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Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.

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.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

The purpose of this paper is to estimate the impact of changes in macro-economic conditions going forward, focusing on a change in interest policy, with regard to office letting and investment markets.

For this analysis, the authors constructed two vector-autoregressive models, measuring the response of office rents and capital values in Germany to economic impulses. The authors isolated effects of unique exogenous positive shocks (such as economic growth or interest leaps) on the basis of impulse-response functions in order to understand the complex dynamic interdependence between several economic factors and office performance changes.

The authors initially find a moderately positive development of both office performance components even although supposing an increase in interest level. In terms of capital values, the authors find that they do not drop before 1.5 years after the interest impulse and the negative effect peaks after approximately nine quarters. Furthermore, the reaction to a change in GDP is significantly lower than a reaction to the interest rate, but impulses in other macro-economic factors provoke stronger reactions. Finally, the authors find that a positive interest shock leads to a comparably robust development and economic sustainability in office rents throughout a consideration horizon of 24 quarters.

Estimations are based on observations from a time period containing two rather extraordinary market phases. As they included bubble growth and the low-interest environment, the authors find that certain patterns in both phases neutralize each other when looking at the total time frame. The authors constructed sub-samples to compensate for this. However, the research does not provide to what extent the measured impulse-responses stay forecast-proof, if the market moves into a phase of short-term normalization.

This paper provides insights into estimated impulse-response patterns on a hypothetical sudden increase of several macro-economic determinants. On this basis, the probable reaction to an increase in, for example, the interest rate level can be approximated. Also, the paper provides a fundamental understanding of the economic sustainability of German office properties in terms of their value and rent performance in the case of exogenous shocks.

This paper contains the first vector-autoregressive, impulse-response analysis of office markets in Germany in the context of several macro-economic drivers, including the interest level. It delivers insights into market reaction patterns on the basis of simulated one standard deviation shocks in all included variables.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

This paper examines the causal relationship between stock market performance and foreign exchange market in Egypt over the period 2009–2016. The study period is divided into two sub-periods: pre- and post-January 25th Egyptian revolution (ER). The reason is to examine how this revolution affects the causal relationship between the two markets' performance.

In this study, the daily basis data are used to enable good and effective observation changes in the foreign exchange rate and stock market performance over time. Stock market indexes and stock market capitalization are used as proxies for stock market performance. Further, the Egyptian pound to US$ exchange rate is used as a measure for foreign exchange market performance. The study analysis is done in stages. The first is to check the variables' stationarity for the pre- and post-revaluation. The second is to examine the cointegration among the variables. The third is to run vector autoregression (VAR) estimates, after which VAR Granger causality tests are employed.

The results show that the data are not stationary at their levels but stationary in their first difference level while there is no cointegration in the long-run among the variables in both sub-periods. Further, findings indicate that, in the pre-January 25th revolution period, there is a significant causal relationship between the foreign exchange market and stock market indexes and a significant causal relationship between market capitalization (CAP) and exchange rate at the 1% level. However, in the post-January 25th revolution period, the study does not find a significant causal relationship between foreign exchange market and stock market indexes and capitalization.

As this study focuses on the causal relationship between foreign exchange and stock markets before and after the 25th January Revolution, other macroeconomic variables such as consumer price index, interest rate and GDP were excluded for the comparison purposes with other studies. Further research is suggested to include them in the analysis to find out its effect on the performance of stock market and foreign exchange market.

The existence of long-run bidirectional causality means that portfolio managers and hedgers may have improved their understanding regarding the dynamic relationship between foreign exchange market and stock market performance as this may help them to plan and implement suitable hedging strategies to guard against currency risk in future crises or events. Investors, fund and portfolio managers and policymakers should give much attention to these event-specific interactions when they make capital budgeting decisions and implement regulation policies. Furthermore, our results may allow portfolio managers, investors and policymakers to assess the importance of informational efficiency for both markets.

This paper is an original contribution to the literature that concerns the causal relationship between stock market and foreign exchange market in the period of political instability and social unrest such as the January 25th Revolution in one of the emerging markets, namely Egypt.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

In order to develop our understanding of valuation models and so extend this to encompass the important area of performance measurement and its interpretation, it is essential to have a framework which will enable such developments to take place. This paper presents a theoretical model based on a certainty equivalent approach which enables the market risk of individual properties and portfolios to be assessed on an expectations basis. The data requirements for using the model are not onerous and with simple extensions it can be adapted to cope with changes in risk that occur when variations in the lease structure are anticipated. Understanding the influence of systematic or market risk is essential if our understanding of valuation is to improve. Systematic risk is the single most important factor which determines the premium which should be allowed to compensate for risk. This aspect has been largely ignored in the property literature with the result that risk premium figures are frequently assumed to be constant across all sectors and properties. This paper derives a model which attempts to overcome some of these problems. Due to data limitations empirical tests of the model cannot be regarded as conclusive. However, those tests that have been carried out suggest that the model could be used for estimating the required rate of return of both sectors and individual properties. It also has considerable potential in estimating growth expectations for groups of properties and can thus be used in the decision‐making process. Much, however, remains to be done.

The uncertainty that surrounds oil and gas exploration environments call for an examination at different angles. In terms of robustness, the purpose of this paper is to focus on three performance measurements: the amount of exploration investments, the growth rate of exploration investments, and the value at risk (VaR) of exploration investments.

The study utilizes the properties of discriminant analysis for deriving Z-score models that can be used for monitoring firms’ performance. A cointegration analysis is utilized as well in order to examine the level of cointegration between predictors of each performance measure. The sample includes annual data for 41 firms (local and multinational) working in the oil and gas industry in Egypt for the period 2009-2014.

The results show that amount and growth of exploration investment are quite robust performance measures in the oil and gas industry; VaR of exploration investment is sporadic as it firm-specific; and GDP, capital expenditure and operating expenditure are quite relevant for managing and monitoring growth of exploration investments.

The study offers robust evidence that amount and growth of exploration investment are quiet relevant for measuring firm performance in the oil and gas industry.

This chapter investigates the presence of a difference in the systemic risk level between Islamic and conventional banks in Bangladesh. The authors compare systemic resilience of three types of banks: fully fledged Islamic banks, purely conventional banks (CB), and CB with Islamic windows. The authors use the market-based systemic risk measures of marginal expected shortfall and systemic risk to identify which type is more vulnerable to a systemic event. The authors also use ΔCoVaR to identify which type contributes more to a systemic event. Using a sample of observations on 27 publicly traded banks operating over the 2005–2014 period, the authors find that CB is the least resilient sector to a systemic event, and is the one that has the highest contribution to systemic risk during crisis times.