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This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

This paper is intended to enable competitive intelligence practitioners using an important method for everyday work when confronted with conditional uncertainties: the Bayes' theorem.

The paper shows how the mathematical concept of the Bayes theorem applies to competitive intelligence problems. The main approach is to illustrate the concepts by a near‐real world example. The paper also provides background for further reading, especially for psychological problems connected with Bayes' theorem.

The main finding is that conditional uncertainties represent a common problem in competitive intelligence. They should be computed explicitly rather than estimated intuitively. Otherwise, serious misinterpretations and complete project failures might follow.

In psychological literature it is a known fact that conditional uncertainties sometimes cannot be handled correctly. Conditional uncertainties seem to be handled well when they are about human properties. This should be verified or falsified in the competitive intelligence context.

In general, the application of Bayes' theorem should be seen as one of the foundations of competitive intelligence education. Especially, when it is clear in which intelligence research situations conditional uncertainties can or cannot be handled intuitively, competitive intelligence education and practice should be adapted to these findings.

CI practitioners can underestimate the value of Bayes' theorem in practice as they are often unaware of the (psychological) problems around handling conditional uncertainties intuitively. The article demonstrates how to take a computational approach to conditional uncertainties in CI projects. Thus, it can be used as part of appropriate CI training material.

We develop a theoretical model to compare forecast uncertainty estimated from time-series models to those available from survey density forecasts. The sum of the average variance of individual densities and the disagreement is shown to approximate the predictive uncertainty from well-specified time-series models when the variance of the aggregate shocks is relatively small compared to that of the idiosyncratic shocks. Due to grouping error problems and compositional heterogeneity in the panel, individual densities are used to estimate aggregate forecast uncertainty. During periods of regime change and structural break, ARCH estimates tend to diverge from survey measures.

The purpose of this paper is to investigate the risk and economic policy uncertainty (EPU) shocks on China’s equity markets while controlling for changes in sentiments and liquidity.

The GED-TARCH(1,1)-M procedure is used in estimations to deal with the heteroscedasticity problem.

Evidence shows that stock returns are positively correlated with predictable volatility and lagged downside risk. This study indicates that the stock returns are negatively correlated with both local and global uncertainty innovations. The test results are robust across different measures of stock returns and model specifications. The global EPU innovations have more profound impact on stock returns than that of Chinese EPU.

The findings are based on the data in the China’s stock market, other global markets may be considered in the future research.

Evidence indicates that a rise in EPU produces a negative effect on stock returns at the time news hits a market; however, investors will be rewarded by a premium as prices rebound in the subsequent period for compensating the investment decision made at a high uncertainty period.

The excess stock returns are negatively related to the EPU innovations, regardless of whether EPU originates from a domestic source or external sources.

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.

This study aims to examine the spillover effects of trans-Atlantic macroeconomic uncertainties on the local stock market returns in the USA and eight selected European countries, namely, Germany, France, Spain, Italy, Greece, Ireland, Sweden and the UK, during the 2000-2019 period.

This paper applies the dynamic conditional correlation multivariate GARCH model (i.e. multivariate generalized autoregressive conditional heteroskedasticity model or DCC MGARCH) to examine the potential existence of the spillover from the uncertainty of the USA to EU stock markets and vice versa. To capture different dynamic relationships between multiple time-series variables following different regimes, this paper applies the Markov switching model to the stock returns of both the USA and the eight major stock markets.

The increases in US uncertainty have significant negative impacts on all EU stock returns, whereas only the increases in the uncertainties of Spain, Ireland, Sweden and the UK have significant negative impacts on US stock returns. Notably, the economic policy uncertainty (EPU) in the USA has a dynamic effect on the European stock markets. In a bear market (State 1), the increases in the EPU of the USA and EU have significant negative impacts on EU stock returns in most cases. However, only the increase in US EPU has significant negative impacts on EU stock returns in bull markets (State 2). Reciprocally, the increases in the EU EPUs of Germany, Spain and the UK have significant impacts on US stock returns in bear market.

The observations challenge the conventional wisdom according to which only larger economies can lead the smaller counterparts. The findings also highlight the stronger dependence of the US stock market on international macroeconomic uncertainty.

The purpose of this paper is to examine the causal relationships between inflation, output growth and their uncertainties in Malaysia.

The modeling approach allows for structural breaks to avoid the masking of specific impacts.

Based on the asymmetric Generalized Autoregressive Conditional Heteroskedasticity model, the paper found strong evidence favoring a positive effect of a change in the inflation uncertainty as predicted by the Friedman-Ball hypothesis. In addition, inflation (inflation uncertainty) has direct (indirect) negative effect on the output growth. The results are consistent with the Taylor effect – increases in inflation uncertainty decreases output uncertainty. The analysis also reveals that economic uncertainty lowers the growth rate of output, complying with Bernanke's idea.

