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We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

The purpose of this study is to investigate the relationship between gold–platinum price ratio (GP) and stock returns in international stock markets. The study addresses three empirical questions: (1) Does GP have robust predictive power in international stock markets? (2) Does GP outperform other macroeconomic variables in international stock markets? (3) What is the relationship between GP and stock market returns during economic recessions?

The study mainly uses OLS regressions to perform empirical tests for a comprehensive set of 17 advanced international stock markets and overall world market. The monthly data is used for the period January 1978 to July 2019, 499 observations for each market.

The study finds that the first-difference of GP (ΔGP), not the initial-level of GP, has strong predictive power for stock returns, both in short- and long-time horizons. The results remain robust after controlling for a number of macroeconomic predictors. The out-of-sample test results are significant, confirming the robustness of the predictive power of ΔGP.

This study is the first to examine the ability of the ΔGP to predict stock returns, and provide novel evidence on the relationship between ΔGP and international stock markets. The study draws on behavioral finance theory, specifically the myopic loss aversion, the herd effect and the limited attention theory, to explain the predictability of stock returns in international stock markets.

To test the Feldstein‐Horioka hypothesis that the investment‐to‐output ratio moves one‐for‐one with the saving‐to‐output ratio, suggesting international capital mobility.

The paper uses the econometric framework developed by Fisher and Seater, interpreting the Feldstein‐Horioka hypothesis as a long‐run phenomenon, and paying particular attention to the integration properties of the data, since meaningful tests critically depend on these properties. The paper also investigates the power of the long‐horizon regression tests, using the inverse power function of Andrews.

The paper tests the Feldstein‐Horioka hypothesis for 15 European countries, as well as for the USA and Japan, using annual data for the period from 1960 to 2002. Evidence is found against the Feldstein and Horioka hypothesis of low international capital mobility.

Although the findings are in contrast to those of Feldstein and Horioka, they are consistent with neoclassical growth theory according to which there is no reason to expect a relation between saving and investment if there are no barriers to capital movements.

It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the conventional cointegration tests may suggest lack of cointegration. This chapter studies the possibility of having cointegrated errors that are characterized simultaneously by high persistence (near-unit root behavior) and very small (near zero) variance. The proposed dual parameterization induces the cointegration error process to be stochastically bounded which prevents the variables in the cointegrating system from drifting apart over a reasonably long horizon. More specifically, this chapter develops the appropriate asymptotic theory (rate of convergence and asymptotic distribution) for the estimators in unconditional and conditional vector error correction models (VECM) when the error correction term is parameterized as a dampened near-unit root process (local-to-unity process with local-to-zero variance). The important differences in the limiting behavior of the estimators and their implications for empirical analysis are discussed. Simulation results and an empirical analysis of the forward premium regressions are also provided.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

The purpose of this paper is to provide an extensive review of the monetary model of exchange rate determination which is the main theoretical framework on analyzing exchange rate behavior over the last 40 years. Furthermore, we test the flexible price monetarist variant and the sticky price Keynesian variant of the monetary model. We conduct our analysis employing a sample of 14 advanced economies using annual data spanning the period 1880–2012.

The theoretical background of the paper relies on the monetary model to the exchange rate determination. We provide a thorough econometric analysis using a battery of unit root and cointegration testing techniques. We test the price-flexible monetarist version and the sticky-price version of the model using annual data from 1880 to 2012 for a group of industrialized countries.

We provide strong evidence of the existence of a nonlinear relationship between exchange rates and fundamentals. Therefore, we model the time-varying nature of this relationship by allowing for Markov regime switches for the exchange rate regimes. Modeling exchange rates within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. These results show that linearity is rejected in favor of an MS-VECM specification which forms statistically an adequate representation of the data. Two regimes are implied by the model; the one of the estimated regimes describes the monetary model whereas the other matches in most cases the constant coefficient model with wrong signs. Furthermore it is shown that depending on the nominal exchange rate regime in operation, the adjustment to the long run implied by the monetary model of the exchange rate determination came either from the exchange rate or from the monetary fundamentals. Moreover, based on a Regime Classification Measure, we showed that our chosen Markov-switching specification performed well in distinguishing between the two regimes for all cases. Finally, it is shown that fundamentals are not only significant within each regime but are also significant for the switches between the two regimes.

The results are of interest to practitioners and policy makers since understanding the evolution and determination of exchange rates is of crucial importance. Furthermore, our results are linked to forecasting performance of exchange rate models.

The present analysis extends previous analyses on exchange rate determination and it provides further support in favor of the monetary model as a long-run framework to understand the evolution of exchange rates.

