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We analyze the cross-sectional expected return of KOSPI stocks using equity duration. From 1991 to 2018, we calculate equity durations for the KOSPI listed stocks (including de-listed stocks) and find that the shorter the equity duration, the higher the risk premium. Using the 4-factor model with equity duration added to the benchmark 3-factor model, the explanatory power of the 4-factor model is superior to that of the existing benchmark model in accounting for risk premiums. This is an unusual finding that is not readily explainable by the traditional CAPM or the Fama-French 3-factor model. This can be interpreted that the equity duration is a separate and significant risk factor dissociated from the HML of the 3-factor model.

With the globalization and liberalization in terms of increasing financial flows across the countries, the policy makers around the world are not independent in the context of monetary and fiscal policy initiatives. In this regard, this paper aims to attempt to quantify and capture long run, short run as well as time-varying linkages among the two financial stress indices, namely, Kansas City Financial Stress Index (KCFSI) and Indian Financial Stress Index (IFSI) across the monthly period (2004 to 2014).

Owing to the non-existence of a standardized financial stress index with regards to the Indian financial system, the study has developed an index/stress indicator using principal component analysis. Furthermore, to comprehend the linkages, the study uses bivariate Johansen cointegration model, vector error correction model, impulse response functions (IRF), variance decomposition analysis (VDA), Toda-Yamamoto’s Granger causality test and, finally, bivariate generalized autoregressive conditional heteroskedastic (BVGARCH) (1,1) model under constant conditional correlation (CCC) framework.

The results report a stochastic trend among the two indices wherein the US financial system acts as a source of a shock causing disequilibrium in the long run co-movement. About 40 per cent of the adjustments take place in one month and rest in the coming months. Both the IRF and VDA report a greater degree impact of the US financial stress on the Indian financial system. Moreover, there is a uni-directional short run causality running from the stress in the US financial system to the Indian financial stress. Furthermore, the co-movement between the US and Indian financial stress reached to its maximum significant level during the sub-prime crisis even confirmed by the Markov switching model results.

Overall, the results provide an insight to the financial market investors both domestic as well as international in their act of risk management. The financial stress prevailing in an economy further has an impact on different economic factors like foreign exchange rates, interest rates, yield curves, equity market returns and volatility. So, the empirical results support strong implications for the Indian policy makers as well as investors in the Indian financial markets.

The present study contributes to the literature in three senses. First, the study considers indices reflecting financial stress in the Indian as well as US financial system. Second, the study captures long run as well as short run linkages among the financial stress indices relating to a developed and an emerging market. Finally, the study uses CCC-BVGARCH (1,1) model to account for the time-varying co-movement among the financial stress indices. This helps in comprehending time-varying nature of the co-movement of the stress in the financial system prevalent in the respective markets.

Credit migration correlation is a critical assumption for the integration of market risk and credit risk within enterprise‐wide risk management. This article describes hypothesis testing performed on credit migration correlation, based on two models: 1) a factor model and 2) an asset‐value model. These tests involve both the correlation between obligors and the correlation between credit migration events and systematic market risk factors. The author concludes from the test results that over shorter risk horizons (e.g., biweekly or monthly) where all relevant underlying processes are distributed multi‐variate normal, non‐zero positive correlation weights overestimate risk capital requirements, on average.

After a seven-year period of being stuck in the zero lower bound (ZLB) range, the target rate was raised by 25 basis points on December 16, 2015. Prior to the rate hike, the important issues that the Federal Reserve dealt with were the magnitude, timing, and the information conveyed by a first-time rate hike from the ZLB period. The purpose of this paper is to use the data from the ZLB period and simulate the impact of an increase in the proxies for the federal funds rate: effective federal funds rate and shadow rate, and measure the impact on the resulting changes in credit default swap (CDS) spreads across 11 industries. Increases in both proxies predict a significant decrease in CDS spreads which is indicative of an economic recovery. This prediction is confirmed by the announcement effect of the actual rate increase on December 16, 2015 and the three subsequent rate increases.

In the absence of target rate changes in the ZLB environment, the authors use a recursive vector autoregressive (VAR) model to simulate the rate increases in proxies for target federal rate and predict the impact on the economy by observing the reaction in CDS spreads and stock returns across 11 industries.

