Examining the Time Varying Spillover Dynamics of Indian Financial Indictors from Global and Local Economic Uncertainty.

The research aims to excavate the role of global (Fed Rate, Crude, Real Dollar Index) and endogenous economic variables (GDP and Consumer Price Index) in shaping the spillover amongst the major Indian Financial indicators, viz. Nifty Index, MCX Gold, USDINR, Govt. Bond 10Y maturity and agricultural index N-Krishi. To facilitate cross-comparison decomposition of time-varying spillover output generated from Time-Varying Vector Autoregression (TVP-VAR) with aggregation at three layers is performed. The research finds that Indian Financial Indicators are vulnerable to spillover shocks from global variables predominantly driven by Fed Rate and Real Dollar Index. USDINR turns out to be most sensitive to global shocks and transgresses the shock to other financial indicators. Importantly, persistently high inflation has brought volatility spikes in the directional spillover to financial indicators. Though spillover subsidence is observed post-2014, with an all-time high during GFC, a sudden spurt in all financial indicators has been observed post-Covid-19, with Govt. bonds showing a sporadic rise. An important observation relates to staunch spillover from GDP during GFC with reoccurrence post-Covid. Additionally, a closely knit spillover tie is observed among USDINR, N-Krishi, and Crude. The study is beneficial to RBI to proactively monitor the weakening rupee along with Fed tapering to manage the rising spillover post-Covid-19. The effort of RBI has to be reciprocated by the government in inflation targeting to reinforce the curbing efforts of rising shock spillover.

Systemic spillover dynamics of crude oil with Indian Financial indicators in post WPI revision and COVID era

The study first investigates the sensitivity of major Indian financial indicators, viz. Equity Index (Nifty), Exchange Rate (USDINR), Bond (Govt 10Y Bond), Gold, Agricultural Index (N-Krishi) to ascertain the existence of significant sensitivity to standard shocks Engle and Campos-Martins (2020) either arising due to global political/economic factor or endogenous macroeconomic scenario. After that, the study undertakes the systemic spillover dynamics of Crude by estimating a self-exciting regime-switching Threshold Vector Auto-regression model based on cut-off values of Crude to test shock transmission from Crude amid major global events empirically. The findings indicate the existence of three regimes with threshold cut-offs for Crude return, viz. −2.358% and 2.294% for 2008 and 2020, respectively. After that Spillover Index is estimated across the three regimes. The findings indicate a sporadic increase in spillover during the Covid era, GFC and Oil Crisis. Despite Govt. Bond ranking high insensitivity, the spillover linkage is low. The results also indicate that, on average, systemic volatility shocks from crude oil are highest in Gold. In fact, Gold's sensitivity to crude price fluctuations escalates to new heights during the 2008–09 crisis, thus serving as a source of idiosyncratic shocks. Moreover, in recent duration, a unique see-saw kind of link has emerged between crude oil and Gold, where downward pressure by Crude on USDINR is eased by a fall in gold imports. Moreover, an analysis of the spillover pattern across the Wholesale Price Index (WPI) 2012 shows an intensification of spillover from Crude. The study is beneficial to policymakers to apply a systemic approach while empirically analyzing the spillover from a Global commodity such as Crude.

Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform.

The novel coronavirus outbreak has spread worldwide, causing respiratory infections in humans, leading to a huge global pandemic COVID-19. According to World Health Organization, the only way to curb this spread is by increasing the testing and isolating the infected. Meanwhile, the clinical testing currently being followed is not easily accessible and requires much time to give the results. In this scenario, remote diagnostic systems could become a handy solution. Some existing studies leverage the deep learning approach to provide an effective alternative to clinical diagnostic techniques. However, it is difficult to use such complex networks in resource constraint environments. To address this problem, we developed a fine-tuned deep learning model inspired by the architecture of the MobileNet V2 model. Moreover, the developed model is further optimized in terms of its size and complexity to make it compatible with mobile and edge devices. The results of extensive experimentation performed on a real-world dataset consisting of 2482 chest Computerized Tomography scan images strongly suggest the superiority of the developed fine-tuned deep learning model in terms of high accuracy and faster diagnosis time. The proposed model achieved a classification accuracy of 96.40%, with approximately ten times shorter response time than prevailing deep learning models. Further, McNemar's statistical test results also prove the efficacy of the proposed model.