Asset prices are a valuable source of information about financial market participants' expectations about key macroeconomic variables. However, the presence of time-varying risk premia requires an adjustment of market prices to obtain the market's rational assessment of future price and policy developments. This paper reviews empirical approaches for recovering market-based expectations. It starts by laying out the two canonical modeling frameworks that form the backbone for estimating risk premia and highlights the proliferation of risk pricing factors that result in a wide range of different asset-price-based expectation measures. It then describes a key methodological innovation to evaluate the empirical plausibility of risk premium estimates and to identify the most accurate market-based expectation measure. The usefulness of this general approach is illustrated for price expectations in the global oil market. Then, the paper provides an overview of the body of empirical evidence for monetary policy and inflation expectations with a special emphasis on market-specific characteristics that complicate the quest for the best possible market-based expectation measure. Finally, it discusses a number of economic applications where market expectations play a key role for evaluating economic models, guiding policy analysis, and deriving shock measures.

Using daily data for 34 emerging markets in the period 1994–2016, we find robust evidence that higher export commodity prices are associated with lower sovereign default risk, as measured by lower EMBI spreads. The economic effect is especially pronounced for heavy commodity exporters. Examining the drivers, we find that, first, commodity dependence is higher for countries that export large volumes of commodities, whereas other portfolio characteristics like volatility or concentration are less important. Second, commodity-sovereign risk dependence increases in times of recessions and expansionary U.S. monetary policy. Third, the importance of raw material prices for sovereign financing can likely be mitigated if a country improves institutions and tax systems, attracts FDI inflows, invests in manufacturing, machinery and infrastructure, builds up reserve assets and opens capital and trade accounts. Fourth, the country’s government indebtedness or amount of received development assistance appear to be only of secondary importance for commodity dependence.

The term structure of equity and its cyclicality are key to understand the risks drivingequilibrium asset prices. We propose a general equilibrium model that jointly  explainsfour important features of the term structure of equity: (i) a negative unconditionalterm premium, (ii) countercyclical term premia, (iii) procyclical equity yields, and (iv)premia to value and growth claims respectively increasing and decreasing with thehorizon. The economic mechanism hinges on the interaction between heteroskedasticlong-run growth — which helps price long-term cash flows and leads to countercyclicalrisk premia — and homoskedastic short-term shocks in the presence of limited marketparticipation — which produce sizeable risk premia to short-term cash flows. The slopedynamics hold irrespective of the sign of its unconditional average. We provide empirical support to our model assumptions and predictions.

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

Using daily data for 34 emerging markets in the period 1994-2016, we find robust evidence that higher export commodity prices are associated with higher sovereign bond returns (indicating lower sovereign risk). The economic effect is especially pronounced for heavy commodity exporters. Examining the drivers, we find, first, that commodity-dependence is higher for countries that export large volumes of volatile commodities and that the effect increases in times of recessions, high inflation, and expansionary U.S. monetary policy. Second, the importance of raw material prices for sovereign financing can likely be mitigated if a country improves institutions and tax systems, attracts FDI inflows, invests in manufacturing, machinery and infrastructure, builds up reserve assets and opens capital and trade accounts. Third, the concentration of commodities within a country’s portfolio, its government indebtedness or amount of received development assistance appear to be only of secondary importance for commodity-dependence.

Previous studies report a robust positive relationship between cash endowments and asset prices in experimental asset markets. Higher cash endowments generally increase the proportion of riskless versus risky wealth at the individual and aggregate level as well as the capacity of market participants to seize investment opportunities, i.e., their transactional liquidity. In this study, we vary the size and composition of riskless endowments in order to analyze the impact of different types of “cash” on trading behavior in experimental asset markets with randomly fluctuating fundamental values. In all treatments except the baseline, we allow subjects to control the liquidity of their cash endowment endogenously by providing some proportion of their riskless endowment in a physical store of value, which can be converted into experimental currency for trading. We observe that most subjects retain a large proportion of their wealth in the physical store of value. Inconsistent with rational choice theory, average trading prices and trading volumes are lower when “cash” is provided in a convertible store of value rather than experimental currency. Surprisingly, the price effect manifests asymmetrically on the buy side but not the sell side. Moreover, we control for potential changes in risk appetite resulting from higher riskless endowments. Our results suggest that transactional liquidity not a risky demand shift drives the relationship between cash endowments and asset prices.

Adopting an instrumental approach for stakeholder management, we focus on two primary stakeholder groups (employees and creditors) to investigate the relationship between employee treatment and loan contracts with banks. We find strong evidence that fair employee treatment reduces loan price and limits the use of financial covenants. In addition, we document that relationship bank lenders price both the levels and changes in the quality of employee treatment, whereas first-time bank lenders only care about the levels of fair employee treatment. Taking a contingency perspective, we find that industry competition and firm asset intangibility moderate the relationship between good human resource management and bank loan costs. The cost reduction effect of fair employee treatment is stronger for firms operating in a more competitive industry and having higher levels of intangible assets.

We develop an ethical preference-based model that reproduces the average return and volatility spread between sin and non-sin stocks. Our investors do not necessarily boycott sin companies. Rather, they are open to invest in any company while trading off dividends against ethicalness. When dividends and ethicalness are complementary goods and investors are sufficiently risk averse, the model predicts that the dividend share of sin companies exhibits a positive relation with the future return and volatility spreads. An empirical analysis supports the model’s predictions. Taken together, our results point to the importance of ethical preferences for investors’ portfolio choices and asset prices.

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

• Signs of a slowing world economy are piling up: Since the beginning of the year purchasing manager indices have been declining globally, in summer higher US interest rates led investors to withdraw capital from emerging markets, and as a consequence, capital costs rose and currencies depreciated in many emerging markets economies. In October stock prices decreased markedly worldwide including the US, despite the strong upswing in this country. • As a consequence of the turmoil on financial markets, monetary conditions in many emerging economies are no longer favorable. What ultimately counts for the prospects of the global economy, is, however, the performance of the US, the Euro area, China and Japan. The upswing in the US appears stable enough to continue well into 2019. While at present the rest of the group appears to lose momentum, there is a good chance that production in each of these economies will still expand at rates that are close to their potential growth. Further protectionist rounds are the most important risk to this scenario. • In the first half of the current year the euro area economy expanded at a markedly slower rate than in 2017, about 0.4% per quarter, but still substantial. Rising risk premia on Italian assets will probably force banks in this large country to tighten credit conditions, and a slowing world trade will dent export growth. All in all, we expect GDP growth in the euro area to go down from 2.4% in 2017 to 2.0% in 2018 and to 1.7% in 2019. • Employment continues to expand and vacancy rates are at present higher than in 2017. As a consequence, wages rise more quickly: nominal compensations per employee started accelerating early in 2017, and negotiated wages followed at the beginning of 2018. Wage inflation of slightly below 2.5% and healthy growth in employment raise real labor incomes markedly. • Our forecasts are based on the assumption that rating agencies will continue as-signing investment grade to the Italian government debt, and that the Italian government and the European Commission will find a compromise about the draft budget of the country in the coming weeks. Another assumption is that the UK will not exit the EU in an unorderly way in March 2019.