Real estate markets and the macroeconomy: A dynamic coherence framework

This paper analyzes the dynamic interactions between real estate markets, in the US and the UK and their macroeconomic environments. We apply a new approach based on a dynamic coherence function (DCF) to study these interactions bringing together different real estate markets (the securitized market, the commercial market and the residential market). The results suggest that there is a common trend that drives the different real estate markets in the UK and the US, particularly in the long run, since they have a similar shape of the DCF. We also find that, in the US, wealth and housing expenditure channels are very conductive during real estate crises. However, in the UK, only the wealth effect is significant as a transmission channel during real estate market downturns. In addition, real estate markets in the UK and the US react differently to institutional shocks. This brings some insights on the conduct of monetary policy in order to avoid disturbances in real estate markets.     

Low interest rate policy and the use of reserve requirements in emerging markets

This paper sheds light on the link between the interest rate policy in large advanced economies with international funding and reserve currencies (the United States and the euro area) and the use of reserve requirements in emerging markets. Using reserve requirement data for 28 emerging markets from 1998 to 2012, we provide evidence that emerging market central banks tend to raise reserve requirements when interest rates in international funding markets decline or financial inflows accelerate, most likely to preserve financial stability. In contrast, when global liquidity risk rises and funding from the large advanced economies dries up, emerging markets lower reserve requirements.     

Adaptive learning and distributional dynamics in an incomplete markets model

Recent research shows that several DSGE models provide a closer fit to the data under adaptive learning. This paper extends this research by introducing adaptive learning in the model of Krusell and Smith (1998) with uninsurable idiosyncratic risks and aggregate uncertainty. A first contribution of this paper establishes that the equilibrium of this framework is stable under least-squares learning. The second contribution consists of showing that bounded rationality enhances the ability of this model to match the distribution of income in the US. Learning increases significantly the Gini coefficients because of the opposite effects on consumption of the capital-rich and of the capital-poor agent. The third contribution is an empirical exercise that shows that learning can account for increases in the income Gini coefficient of up to 25% in a period of 28 years. Overall, these findings suggest that adaptive learning has important distributional repercussions in this class of models.     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

W. H. Hutt (1899–1988): Free Markets and Social Justice

W.H. Hutt reaffirmed the principles of classical economics and classical liberalism and, by applying them to urgent issues he observed in the world around him, he demonstrated the contemporary relevance of freedom and competitive institutions. A wide‐ranging critique of Keynes was founded upon a classical analysis of the labour market, while his trenchant opposition to apartheid advanced along economic and political paths. Hutt questioned the justice of the accumulation of wealth from the exploitation of monopoly power over generations. In all of these respects there are affinities between Hutt's thought and that of ‘Bleeding Heart Libertarians’.     

What drives racial segregation? New evidence using Census microdata

Using restricted Census microdata that link households to the Census block in which they live, this paper re-examines the question of whether racial differences in sociodemographic characteristics can explain observed levels of racial segregation. We develop a simple measurement framework designed to make use of the rich joint distribution of individual and neighborhood characteristics that these data provide, analyzing segregation patterns in the San Francisco Bay Area. The results indicate that racial differences in the collective set of characteristics we consider do have the potential to explain a considerable amount of the observed segregation, although more so for Asians and especially Hispanics than whites and blacks. Different sociodemographic factors emerge as potentially important for each race.     

Recent topics in Applied Financial Economics

This note discusses topics concerning recent evolutions in financial economics research. It focuses in particular on the progress of quantitative finance and applied economics in the context of the global financial crisis. To this end, I examine various important topics in Economics and Finance and discuss several empirical studies on the statistical properties of macroeconomic and financial data through the application of different econometric methodologies. I analyse their empirical findings and discuss their conclusions.     

The relationship between developed equity markets and the Pacific Basin's emerging equity markets

Using a trivariate vector autoregression (VAR) model with a proper control for heteroscedasticity, this paper investigates the relationships between the two largest equity markets in the world—the U.S. and Japan—and the four Asian emerging equity markets: Hong Kong, Korea, Singapore, and Taiwan. Evidence indicates that the links between the developed markets and the Asian emerging markets (AEMs) began to increase after the stock market crash in October 1987, and have significantly intensified since the outbreak of the Asian financial crisis in July 1997.     

Risk spillover analysis across worldwide ESG stock markets: New evidence from the frequency-domain

With the increasing global awareness of green environmental protection, the international environmental, social, and governance (ESG) stock markets are developing rapidly together with rising risk linkages across worldwide markets. Therefore, this study explores the risk spillover characteristics of international ESG stock markets in the time and frequency domains and constructs a risk linkage network to further explore the risk contagion mechanism. The results show that in most cases, the developed North American market is the core of outward risk spillover in international ESG stock markets. The entire system presents a small-world structure, and the internal regions display different risk spillover characteristics. Moreover, international ESG markets generally have strong time–frequency spillover and medium-frequency (a month to a year) spillover. In contrast, the high- (a day to a month) and low-frequency (more than one year) spillovers are located at relatively low levels, but they will rise significantly under sudden financial events. The empirical results expand the ESG stock market's theoretical framework and provide a reference for investors and market regulators to reduce the investment risk of ESG.     

Seasonality and momentum across national equity markets

This paper examines seasonality and momentum jointly across national equity markets at the index level. We find that seasonality and momentum are almost uncorrelated and appear to arise from different global or local risk factors, rather than from different loadings on the same risk factors. Employing a trading strategy that integrates seasonality and momentum parametrically, we confirm our conclusion about the relationship between seasonality and momentum: while the pure seasonality and momentum strategies individually generate sizable and significant returns, the combination strategy significantly outperforms the pure strategies in a way that is quantitatively consistent with their lack of correlation.