Contributions published at Financial Economics (Thorsten Hens)

This paper proposes a wavelet (spectral) approach to estimate the parameters of a linear regression model where the regressand and the regressors are persistent processes and contain a measurement error. We propose a wavelet filtering approach which does not require instruments and yields unbiased estimates for the intercept and the slope parameters. Our Monte Carlo results also show that the wavelet approach is particularly effective when measurement errors for the regressand and the regressor are serially correlated. With this paper, we hope to bring a fresh perspective and stimulate further theoretical research in this area.

We employ a novel approach to identify overbidding in auctions. We compare online auction prices to fixed prices for the same item on the same webpage. In detailed data on auctions of a board game, 42 percent of auctions exceed the simultaneous fixed price. The result replicates in a broad cross-section of auctions (48 percent overbidding). A small fraction of overbidders, 17 percent of bidders, suffices to generate the large fraction of auctions with overbidding. We show that the observed behavior is inconsistent with rational behavior, even allowing for uncertainty about prices and switching costs, since the expected auction price also exceeds the fixed price. Limited attention best explains our results.

When it comes to the role of theory in their research, empirical microeconomists are torn. On the one hand, we devote a large fraction of our graduate instruction to models of consumer behavior and firm decision making, and to the interactions that determine market equilibrium. On the other hand, it is not always obvious how these theories are relevant to empirical research. Outside the academy, policymakers and business leaders often demand "basic facts" and simplified policy guidance with little or no concern for theoretical nuances. How then do empirical economists negotiate between theory and "facts"? In this paper, we focus on the role of theory in the rapidly growing area of field experiments. We take an empirical approach and quantify the role of theoretical modeling in all published field experiments in five top economics journals from 1975 to 2010. We propose a new classification of experimental studies that captures the extent to which the experimental design and analysis is linked to economic theory. Specifically, we distinguish between four classes of studies: Descriptive studies that lack any explicit model; Single Model studies that test a single model-based hypothesis; Competing Models studies that test competing model-based hypotheses; and Parameter Estimation studies that estimate structural parameters in a completely specified model. Applying the same classification to laboratory experiments published over the same period we conclude that theory has played a more central role in the laboratory than in field experiments. Finally, we discuss in detail three sets of field experiments that illustrate both the potential promise and pitfalls of a tighter link between experimental design and theoretical underpinnings.

Contrary to the common wisdom that asset prices are hardly possible to forecast, we show that high and low prices of equity shares are largely predictable. We propose to model them using a simple implementation of a fractional vector autoregressive model with error correction (FVECM). This model captures two fundamental patterns of high and low prices: their cointegrating relationship and the long memory of their difference (i.e. the range), which is a measure of realized volatility. Investment strategies based on FVECM predictions of high/low US equity prices

Using 10 years of high?frequency foreign exchange data, we present evidence of time?of?day effects in foreign exchange returns through a significant tendency for currencies to depreciate during local trading hours. We confirm this pattern across a range of currencies and find that, in the case of EUR/USD, it can form a simple, profitable trading strategy. We also find that this pattern is present in order flow and suggest that both patterns relate to the tendency of market participants to be net purchasers of foreign exchange in their own trading hours. Data from alternative sources appear to corroborate that interpretation.

Die Alternative-Investment-Industrie, deren wichtigste Säule v.a. die Hedgefondsund Private-Equity-Branche bildet, erfährt eine nie zuvor erlebte Aufmerksamkeit in der deutschen Medienlandschaft - dabei werden die positiven Effekte von Hedgefonds und Private Equity zumeist nicht differenziert genug diskutiert: Sie tragen dazu bei, durch die Ausnutzung von Preisannomalien die Markteffizienz zu erhöhen. Hedgefonds- und Private Equity sollten daher eine wichtige Rolle im Anlageuniversum von Versicherungen, Pensionskassen u. a. institutionellen Investoren spielen. Sie sind der Schlüssel zu einem signifikanten Wachstum der Alternative-Investment-Industrie. Internationale Fachleute, hochrangige Vertreter aus Praxis und Wissenschaft führen in jeweils dezidierter Darstellung wesentlicher Einflussfaktoren das relevante Themenspektrum zu einem Gesamtbild der Alternativen Investments zusammen. Mit ihren Beiträgen zeichnen sie die jüngsten Entwick-lungen nach und zeigen Perspektiven der Marktentwicklung auf.

Does global currency volume increase on Federal Open Market Committee (FOMC) days? To test hypotheses of abnormal currency volume on FOMC days, a new data set from the Continuous Linked Settlement (CLS) Bank is used. The CLS measure captures more than half of the global trading volume in foreign exchange (FX) markets. The evidence shows that FX trading volume increases about 5% in the spot and the spot-next market following FOMC deliberations. The novelty of this result is that the aggregated CLS data controls for responses in various derivatives markets: a feature that existing studies based on intradaily data for specific trading platforms do not consider.

