parameters in the Indian EEZ

The thesis describes the importance of Indian EEZ, definition and the various factors affecting primary production, general account of phytoplankton and its importance in marine ecosystem etc. In review of literature, general oceanography of Arabian Sea and Bay of Bengal and hydrography of eastern Arabian Sea and western Bay of Bengal. It deals with the distribution patterns of primary production, chlorophyll a, phytoplankton composition and particulate organic carbon in the eastern Arabian Sea and western Bay of Bengal during different seasons. Factors that affect primary productivity are irradiance, temperature, stability of the surface waters, nutrients and zooplankton grazing. The differential biological response of eastern Arabian Sea and western Bay of Bengal to monsoonal regimes. A precise estimation on the primary production of the entire EEZ of India on a regional basis and on a seasonal scale would be the only way to achieve any kind of predictive assessment on the fish stock and their sustainable yield. This study mainly envisages the qualitative and quantitative aspects on the magnitude of phytoplankton standing crop and production of organic carbon and their relationship to environmental characteristics during summer monsoon, Inter monsoon and winter monsoon periods in the east and west coasts of the Indian EEZ.This study revealed that the seasonality exerts a great impact on the biological production in the eastern Arabian Sea and western Bay of Bengal. High biological production may be the reason why most of the fish landings are Concentrated in the west coast of India than east coast. The present data on Phytoplankton production rate and the species composition will provide a meaningful ground for evaluations of exploitable renewable resources of the IndianEEZ

Computationally Efficient Bootstrap Prediction Intervals for Returns and Volatilities in ARCH and GARCH Processes

We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.