The impulse response indicates that an increase of one standard deviation in the effective rate (approximately 25 basis points) results in a statistically significant decrease in the spreads of CDS contracts in 8 of the 11 sectors studied in this research. Similar results obtain for an increase in shadow rate thus providing a robustness check. These results suggest a rate increase from the ZLB period and the resulting dynamics captured in the VAR system is indicative of an economic recovery.

Prior studies have used the event study methodology to evaluate the impact of rate changes on credit spreads. The ZLB environment does not contain data on target rate changes and renders the event study methodology as ineffective. This paper is the first to simulate the implications of a first-time rate increase from the ZLB environment in the context of a recursive VAR model. The results are very helpful to the Federal Reserve of countries experiencing a ZLB environment such as Japan and Europe.

Insurance companies play a significant role in every economy; hence, it is essential to investigate and understand the factors that propel their profitability. Unlike previous studies that present a linear relationship, this study provides initial evidence by exploring the non-linear impacts of the determinants of profitability amongst life insurers in South Africa.

The study uses a panel dataset of 62 life insurers in South Africa, covering 2013–2019. The generalised method of moments and the dynamic panel threshold estimation technique were used to estimate the relationship.

The empirical results from the direct relationship reveal that investment income and solvency significantly predict life insurance companies' profitability. On the other hand, underwriting risk, reinsurance and size reduce profitability. Further, the dynamic panel threshold analysis confirms non-linearities in the relationships. The results show that insurance size, investment income and solvency promote profitability beyond a threshold level, implying a propelling effect on life insurers' profitability at higher levels. Below the threshold, these factors have an adverse effect. The study further points to underwriting risk, reinsurance and leverage having a reduced effect on life insurers' profitability when they fall above the threshold level.

The findings suggest that insurers interested in boosting their profit position must commit more resources to maintain their solvency and manage their assets and returns on investment. The study further recommends that effective control of underwriting risk is critical to the profitability of the life insurance industry.

The study contributes to the literature by providing first-time evidence on the determinants of life insurance companies' profitability by way of exploring threshold effects in South Africa.

Systemic risk has been a cause of concern for the bank regulatory authorities worldwide since the global financial crisis. This study aims to identify systemically important banks (SIBs) in India by using SRISK to measure the expected capital shortfall of banks in a systemic event. The sample size comprises a balanced data set of 31 listed Indian commercial banks from 2006 to 2019.

In this study, the authors have used SRISK to identify banks that have a maximum contribution to the systemic risk of the Indian banking sector. Leverage, size and long-run marginal expected shortfall (LRMES) are used to compute SRISK. Forward-looking LRMES is computed using the GJR-GARCH-dynamic conditional correlation methodology for early prediction of a bank’s contribution to systemic risk.

This study finds that public sector banks are more vulnerable to macroeconomic shocks owing to their capital inadequacy vis-à-vis the private sector banks. This study also emphasizes that size should not be used as a standalone factor to assess the systemic importance of a bank.

Systemic risk has attracted a lot of research interest; however, it is largely limited to the developed nations. This paper fills an important research gap in banking literature about the identification of SIBs in an emerging economy, India. As SRISK uses both balance sheet and market-based information, it can be used to complement the existing methodology used by the Reserve Bank of India to identify SIBs.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

Although the pervasive influence of investor sentiment in equity markets is well documented, little is known about behavioral manifestations in bond markets. In this paper, we explore the impact of investor sentiment on corporate bond yield spreads. Our results reveal that bond yield spreads co‐vary with sentiment, and sentiment‐drivenmispricings and systematic reversal trends are very similar to those for stocks. Bonds appear underpriced (with high yields) during pessimistic periods and overpriced (with low yields) when optimism reigns. Consequent reversals result in predictable trends in post‐sentiment yield spreads.When beginning‐of‐period sentiment is low, subsequent yield spreads are low; high sentiment periods are followed by high spreads. High‐yield bonds (low ratings, Industrials and Utilities, extreme maturities or low durations, specially if low rated) demonstrate greater susceptibility to mispricings due to sentiment compared to low‐yield bonds. The incremental yield spread gap between highand low‐yield bonds converges subsequent to periods of low sentiment, and diverges after high sentiment. Equity attributes marginally influence the impact of sentiment on bond spreads, but mostly for distressed bonds only.