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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.

The study aims to analyze the interaction between macroeconomic uncertainty and stock market return and volatility for China and USA and tries to draw some invaluable inferences for the investors, portfolio managers and policy analysts.

Empirically the study uses GARCH family models to capture the time-varying volatility of stock market and macroeconomic risk factors by using monthly data ranging from 1995:M7 to 2018:M6. Then, these volatility series are further used in the multivariate VAR model to analyze the feedback interaction between stock market and macroeconomic risk factors for China and USA. The study also incorporates the impact of Asian financial crisis of 1997–1998 and the global financial crisis of 2007–2008 by using dummy variables in the GARCH model analysis.

The empirical results of GARCH models indicate volatility persistence in the stock markets and the macroeconomic variables of both countries. The study finds relatively weak and inconsistent unidirectional causality for China mainly running from the stock market to the macroeconomic variables; however, the volatility spillover transmission reciprocates when the impact of Asian financial crisis and Global financial crisis is incorporated. For USA, the contemporaneous relationship between stock market and macroeconomic risk factors is quite strong and bidirectional both at first and second moment level.

This study investigates the interaction between stock market and macroeconomic uncertainty for China and USA. The researchers believe that none of the prior studies has made such rigorous comparison of two of the big and diverse economies (China and USA) which are quite contrasting in terms of political, economic and social background. Therefore, this study also tries to test the presumed conception that macroeconomic uncertainty in China may have different impact on the stock market return and volatility than in USA.

This study aims to examine the impact of the stringency of COVID-19 protocols on the volatility of sectoral indices during the period 03:2020–05:2021. Specifically, this study investigates the role of economic disturbances on sectoral volatility by applying a range of conditional volatility techniques.

For this analysis, two approaches were adopted. The first approach considers COVID stringency as a factor in the conditional variance equation of sectoral indices. In contrast, the second approach considers the stringency indicator as a possible determinant of their estimated conditional volatility.

Results show that the stringency of the protocols throughout the pandemic phase led to an instantaneous spike followed by a gradual decrease in estimated volatility of all the sectoral indices except pharma and health care. Specific sectors such as bank, FMCG, consumer durables, financial services, IT, media and private banks respond to protocols expeditiously compared to other sectors.

The key contribution of this study to the existing literature is the innovative approach. The inclusion of the COVID stringency index as a regressor in the variance equation of the conditional volatility techniques was a distinctive approach for assessing the volatility dynamics with the stringency of COVID protocols. Furthermore, this study also adopts an alternative approach that estimates the conditional volatility of the indices and then tests the effect of the stringencies on estimated volatility in a regression framework.

Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news. As a result, we assume that European stock market returns are affected by endogenous and exogenous shocks. The former raise in the market itself, the latter come from the US market, because of its most influential role in the world. Under standard assumptions, the distribution of the European market index returns conditionally on the sign of the one-day lagged US return is skew-normal. The resulting model is denoted Skew-GARCH. We study the properties of this new model and illustrate its application to time-series data from three European financial markets.

Motivated by recent evidence that securitized real estate returns exhibit higher levels of predictability than stock market returns and that feedback trading (FT) can induce returns autocorrelation and market volatility, the purpose of this study is to examine the impact of FT strategies on long-term market volatility of eight international real estate markets (UK, Germany, France, Italy, Sweden, Australia, Japan and Hong Kong).

Assuming that the return autocorrelation may vary over time and the impact of positive feedback trading (PFT) or negative feedback trading (NFT) could be a function of return volatility, the authors use a combination of a FT model and a fractionally integrated Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model.

The results are mixed, revealing that both PFT and NFT strategies persist. Specifically, the authors detect PFT in the real estate markets of France, Hong Kong and Italy as opposed to the real estate markets of Australia, Germany, Japan and Sweden where NFT was present. A noteworthy exception is the UK real estate market, with important and rational FT strategies to sustain. With respect to the long-term volatility persistence, this seems to capture the mean reversion of real estate returns in the UK and Hong Kong markets. In general, the results are not consistent with those reported in previous studies because NFT dominates PFT in the majority of real estate markets under consideration.