The present study suggests that extra efforts are required to locate the breaks in the variance in order to draw concrete evidence on link between economic uncertainty and macroeconomic performance.

The purpose of this paper is to analyse the impact of macroeconomic uncertainty on stock‐price volatility in Ghana.

The method of analysis is in two stages. The first stage estimates univariate volatility models for each macroeconomic variable; namely consumer price index (proxy for inflation), exchange rate, money supply, interest rates, oil price, gold price, and cocoa price using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model. In the second stage volatility effect of macroeconomic variables on stock prices is estimated using the most recent squared residuals from the mean‐conditional variance of macroeconomic variables as exogenous variables in the conditional variance equation of the stock price.

The results show that higher volatility in cocoa prices and interest rates increases volatility of the stock prices, whilst higher volatility in gold prices, oil prices, and money supply reduces volatility of stock prices.

This paper departs from previous studies on African markets, by incorporating time‐varying volatility characteristics of stock returns and further examining the effect of conditional volatility of macroeconomic variables on the volatility of stock. It also incorporates the effect of external macroeconomic uncertainties from oil and commodity price shocks.

The purpose of this paper is to explore the relationship between time orientation and strategic practices in the context of an Arab country. Toward this end, the paper studies a conditional process model that assesses the role of visioning ability and perceived uncertainty in explaining how future-oriented managers may be better at strategic management.

The study adopts a quantitative research design with closed-ended questionnaires as the main mode of data collection, and applies bootstrapping technique to test the significance and validity of the conditional process model.

The results confirm that time orientation influences strategic practices in an organization through its impact on a manager’s visioning ability, when uncertainty in the environment is perceived as low-moderate. The study also notes that local managers in the United Arab Emirates (UAE) tend to be relatively future oriented and demonstrate a greater preference toward strategic work in comparison to operational tasks.

The scope of this study has been limited to UAE nationals, and generalization of these results should be done with caution. Future research is recommended on a wider geographical area, such that cross-national results can be used to better understand the concept of time orientation in Arab countries.

Findings of this paper contribute to the literature by studying the concept of time orientation in a unique cultural domain. Moreover, by providing a theoretically relevant model for understanding the relationship between time orientation and strategic practices, the study highlights the significance of environmental uncertainty, and the importance of developing the visioning abilities of those involved in strategic roles in an organization.

The purpose of this paper is twofold. First, based on the value optimization problem of the firm, the authors proposed a theoretical model for firms’ investment decisions, which incorporates the effects of both idiosyncratic (firm specific) and macroeconomic uncertainty/risk. Second, the authors empirically estimate the proposed model for Pakistan.

The authors utilize an unbalanced firm-level panel data covering the period 1988-2013. To generate time-variant firm-specific uncertainty, the authors estimate the autoregressive model on firm sales for each firm included in the sample over the examined period. Firm-specific risk is also measured based on the square of the residuals of firms’ sales. Two measures of macroeconomic uncertainty are computed using the conditional variance obtained by estimating the ARCH model for consumer price index and industrial production index. Several alternative measures of both types of uncertainties are used to ensure the robustness of uncertainty effects. To mitigate the problem of endogeneity, the robust two-step system-generalized method of moments estimator is used to estimate the empirical model.

The results indicate that firms are likely to cut down their level of investment spending when either type of uncertainty increases. The results also reveal that the sensitivity of firms’ investment decisions to macroeconomic (aggregate) uncertainty is higher as compared to the firm-specific uncertainty. The authors show that these findings are robust to different uncertainty measures used in the analysis. The results related to firm characteristics suggest that the firm-specific variables namely the debt to assets ratio, the costs of debt to assets ratio, and the sales to assets ratio are also equally important in the determination of investment decisions of corporate manufacturing firms.

The empirical findings of the paper are useful for firm managers, investors, and government authority. Specifically, the results help firm managers and investors to understand how firm-specific and macroeconomic uncertainty affects firms’ investment decisions. The finding that firms cut their investment spending in times of macroeconomic instability implies that declines in firms’ investment spending during the periods of macroeconomic turmoil may delay the process of recovery. Therefore, the policy makers should design such policies that encourage firms to invest more in economic crisis periods, which, in turn, would enhance the growth of the economy and help to overcome the problem of downturn/recession.

The authors first propose a theoretical model for firms’ investment decisions based on the value optimization problem of the firm by incorporating the role of both firm-specific and macroeconomic uncertainty. Next, unlike most of previous studies, they estimate the proposed model for non-financial firms operating in Pakistan. The authors predict that a higher exposure to both idiosyncratic and macroeconomic uncertainties leads to lower investment in Pakistani manufacturing firms. Further, the authors hypothesize that both types of uncertainties have differential effects on firms’ investment decisions.