Cyclicality of stock market volatility

By utilizing the significance and stochastic dominance tests, this paper formally tests the relationship between stock market volatility and the business cycle. Results show that, for most matured markets, stock market volatility is countercyclical, while for emerging markets, the volatility can be procyclical.     

Forecasting stock market volatility and information content of implied volatility index

This study investigates the incremental information content of implied volatility index relative to the GARCH family models in forecasting volatility of the three Asia-Pacific stock markets, namely India, Australia and Hong Kong. To examine the in-sample information content, the conditional variance equations of GARCH family models are augmented by incorporating implied volatility index as an explanatory variable. The return-based realized variance and the range-based realized variance constructed from 5-min data are used as proxy for latent volatility. To assess the out-of-sample forecast performance, we generate one-day-ahead rolling forecasts and employ the Mincer–Zarnowitz regression and encompassing regression. We find that the inclusion of implied volatility index in the conditional variance equation of GARCH family model reduces volatility persistence and improves model fitness. The significant and positive coefficient of implied volatility index in the augmented GARCH family models suggests that it contains relevant information in describing the volatility process. The study finds that volatility index is a biased forecast but possesses relevant information in explaining future realized volatility. The results of encompassing regression suggest that implied volatility index contains additional information relevant for forecasting stock market volatility beyond the information contained in the GARCH family model forecasts.     

Forecasting the realized volatility in the Chinese stock market: further evidence

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.     

Sign and phase asymmetry: News, economic activity and the stock market

In this paper, we present a general model of the joint data generating process underlying economic activity and stock market returns allowing for complex nonlinear feedbacks and interdependencies between the conditional means and conditional volatilities of the variables. We propose statistics that capture the long and short run responses of the system to the arrival of news, conditioning on the sign and time of arrival of the news. The model is applied to US data. We find that there are significant differences between the short and long run responses of economic activity and stock returns to the arrival of news. Moreover, for certain classifications of news, the respective responses of economic activity and stock returns vary according to the nature of the news and the phase of the business cycle at which the news arrives.     

Financial market segmentation,stock market volatility and the role of monetary policy

We study a segmented financial markets model where only the agents who trade stocks encounter financial income risk. In such an economy, the welfare-maximizing monetary policy attains the novel role of redistributing the traders' financial market risk among all agents in the economy. In order to do that, optimal monetary policy reacts to financial market movements; it is expansionary in bad times for the financial markets and contractionary in good ones. In our quantitative exercise, a dividend shock generates different policy responses and consumption paths among the optimal and the 2% inflation targeting policy. The latter implies large distributional welfare losses and risk sharing losses of similar magnitude with those generated by business cycle fluctuations. In addition, the optimal monetary policy does not minimize stock price volatility and implies lower inflation volatility than other commonly used policies.     

Non-causality: The role of the omitted variables

In this paper a proof is offered that if a variable Y₃ does not cause a variable Y₁ in the bivariate system (Y₁, Y₃) and Y₃ causes a variable Y₁ in higher-order system (Y₁, Y₂, Y₃), then the omitted variable Y₂ must cause the variable Y₁ in the bivariate system (Y₁, Y₂) and in the trivariate system (Y₁, Y₂, Y₃).     

Forecasting the realized volatility: the role of jumps

This article investigates the role of jump components dependent on the ABD-LM jump test in forecasting volatility. Our out-of-sample forecasting results show that compared with the ABD-LM jump component, its decomposition forms based on signed returns can significantly improve the models’ forecasting performance and our findings have important implications for investors and policymakers.     

The Saturday effect: an interesting anomaly in the Saudi stock market

The vast majority of empirical research on calendar anomalies has studied financial markets in countries where the Western calendar is used. This article investigates day of the week effects in Saudi Arabia’s stock market, where an Islamic calendar is used and where Saturday is the first working day of the week over the sample period considered. The Saudi stock market is the largest in the Gulf region, and we consider both total market data (the TASI index) and data for 15 sector indices. Our investigation reveals the existence of a positive Saturday effect, which contrasts with the results on first day of the week effects that are typically obtained for Western calendar markets.     

The influence of cultural distance on the volatility of the international stock market

Along with the development of cultural dimensions and cultural distance, the influence of cultural variables on the stock market is attracting more and more attention. In this study, we propose an improved gravity model to examine the relationship between culture and the volatility of the international stock market. Firstly, based on Hofstede's cultural dimensions theory, a model of the impact of cultural dimensions on the volatility of the national stock market is presented. Secondly, cultural distance is incorporated into the extended gravity model. Then, models of the impact of cultural distance on fluctuations in the international stock market and on foreign securities investment are proposed. Finally, the results of case studies using samples of national stock market indices indicate that different cultural dimensions have different influences on the volatility of national stock markets. The smaller the cultural distance between countries, the more similar the level of volatility in those countries' stock markets. Greater cultural similarity promotes increased securities investment between countries.     

The changing role of house price dynamics over the business cycle

In this paper, we attempt to analyse the relationship between house price dynamics and the business cycle. Employing a time-varying transition probability Markov switching framework, we provide empirical evidence that house price growth may prove a useful leading indicator for turning point detection. Focusing on three countries, the US, UK and Spain, we furthermore provide evidence that although potentially informative from an overall perspective in business cycle modelling, the significance of signals contained in house prices may not be symmetric across the identified high growth and low growth states. In addition, we suggest a possible range of values for house price deflation which may trigger a recession the following period.     

