Wilhelm Röpke and Richard C. Koo On Secondary Deflations and Balance Sheet Recessions

The concept of a ‘secondary deflation’ was developed in the 1930s by the German economist Wilhelm Röpke, who saw it as something different from a normal depression. While a primary deflation is a necessary reaction to the inflation from a boom period, a secondary deflation is independent and economically purposeless. Röpke argued that secondary depressions occurred in the US, Germany, France and Switzerland during the 1930s, but was vague on what made them follow primary depressions. Recently, the Taiwanese–American economist Richard C. Koo has claimed to have discovered the ‘Holy Grail of macroeconomics’, that is, what made the Great Depression so deep and long. During the Great Depression, the bursting of the asset price bubble resulted in private sectors having more debt than assets; as they shifted from maximising profits to minimising debt, the consequent debt deflation shrank the economy. According to Koo, Western economies today are suffering from a similar ‘balance sheet recession’. Strengthened by the notion of a balance sheet recession, Röpke's long‐lost insights might advance our understanding of the business cycle in general and the present crisis in the US and the Eurozone in particular.     

Climate Change and Asset Prices: Are Corporate Carbon Disclosure and Performance Priced Appropriately?

This paper empirically assesses the relevance of information on corporate climate change disclosure and performance to asset prices, and discusses whether this information is priced appropriately. Findings indicate that corporate disclosures of quantitative greenhouse gas (GHG) emissions and, to a lesser extent, carbon performance are value relevant. We use hand‐collected information on quantitative GHG emissions for 433 European companies and build portfolios based on GHG disclosure and performance. We regress portfolios on a standard four factor model extended for industry effects over the years 2005 to 2009. Results show that investors achieved abnormal risk‐adjusted returns of up to 13.05% annually by exploiting inefficiently priced positive effects of (complete) GHG emissions disclosure and good corporate climate change performance in terms of GHG efficiency. Results imply that, firstly, information costs involved in carbon disclosure and management do not present a burden on corporate financial resources. Secondly, investors should not neglect carbon disclosure and performance when making investment decisions. Thirdly, during the period analysed, financial markets were inefficient in pricing publicly available information on carbon disclosure and performance. Mandatory and standardised information on carbon performance would consequently not only increase market efficiency but result in better allocation of capital within the real economy.     

Willingness to Pay for Green Products in Air Travel: Ready for Take‐Off?

We examine the willingness to pay (WTP) for green products in air travel. Green products in aviation are supplementary services, which are sold on top of the travel service (e.g. carbon offsets, organic on‐board food). We identify a set of potential green products in aviation and report the preferences for additional airline services of 811 Swiss air travellers using an adaptive choice‐based conjoint survey. We find that 20% of those passengers who are interested in purchasing supplementary services show a considerable WTP for green products. The green segment differs from the regular segment only in terms of behavioural features, not in terms of demographic or socio‐economic characteristics. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment     

PERSONALITY TRAITS AND THE PERCEPTION OF MACROECONOMIC INDICATORS: SURVEY EVIDENCE

I examine the determinants of both perceived inflation and unemployment in one single survey and include Big Five traits in the analysis. This is the first survey on this topic in Germany. My sample consists of 1771 students from different fields and levels. Using PhD students’ estimates as a reference, I create categories for underestimation and overestimation of both variables. Multinomial logit regressions show that females overestimate both variables. Education and news consumption reduce misestimation. A higher level of Neuroticism is related with a higher probability to overestimate unemployment. Overstating (understating) one indicator is associated with overstating (understating) the other.     

The determinants of commercial banking profitability in low-, middle-, and high-income countries

Using a broad bank-level dataset and the GMM estimator technique described by Arellano and Bover (1995), this paper analyses how bank-specific characteristics, macroeconomic variables, and industry-specific factors affect the profitability of 10,165 commercial banks across 118 countries over the period from 1998 to 2012. Grouping the countries according to three income levels, we show that the determinants of bank profitability included in our model can explain existing profitability differences among commercial banks in low-, middle-, and high-income countries. The profitability determinants vary quite widely across the different levels of income in terms of significance, sign and size of the effect. The level of income has thus an important impact on the determinants of bank profitability.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Value at risk forecasting for volatility index

Forecasts of values at risk (VaRs) are made for volatility indices such as the VIX for the US S&P 500 index, the VKOSPI for the KOSPI (Korea Stock Price Index) and the OVX (oil volatility index) for crude oil funds, which is the first in the literature. In the forecasts, dominant features of the volatility indices are addressed: long memory, conditional heteroscedasticity, asymmetry and fat-tails. An out-of-sample comparison of the VaR forecasts is made in terms of violation probabilities, showing better performance of the proposed method than several competing methods which consider the features differently from ours. The proposed method is composed of heterogeneous autoregressive model for the mean, GARCH model for the volatility and skew-t distribution for the error.