The main contribution of this study is the investigation of the link between short-term PFT or NFT and long-term volatility in eight international real estate markets, symmetrically. Particular attention has been given to the link between short-term FT and long-term volatility, by means of a fractionally integrated GARCH approach, a symmetric one. Moreover, investigating the relationship between returns’ volatility and investors’ strategies based on FT entails significant implications because real estate assets offer a good alternative investment for many investors and speculators.

Bounce-back effect of stock market returns has been found empirically using different approaches. However, few paper explains the underlying mechanism. The paper aims to discuss these issues.

This paper fills this gap and provides an explanation for bounce-back effect in stock market.

This paper contributes to the literature in threefold. The authors contribute a formal economic model to rationalize the bounce-back effect of stock market returns. It is based on a model of stock return with volatility feedback under the assumption of Markov-Switching market volatility.

The authors use the general Markov-Switching bounce-back model, developed by Bec et al. (2015), to provide empirical evidence for the existence of bounce-back effect in stock market. The empirical result shows “W” shape of bounce-back effect, which is exactly the same as predicted by the economic theoretical model. Finally, the authors propose an alternative approach to estimate the magnitude of volatility feedback and the marginal effect on the expected return of an anticipated high variance regime.

We investigate the return and volatility spillovers from major UK banks to Financial Times Stock Exchange 100 (FTSE 100) index using Gaussian estimation and continuous time models as well as discrete time multivariate GARCH (MGARCH) modelling approaches. Using daily, weekly and monthly data over the period December 1999–December 2010, which includes the recent 2007–2009 global financial crisis, empirical estimates of uni- and/or bi-directional return and volatility spillovers are provided. The bivariate MGARCH results reveal strong return spillovers from the FTSE to the banks, and no return spillover from the latter to the FTSE. Nevertheless, strong bi-directional volatility transmission is verified. The continuous time analysis provides mixed evidence of feedback effects over the different models.

This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market.

It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market.

The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only.

This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

The main purpose of this paper is to investigate autocorrelation structure of stock and portfolio returns in a unique market setting of Saudi Arabia, where nearly all active traders are the retail individuals and the market operates under severe limits to arbitrage. Specifically, the authors examine how return autocorrelation of Saudi Arabian stock market is related to factors such as the day of the week, stock trading, performance on the preceding day and volatility.

The sample consists of the daily stock price and index data of 159 firms listed in Tadawul (Saudi Arabian Stock Exchange) for the period from January 2004 through December 2015. The methodology of Safvenblad (2000) is primarily used to investigate the autocorrelation structure of individual stock and index returns. The authors also use the Sentana and Wadhwani (1992) methodology to test for the presence of feedback traders in the Saudi stock market.

Results show that there is significantly positive autocorrelation in individual stock, size portfolio and market returns and that the last two are almost always larger than the first. Return autocorrelation is negatively related to firm size. Interestingly, return autocorrelation is positively related to trading frequency. For portfolios, autocorrelation of returns following a high absolute return day is significantly higher than that following a low absolute return day. Similarly, return autocorrelation during volatile periods is generally larger than that during tranquil periods. Return correlation between weekdays is usually larger than that between the first and last days of the week. Overall, the results suggest that the possible reason for positive autocorrelation in stock returns could be the presence of negative feedback traders who are engaged in frequent profit-taking activities.

This is the first paper that thoroughly investigates the autocorrelation structure of the returns of the Saudi stock market using both index and individual stock returns. As this US$583bn (as of August 21, 2014) market opened to foreign institutional investors in June 2015, the results of this paper should be of significant value for the potential uninformed foreign investors in this relatively lesser known and previously closed yet highly prospective market.

This paper investigates the possibility that futures markets attract noise traders who engage in positive feedback trading, an especially destabilizing form of noise trading. The hypothesis is tested using data from four major national index futures markets. The empirical evidence is consistent across all index futures markets under examination. Specifically, there is significant evidence of positive feedback trading. More importantly, the feedback trading pattern exhibits significant long memory in the sense that it depends on longer lags of past prices. Because volatility is asymmetric, the implication is that feedback trading is also asymmetric, being more prevalent during down markets so that mispricing is more likely during those periods that feedback traders are more active.