股票市场系统动力学分析：以上海股票市场为例

本文突破了传统经济理论研究的线性框架,视股票为一非线性系统,运用分形、混沌等复杂性理论对上海股票市场的系统动力学特征进行实证研究,得出了上海股票市场系统的分形特征、复杂性程度、系统演化类型及稳定性,最后,探讨了这些结论对股票市场的理论与实践意义。     

The anatomy of returns from moving average trading rules in the Russian stock market

This paper examines the profitability of index trading strategies that are based on dual moving average crossover (DMAC) rules in the Russian stock market over the 2003–2012 period. It contributes to the existing technical analysis (TA) literature by comparing for the first time in emerging markets the relative performance of individual stocks’ trading portfolios with that of trading strategies for the index that consists of the same stocks (i.e., the most liquid stocks of the Moscow Exchange). The results show that the best trading strategies of the in-sample period can outperform buy-and-hold strategy during the subsequent out-of-sample period, although with low statistical significance. In addition, we document the benefits of using DMAC combinations that are much longer than those employed in previous TA literature. Moreover, the decomposition of the full-sample-period performance into separate bull- and bear-period performances shows that the outperformance of the best past index trading strategies over is mostly attributable to the fact that they managed to stay mostly out of the stock market during a dramatic crash caused by the global financial crisis.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

The role of inventories and capacity utilization as shock absorbers

We examine the role of inventories and capacity utilization (of both capital and labor) for the propagation of business cycle fluctuations. We document a new set of facts regarding the U.S. cyclical regularities of inventories and capacity utilization. First, we find that capital utilization and the flows of services from both capital and labor are procyclical, and comove with the holdings of inventories. Second, we find that labor utilization is procyclical as well, but is weakly negatively correlated with inventories. We build a model that accounts for these facts, and also accounts for the stylized inventory facts, i.e., inventory holdings are procyclical, while the inventory-to-sales ratio is countercyclical. The analysis is centered on the effects of two possible shocks: preference (demand) shocks and technology shocks. Our model shows that inventories and the rate of capital utilization are mostly complements, while inventories and the rate of labor utilization are mostly substitutes. It further shows that temporary demand shocks emphasize the role of inventories as being a “shock absorber,” whereas high-persistence demand shocks, as well as technology shocks of any persistence, emphasize the role of inventories as being a complement to consumption.     

A switching ARCH (SWARCH) model of stock market volatility: some evidence from Latin America

We use the Markov regime-switching ARCH (SWARCH) model of Hamilton and Susmel (J Econometrics 64:307–333, 1994) to document the presence of high volatility regimes in six Latin American countries (Argentina, Brazil, Chile, Mexico, Peru, and Venezuela). We found four high volatility episodes, each associated to either a local (the Mexican crisis of 1994, the Brazilian crisis of 1998–1999, the Argentinean crisis of 2001–2002) or a worldwide financial crisis (the Asian financial crisis of 1997). However, we found that the effects of each financial crisis are short-lived and that between 2 and 4 months after each crisis, all markets return to low volatility regimes.

OECD pension reform: The role of demographic trends and the business cycle

Using a new real-time dataset from Beetsma et al. (2020) containing all pension reform measures in 23 OECD countries between 1970 and 2017, we demonstrate that, in contrast to what one might a priori expect, the timing of pension reform measures coincides with business cycle shocks and not with current or projected demographic shocks. The OECD-wide demography only explains the general reform trend. We rationalize this finding using a political-economy model with two-sided adjustment costs to explain a lack of response of pension reform measures to changes in demographic indicators.     

The Schumpeterian role of financial innovations in the New Economy's business cycle

Alan Greenspan claims that modern financial innovations, especiallyfinancial derivatives, were major contributors to a Schumpeterianprocess of ‘creative destruction’ which produceda high-growth ‘New Economy’ and opposes their regulation.A different perspective emerges when it is recognised that the‘New Economy’ followed the general contours of aSchumpeterian business cycle, and the role of modern financialinnovations is examined in that context. The authors argue thatthe primary role of financial derivatives has been in contributingto ‘reckless finance’ and speculative excesses inthe second phase of that cycle, and that Schumpeter would favoursubjecting the use of derivatives to more regulation.     

Revisiting the risk-return relation in the Chinese stock market: Decomposition of risk premium and volatility feedback effect

The empirical results of the risk-return relationship are mixed for both mature and merging markets. In this paper, we develop a new volatility model to revisit the risk-return relation of the aggregate stock market index by extending the Realized GARCH model of Hansen et al. (2012) with the Wang and Yang (2013) framework, in which the overall risk-return relation is decomposed into a risk premium and a volatility feedback effect. An empirical analysis of three major Chinese stock indices reveals positive risk premium and negative volatility feedback effect, and those findings are stable across different markets and sub-samples. However, their relative magnitudes differ between markets and varies through time.     

The distribution of sectoral TFP growth rates: International evidence

This paper investigates the distributional properties of TFP growth rates for countries in the G7 group. Our findings lend support to the hypothesis that multifactor productivity shocks can be plausibly fitted by a symmetric non-Gaussian stable distribution model. This leads to non-negligible implications for business cycle analysis.     

The impact of interest rate and exchange rate volatility on banks' stock returns and volatility: Evidence from Turkey

This paper investigates the effects of interest rate and foreign exchange rate changes on Turkish banks' stock returns using the OLS and GARCH estimation models. The results suggest that interest rate and exchange rate changes have a negative and significant impact on the conditional bank stock return. Also, bank stock return sensitivities are found to be stronger for market return than interest rates and exchange rates, implying that market return plays an important role in determining the dynamics of conditional return of bank stocks. The results further indicate that interest rate and exchange rate volatility are the major determinants of the conditional bank stock return volatility.