Investigating the Determinants of Credit Spread Using a Markov Regime-Switching Model: Evidence from Banks in Taiwan

In this study, we investigate the determinants of credit spread using a Markov regime-switching model. We consider corporate governance variables and credit risk to analyze the determinants of credit spread. The corporate governance mechanism is an indicator of company sustainability, and credit spread is the main factor in profits obtained by banks. However, the relationship between credit spread and corporate governance is seldom discussed. We focus on loans from banks in Taiwan between 2000 and 2019 and apply a Markov regime-switching model, which is superior to other models in capturing different effects in various regimes. We specify two regime types: corporate governance and credit risk regimes. Furthermore, we consider four aspects of corporate governance: firm ownership structure, board structure, deviation, and information environment. In this study, the determinants of credit spread are investigated more thoroughly than in previous studies. Moreover, in this study, we examine the effects of monetary policy and economic status on credit spread using a Markov regime-switching model; such models are not employed to their full extent in related studies of credit spread. Empirical results indicate that credit spread has different effects in various regimes. Thus, understanding the determinants of credit spread in different regimes is crucial for financial analysts, investors, economic policymakers, and banks. Consequently, we expect that this study can improve the management and measurement of credit risk and be of value to financial institutions.

Nordic stock market performance of the travel and leisure industry during the first wave of Covid-19 pandemic

This article investigates the performance of the stock market and its volatility in the travel and leisure industry for three Nordic countries using daily data from June 2018 to June 2020, a period that includes the first wave of Covid-19 pandemic. The methodology is based on the Markov regime switching model that allows unobservable regime shifts in the stock return relationship between the travel and leisure industry and the overall market in the period before the outbreak of Covid-19 crisis and during the recovery period at the end of the first wave. The results provide strong evidence of regime switching behaviour in the form of idiosyncratic risk as measured by volatility. The period before Covid-19 corresponds to a low/medium idiosyncratic risk, while the period of the pandemic is characterized by a regime with high idiosyncratic risk. Overall, the timing, likelihood and duration of this crisis regime depend on the composition of the travel and leisure firms. Those with a large proportion of online gambling firms perform better, while those consisting of international transportation firms, hotels and restaurants perform negatively. This study shows that the high-frequency data and the model chosen here can provide timely information on the impact of the pandemic on various tourism and leisure businesses that could be useful for policymaking.

Stock Returns and Valuation Ratios at Sector Level in South Africa: The Regime-switch Modelling Approach

There is mounting evidence of stock return predictability based on valuation ratios across various stock markets. Most studies in this regard assume that the link between stock returns and valuation ratios is constant and linear. Yet, return predictability may vary according to the prevailing market regime. Accordingly, this study investigated whether the dividend and price-earnings valuation ratios predict returns on six sector indices on the Johannesburg Stock Exchange and whether that predictability is dependent on the prevailing market regime. The study employed a Markov regime-switching model over a sample period spanning from 1996:01 to 2018:12. The results showed that in most sectors, predictability was present, and its significance was dependent on whether the market was in a bullish or bearish regime. These findings are useful to investors who use valuation ratios to predict returns and adjust portfolios in various sectors across different market regimes on the South African market.

Hybrid Balanced Justified Treynor ratio

PurposeRisk and return are the most important components in the financial and investment world and the existence of a better balance between them with the goal of the best solution for investing in different assets has always been studied and discussed by researchers. For this purpose in this study introduced the Hybrid Balanced Justified Treynor ratio (HBJTR) criterion.Design/methodology/approachThis study introduced the HBJTR criterion, which has three major attributes, including combination of both the frequency and severity of the risk using Markov regime switching model which was modeled on the Justified Beta (Jßi). The second is the merger of data of both the cycles of boom and recession, which was modeled on the Hybrid Justified Treynor Ratio (HJTR). The third was the balancing act in two periods of boom and recession, which was introduced on the HBJTR model.FindingsBased on a weighted averaging of the Justified Treynor ratio of both the cycles of boom and recession, which was introduced by the HJTR term in this study, the superiority in the first grade related to the two indexes were sugar index (0.0096) and insurance index (0.0053). Finally, using the final model in this study, namely HBJTR, the overall advantage was the defensive index, i.e. the insurance index of 1.23.Originality/valueIn other words, the HBJTRi criterion consists of three steps: first, the Justified Beta (Jßi) and Justified Treynor ratio of each index using two regimes of Markov switching model were calculated for each of the cycles of boom and recession separately according to formulas 8 and 9. In the second step, the weighted average was taken from both Justified Treynor ratios of boom and recession cycles, which was called the HJTR. In the third step, to calculate the HBJTR criterion