The determinants of demand for tourism in Turkey: Does terror-threat matter? A Markov Regime Switching-VAR approach

This paper investigates the impacts of potential determinants of demand for tourism in Turkey through Markov Regime Switching-Vector Auto Regression (MS-VAR) estimations from 1999 to 2017 on monthly data. The determinants are income level, exchange rates and the threat of terror incidences. The terror variable, following the Global Terrorism Index (GTI) 2017 report, is calculated for Turkey by the author. This research has conducted two separate MS-VAR models to observe the relevant parameters’ signs of the demand for tourism function. Both MS-VAR models revealed that income level and exchange rates have positive influences on tourism while the terror threat has a negative impact on tourism in Turkey. Terror adversely affects the demand for tourism in the short-term in which terror has occurred in the nearest past (i.e., a month ago). The MS-VAR models also yield that a similar negative impact of terror on tourism activities does not appear over the longer periods.

A Comparative Performance Analysis of Sustainability Themed Indices in India: Markov Regime Switching Approach

Sustainability Indices serve as a benchmark for the companies screened for their superior performance on environmental, social and governance (ESG) parameters. This article intends to compare the overall and regime-specific financial performance of socially responsible indices of the National Stock Exchange, Nifty100 ESG and Nifty100 ESG Enhanced with Nifty50 (representing the market) from 1 April 2012 to 31 March 2020. Overall comparative performance analysis of these indices is conducted using risk-adjusted return measures and volatility has been captured through the TGARCH model. Further, time duration has been decomposed into regimes using Markov Regime Switching Model and the comparison of indices has been undertaken in both regimes. Our findings suggest that there is no significant difference between the return performance of sustainability indices and market benchmark index in single time duration and sustainability indices performing marginally better in both the regimes identified. This implies that socially responsible investments in India are providing reasonable returns to investors without comprising non-financial objectives. For corporates, it is a win–win situation to focus on ESG parameters to attract capital from investors and deliver better corporate financial performance and hence increasing the potential of growth of socially responsible investing in India.

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.