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.
Lehren die Universitäten die Fähigkeiten, die in der Praxis gefragt sind? UBS-Personalchef Gery Bruederlin und Finanzprofessor Thorsten Hens im Gespräch.
We study the prices that individual banks pay for liquidity (captured by borrowing rates in repos with the central bank and benchmarked by the overnight index swap) as a function of market conditions and bank characteristics. These prices depend in particular on the distribution of liquidity across banks, which is calculated over time using individual bank-level data on reserve requirements and actual holdings. Banks pay more for liquidity when positions are more imbalanced across banks, consistent with the existence of short squeezing. We also show that small banks pay more for liquidity and are more vulnerable to squeezes. Healthier banks pay less but, contrary to what one might expect, banks in formal liquidity networks do not. State guarantees reduce the price of liquidity but do not protect against squeezes.
While the comparative statics of asset demand have been studied extensively, surprisingly little work has been done on the behavior of equilibrium asset prices and returns in response to changes in the supplies of securities. This is despite considerable interest in the equity premium and interest rate puzzles. In this paper, we seek to fill this void for the classic case of a representative agent economy with a single risky asset and risk free asset in both one and two period settings. It would seem natural to suppose that in response to an increase in the supply of the risky asset, its price would fall and the gross equity risk premium would increase. We show that in standard settings where preferences are represented by frequently assumed forms of expected utility, one can obtain the opposite result. The necessary and su¢ cient condition for prices (gross equity premium) to increase (decrease) with supply is determined by the sign of the slope of the asset Engel curve. This observation allows us to derive (i) sufficient conditions directly in terms of the representative agent's risk aversion properties for general utility functions
and (ii) necessary and su¢ cient conditions for the widely used HARA (hyperbolic absolute risk
aversion) class.
In this paper we study the implications of valuing health in an otherwise standard real business cycle model. We contrast the model predictions over the business cycle with
the corresponding data counterparts. We find that health can improve the predictions of the standard real business cycle model. In particular, the benchmark model with health improves the predictions in terms of the comovements between investment and market hours relative to output. Considering health in the environment also increases
the volatility of consumption, investment and market hours while slightly reducing output volatility. In terms of health observables the benchmark model is able to account
for practically all comovement between health outcomes and health expenditures as well as with output.
We explore the pricing of variance risk by decomposing stocks' total variance into systematic and idiosyncratic return variances. While systematic variance risk exhibits a negative price of risk, common shocks to the variances of idiosyncratic returns carry a large positive risk premium. This implies investors pay for insurance against increases (declines) in systematic (idiosyncratic) variance, even though both variances comove countercyclically. Common
idiosyncratic variance risk is an important determinant for the cross-section of expected option returns. These findings reconcile several phenomena, including the pricing differences between index and stock options, the cross-sectional variation in stock option expensiveness,the volatility mispricing puzzle, and the signifcant returns earned on various option portfolio strategies. Our results are consistent with theories of financial intermediation under capital constraints.
