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A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently-popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally-integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management. JEL Klassifikation: C1, G1
Earlier studies of the seigniorage inflation model have found that the high-inflation steady state is not stable under adaptive learning. We reconsider this issue and analyze the full set of solutions for the linearized model. Our main focus is on stationary hyperinflationary paths near the high-inflation steady state. The hyperinflationary paths are stable under learning if agents can utilize contemporaneous data. However, in an economy populated by a mixture of agents, some of whom only have access to lagged data, stable inflationary paths emerge only if the proportion of agents with access to contemporaneous data is sufficiently high. JEL Klassifikation: C62, D83, D84, E31
In this paper, we study the effectiveness of monetary policy in a severe recession and deflation when nominal interest rates are bounded at zero. We compare two alternative proposals for ameliorating the effect of the zero bound: an exchange-rate peg and price-level targeting. We conduct this quantitative comparison in an empirical macroeconometric model of Japan, the United States and the euro area. Furthermore, we use a stylized micro-founded two-country model to check our qualitative findings. We find that both proposals succeed in generating inflationary expectations and work almost equally well under full credibility of monetary policy. However, price-level targeting may be less effective under imperfect credibility, because the announced price-level target path is not directly observable. Klassifikation: E31, E52, E58, E61
We determine optimal monetary policy under commitment in a forwardlooking New Keynesian model when nominal interest rates are bounded below by zero. The lower bound represents an occasionally binding constraint that causes the model and optimal policy to be nonlinear. A calibration to the U.S. economy suggests that policy should reduce nominal interest rates more aggressively than suggested by a model without lower bound. Rational agents anticipate the possibility of reaching the lower bound in the future and this amplifies the effects of adverse shocks well before the bound is reached. While the empirical magnitude of U.S. mark-up shocks seems too small to entail zero nominal interest rates, shocks affecting the natural real interest rate plausibly lead to a binding lower bound. Under optimal policy, however, this occurs quite infrequently and does not require targeting a positive average rate of inflation. Interestingly, the presence of binding real rate shocks alters the policy response to (non-binding) mark-up shocks. JEL Klassifikation: C63, E31, E52 .
In this article, we investigate risk return characteristics and diversification benefits when private equity is used as a portfolio component. We use a unique dataset describing 642 US-American portfolio companies with 3620 private equity investments. Information about precisely dated cash flows at the company level enables for the first time a cash flow equivalent and simultaneous investment simulation in stocks, as well as the construction of stock portfolios for benchmarking purposes. With respect to the methodology involved, we construct private equity, stock-benchmark and mixed-asset portfolios using bootstrap simulations. For the late 1990s we find a dramatic increase in the extent to which private equity outperforms stock investment. In earlier years private equity was underperforming its stock benchmarks. Within the overall class of private equity, returns on earlier private equity investment categories, like venture capital, show on average higher variations and even higher rates of failure. It is in this category in particular that high average portfolio returns are generated solely by the ability to select a few extremely well performing companies, thus compensating for lost investments. There is a high marginal diversifiable risk reduction of about 80% when the portfolio size is increased to include 15 investments. When the portfolio size is increased from 15 to 200 there are few marginal risk diversification effects on the one hand, but a large increase in managing expenditure on the other, so that an actual average portfolio size between 20 and 28 investments seems to be well balanced. We provide empirical evidence that the non-diversifiable risk that a constrained investor, who is exclusively investing in private equity, has to hold exceeds that of constrained stock investors and also the market risk. From the viewpoint of unconstrained investors with complete investment freedom, risk can be optimally reduced by constructing mixed asset portfolios. According to the various private equity subcategories analyzed, there are big differences in optimal allocations to this asset class for minimizing mixed-asset portfolio variance or maximizing performance ratios. We observe optimal portfolio weightings to be between 3% and 65%.
We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts.
Despite powerful advances in yield curve modeling in the last twenty years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach, which focuses on accurately fitting the cross section of interest rates at any given time but neglects time-series dynamics, nor the equilibrium approach, which focuses on time-series dynamics (primarily those of the instantaneous rate) but pays comparatively little attention to fitting the entire cross section at any given time and has been shown to forecast poorly. Instead, we use variations on the Nelson-Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts. JEL Code: G1, E4, C5
We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis.
We extend the important idea of range-based volatility estimation to the multivariate case. In particular, we propose a range-based covariance estimator that is motivated by financial economic considerations (the absence of arbitrage), in addition to statistical considerations. We show that, unlike other univariate and multivariate volatility estimators, the range-based estimator is highly efficient yet robust to market microstructure noise arising from bid-ask bounce and asynchronous trading. Finally, we provide an empirical example illustrating the value of the high-frequency sample path information contained in the range-based estimates in a multivariate GARCH framework.
Financial theory creates a puzzle. Some authors argue that high-risk entrepreneurs choose debt contracts instead of equity contracts since risky but high returns are of relatively more value for a loan-financed firm. On the contrary, authors who focus explicitly on start-up finance predict that entrepreneurs are the more likely to seek equity-like venture capital contracts, the more risky their projects are. Our paper makes a first step to resolve this puzzle empirically. We present microeconometric evidence on the determinants of debt and equity financing in young and innovative SMEs. We pay special attention to the role of risk for the choice of the financing method. Since risk is not directly observable we use different indicators for financial and project risk. It turns out that our data generally confirms the hypothesis that the probability that a young high-tech firm receives equity financing is an increasing function of the financial risk. With regard to the intrinsic project risk, our results are less conclusive, as some of our indicators of a risky project are found to have a negative effect on the likelihood to be financed by private equity.