Considering the relationship between the central bank balance sheet and unconventional monetary policy after the 2008 financial crisis, it is crucial to see how the unconventional monetary policy, given near-zero interest rates, affects future stock market performance. This paper analyzes the impact of the Fed's balance sheet size on stock market performance.

To analyze the Fed's balance sheet size's long-term stock market implications, this paper uses the asset pricing framework of market return predictability such as Ordinary least squares (OLS) and Generalized method of moments (GMM) analysis.

Findings in this paper suggest that the Fed's balance sheet size, deflated by asset market wealth, presents evidence of return predictability during 1926–2015 that is robust against standard controls. These results can be explained through the redistribution of risk and the wealth channels of monetary policy transmission. The changing balance sheet size of a central bank (1) affects systemic risk, yields and expectations and (2) signals the future direction of monetary policy and thus economic outlook.

The main implication of these findings is that policymakers should avoid a severe imbalance between a central bank's balance sheet size and assets market wealth.

The empirical evidence in this paper documents a century-old relation between the Fed's balance sheet size and US stock market return using the Fed's balance sheet data for the last 100 years and stock market returns from the Center for research in security prices (CRSP) database.

Purpose – The purpose of this chapter is to assess the role of collateralizable wealth and systemic risk in explaining future asset returns.

Methodology/approach – To test this hypothesis, the chapter uses the residuals of the trend relationship among housing wealth and labor income to predict both stock returns and government bond yields. Specifically, it shows that nonlinear deviations of housing wealth from its cointegrating relationship with labor income, hwy, forecast expected future returns.

Findings – Using data for a set of industrialized countries, the chapter finds that when the housing wealth-to-income ratio falls, investors demand a higher risk premium for stocks. As for government bond returns: (i) when they are seen as a component of asset wealth, investors react in the same manner and (ii) if, however, investors perceive the increase in government bond returns as signaling a future rise in taxes or a deterioration of public finances, then they interpret the fall in the housing wealth-to-income ratio as a fall in future bond premia. Finally, this work shows that the occurrence of crisis episodes amplifies the transmission of housing market shocks to financial markets.

Originality/value of chapter – These findings are novel. They also open new and challenging avenues for understanding the dynamics of the relationship between the housing sector, stock market and government bond developments, and the banking system.

In this paper, it is argued that previous estimates of the expected cost of equity and the expected arithmetic risk premium in the UK show a degree of upward bias. Given the importance of the risk premium in regulatory cost of capital in the UK, this has important policy implications. There are three reasons why previous estimates could be upward biased. The first two arise from the comparison of estimates of the realised returns on government bond (‘gilt’) with those of the realised and expected returns on equities. These estimates are frequently used to infer a risk premium relative to either the current yield on index‐linked gilts or an ‘adjusted’ current yield measure. This is incorrect on two counts; first, inconsistent estimates of the risk‐free rate are implied on the right hand side of the capital asset pricing model; second, they compare the realised returns from a bond that carried inflation risk with the realised and expected returns from equities that may be expected to have at least some protection from inflation risk. The third, and most important, source of bias arises from uplifts to expected returns. If markets exhibit ‘excess volatility’, or f part of the historical return arises because of revisions to expected future cash flows, then estimates of variance derived from the historical returns or the price growth must be used with great care when uplifting average expected returns to derive simple discount rates. Adjusting expected returns for the effect of such biases leads to lower expected cost of equity and risk premia than those that are typically quoted.

The purpose of this paper is to test whether investor sentiment is a significant predictor of future Mexican stock market returns. It also estimates the dynamic correlation between investor sentiment and equity returns. Finally, it examines if investor sentiment innovations impact unexpected returns for a variety of portfolios.

This study utilizes predictive regressions to determine if sentiment can predict Mexican equity returns. Multivariate GARCH models are estimated to examine the time-varying correlations between investor sentiment and equity returns.

The results show that Mexican investor sentiment is a significant predictor of Mexican equity returns for up to 24 months ahead. The findings show that high levels of sentiment today are associated with lower equity returns over the near term. Furthermore, multivariate GARCH estimations indicate that the correlation between investor sentiment and equity returns is not static and varies considerably over time. Finally, the findings indicate that sentiment innovations are significantly correlated with unexpected returns, reinforcing the notion that unexplained sentiment fluctuations lead to unexplained changes in stock market returns. Overall, these results suggest that investor sentiment is a significant source of risk for the Mexican stock market.

This study seeks to further our understanding of how behavioral factors influence and predict Mexican equity returns.