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AbstractDoes Indian sovereign yield volatility reflect economic fundamentals, or whether it is a self-generated force flowing through markets with little connection to such fundamentals? To answer the question, this research explores the volatility dynamics and measures the persistence of shocks to the sovereign bond yield volatility in India from 1 January 2016, to 18 May 2022, using a family of GARCH models. The empirical results indicate the high volatility persistence across the maturity spectrum in the sample period. However, upon decomposing the markets into bull and bear phases, our results support the existence of weak volatility persistence and rapid mean reversion in the bear market. This shows that the economic response policies implemented by the government during the pandemic, including fiscal measures, have a restraining effect on sovereign yield volatility. For a positive γ, the results suggest the possibility of a “leverage effect” that is markedly different from that frequently seen in stock markets. Results further indicate that the fluctuations in Indian sovereign yields cannot be dissociated from inflation and money market volatility. Our findings herein provide valuable information and implications for policymakers and financial investors worldwide.

The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.

Abstract Aim This study was designed to evaluate the frequency of developing second and subsequent primary cancers among cancer survivors. Methods We have retrospectively analyzed 121 multiple primary cancer patients treated at Bhagwan Mahavir Cancer Hospital and Research Centre, Jaipur, Rajasthan, India from the last 20 years. The survival analysis was performed by Kaplan-Meier methodology. Results The prevalence of multiple primary malignant tumors (MPMTs) was 1.51% (121/8000), with a male to female ratio of 1:1.42. In males most of the reported MPMTs were synchronous and most common first primary cancer cases were head & neck and lung whereas, in females most of the reported MPMTs were metachronous and most common first primary cancer cases were breast and gynaecological malignancies. Family history was reported in 15 cases. Maximum 33.05% patients received combined treatment of chemotherapy, radiotherapy and 91.73% (111/121) of patients with MPMTs were effectively followed up, 35 (28.92%) patients died and 5-year survival rate of the remaining 86 patients was 68.46%. Conclusions In the present observational study the most frequent sites of MPMTs in men were head & neck and lung whereas, in women breast and gynaecological sites were common. Therefore, careful monitoring and follow up are required for these patients.

The unprecedented outbreak of COVID-19 has affected every aspect of the human life, be it health, social, or economic dimensions. The anxiety and uncertainty wobbled the economies of affected countries worldwide. This study attempts to quantify the impact of COVID-19 on the performance of major stock markets of G-7 nations vis-à-vis BRICS nations. An event study methodology is employed to capture the effect of the systematic event in the form of Buy and Hold Abnormal Returns (BHAR) and Average Buy and Hold Abnormal Returns (ABHAR). The study considers a 90-day observation window, consisting of six sub-event windows after the COVID-19 news up-doves the world, and 120 days prior to the selected event date to estimate average expected returns. BHAR values in the four event windows are statistically significant, covering stock markets from panic and nosedive to their correction and recovery. ABHAR values reported are significantly negative in the event window ranging from –0.15% to –38.43% for G-7 and –0.06% to –37.12% for BRICS nations. Despite similar ABHAR trends, the BHAR values and correlation matrix exhibit a diverse reaction in BRICS nations compared to the highly synchronized reaction in the G-7 group of nations in the COVID period.