Switzerland boasts arguably the highest density of green properties in the world. In 2008, more than 15 percent of total new construction received the Swiss energy building label Minergie. The spatial distribution of these green buildings, however, is highly heterogeneous. In some regions, more than half of the new dwellings are built according to the Swiss green building standard. In others, this share is still negligible. The purpose of this paper is to identify the determinants of the distribution of green housing.

There is strong empirical evidence that the pricing kernel is U-shaped, which provides a way to explain the substantial coskewness premium. Existing studies typically use a polynomial approximation of the pricing kernel. Problematically, these polynomials have, in most cases, increasing parts by construction. Therefore, it is not clear whether the increasing parts are an artifact of the chosen functional form. Taking this concept into consideration, this paper shows that pricing kernels, as estimated by the generalized method of moments on equity data, are still U-shaped and that the increasing part is not a statistical artifact. This conclusion derives from the fact that the functional form of kernels, which allows for strictly decreasing kernels as well as for kernels with increasing parts, is still U-shaped. These results arise from checking for higher order polynomials, various time horizons, and different functional forms of the kernel.

We study the problem of maximizing expected utility from terminal wealth for a non-concave utility function and for a budget set given by one fixed pricing measure. We prove the existence of a maximizer and show that the concave envelope of the (non-concave) value function (indirect utility) is the value function of the utility maximization problem for the concave envelope of the original utility function. The value functions are shown to coincide if the underlying probability space is atomless. For a converging sequence of models, we prove that the sequence of value functions and a subsequence of optimal allocations converge to the corresponding quantities in the limit model. We illustrate our results by concrete numerical examples.

The disposition effect is the observation that investors hold winning stocks too long and sell losing stocks too early. A standard explanation of the disposition effect refers to prospect theory and in particular to the asymmetric risk aversion according to which investors are risk averse when faced with gains and risk-seeking when faced with losses. We show that for reasonable parameter values the disposition effect can however not be explained by prospect theory as proposed by Kahneman and Tversky. The reason is that those investors who sell winning stocks and hold loosing assets would in the frst place not have invested in stocks. That is to say the standard prospect theory argument is sound ex-post, assuming that the investment has taken place, but not ex-ante, requiring that the investment is made in the first place.

This paper presents a general reward-risk portfolio selection model and derives sufcient conditions for two-fund separation. In particular we show that many reward-risk models presented in the literature satisfy these conditions.

Most time series models used in econometrics and empirical finance are estimated with maximum likelihood methods, in particular when interest centers on density and Value-at-Risk (VaR) prediction. The standard maximum likelihood principle implicitly places equal weight on each of the observations in the sample, but depending on the extent to which the model and the true data generating process deviate this can be improved upon. For example, in the context of modeling financial time series, weighting schemes which place relatively more weight on observations in the recent past result in improvement of out-of-sample density forecasts, compared to the default of equal weights. Also, if instead of accurate forecasting of the entire density, interest is restricted to just downside risk, placing more weight on the negative observations in the sample improves results further. In this paper, a third and quite general strategy of shifting more weight towards certain observations of the sample is proposed. Weights are derived from external variables that convey additional information about the true DGP, like trading volume, news arrivals or even investor sentiment. As such, those observations are down weighted that bear a high probability of being destructive outliers with no bene¯t of using them when fitting the model. Considerable improvements in forecast accuracy for a variety of data sets and different time series models can be realized.

We study a number of large international military conflicts since World War II where we establish a news analysis as a proxy for the estimated likelihood that the conflict will result in a war. We find that in cases when there is a pre-war phase, an increase in the war likelihood tends to decrease stock prices, but the ultimate outbreak of a war increases them. In cases when a war starts as a surprise, the outbreak of a war decreases stock prices. We show that this paradox cannot be explained by uncertainty about investment decisions, nor by the expectation about a quick end of the war or ambiguity aversion. A connection of this puzzling phenomenon to mean-variance preferences of investors is suggested.

The paper is the first one outside the high-frequency domain to use sentiment-signed news to directly compare news and no-news stock returns. This is done by estimating whether returns on positive, neutral and negative news days are significantly different from the average daily return for a large sample of US stocks over the period from January 2003 to August 2010. The general results show that positive news days indeed have above-average returns and negative news days returns are below average, while the neutral news days are economically barely distinguishable from the average. The market also proves to be fast and accurate at pricing new information, as there are no signs of drift shortly after news days. On the contrary, a directionally correct and statistically significant movement can be found on the day before the news day. The cross-sectional analysis reveals significant differences in the strength of market reactions between stocks ranked on size, book-to-market or news coverage. The general results however hold across all subsamples and are also not driven by earnings announcements or past stock returns. Moreover, the average news sensitivity is itself a priced source of risk. A portfolio of stocks with high sensitivity to news outperforms a portfolio of stocks with low sensitivity by a statistically and economically significant 0.84% per month. This news premium seems to primarily relate to the high impact of news in situations of general uncertainty.

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