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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.

We investigate the link between firm volatility and risk-taking (RT) among 4232 institutions across 11 countries during the period of 2000–2017 and find RT is negatively correlated with volatility measures. Second, a decomposition of the primary risk measure, the Z score and Merton distance-to-default, reveals that high RT contributed to lower stock return volatility mainly through better corporate governance, firm size, higher information efficiency, and strong BOD. Third, Australia firms engage in more RT compared to other countries. Finally, majority of the selected countries show the negative impact of RT in firm volatility in the pre-crises period (2002–2006) and during the crises period (2007–2009) but not in the post-crises period (2010–2014).

Purpose: This work provides an empirical analysis of investor behaviour's simultaneous influence due to the surprise effect caused by COVID-19 cases and government responses to market risk. This analysis compares tourism assets risk with other sectors and different types of investors' assets and categories in Europe.

Design: The paper applies an ARIMA with a GARCH model to predict conditional volatility of models for market uncertainty. Nonlinear models, factor analysis and time series linear regression for stationary variables in first differences are applied to predict market uncertainty.

Findings: We demonstrate that market risk does not arise from COVID-19 cases but instead from the surprise effect, as the market accurately predicts future cases. Only the volatility of the sectors Travel, Airline, and Utility are influenced by both surprise effect and government response, but only the travel sector reveals an interaction effect with both government response effort and surprise effect.

Originality: The article mutually studies the simultaneous interactions among investor behaviour due to the surprised effect caused by COVID-19 and government responses to the pandemic and the influence on professional investors' volatility in two asset types and between different sectors.

Practical implications: With this model and results, investors and financial service providers may verify whether or not government intervention during pandemic periods is effective in reducing uncertainty and risk levels on sectors, types of investors and different sorts of assets.

This chapter investigates the determinants of the volatility of spread in the over-the-counter foreign exchange market and examines whether the relationships differ in the crisis periods. We compute the measures for the volatility of liquidity by using bid-ask spread data sampled at a high frequency of five minutes. By examining 11 currencies over a 13-year sample period, we utilize a balanced dynamic panel regression to investigate whether the risk associated with the currencies quoted or trading activity affects the variability of liquidity provision in the FX market and examine whether the crisis periods have any effect. We find that both the level of spread and volatility of spread increases during the crisis periods for the currencies of emerging countries. In addition, we find increases in risks associated with the currencies proxied by realized volatility during the crisis periods. We also show risks associated with the currency are the major determinants of the variability of liquidity and that these relationships strengthen during periods of uncertainty. First, we develop measures to capture the variability of liquidity. Our measures to capture the variability of liquidity are non-parametric and model-free variable. Second, we contribute to the debate of whether variability of liquidity is adverse to market participants by examining what drives the variability of liquidity. Finally, we analyze seven crisis periods, allowing us to document the effect of the crises on determinants of variability of liquidity over time.

This study aimed to know the effect of cross-sectional risk, which comprises business-specific risk and stock market volatility, as a variable for estimating macroeconomic risk in Indonesia. This study observes public companies in Indonesia and Indonesian macroeconomic data from 2004 to 2020. In this study, the author uses term spread as the dependent variable that reflects macroeconomic risk. The cross-sectional risk comprises financial friction (FF), cash flow (CF), debt–service ratio, and stock market volatility as independent variables. By using the Autoregressive Distributed Lag (ARDL) Model method, this study shows that business-specific and stock market risk can estimate macroeconomic risk, so that it becomes an early signal of economic shock, such as recession or high inflation, in the future. The model in this study also examines the cross-sectional risk relationship with other macroeconomic indicators, such as the Consumer Confidence Index (CCI), money supply (M0), and Indonesia’s trade balance (TB).

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.

In this chapter, we investigate the effect of long memory in volatility on the accuracy of emerging stock markets risk estimation during the period of the recent global financial crisis. For this purpose, we use a short (GJR-GARCH) and long (FIAPARCH) memory volatility models to compute in-sample and out-of-sample one-day-ahead VaR. Using six emerging stock markets index, we show that taking into account the long memory property in volatility modelling generally provides a more accurate VaR estimation and prediction. Therefore, conservative risk managers may adopt long memory models using GARCH-type models to assess the emerging market risks, especially when incorporating crisis periods.