Financial innovation through the creation of new markets and securities impacts related markets as well, changing their efficiency, quality (pricing error), and liquidity. The credit default swap (CDS) market was undoubtedly one of the salient new markets of the past decade. In this paper we examine whether the advent of CDS trading was beneficial to the underlying secondary market for corporate bonds. We employ econometric specifications that account for information across CDS, bond, equity, and volatility markets. We also develop a novel methodology to utilize all observations in our data set even when continuous daily trading is not evidenced, because bonds trade much less frequently than equities. Using an extensive sample of CDS and bond trades over 2002–2008, we find that the advent of CDS was largely detrimental. Bond markets became less efficient, evidenced no reduction in pricing errors, and experienced no improvement in liquidity. These findings are robust to various slices of the data set and specifications of our tests

This paper studies the time series predictability of currency carry trades, constructed by selecting currencies to be bought or sold against the US dollar, based on forward discounts. Changes in a commodity index, currency volatility and, to a lesser extent, a measure of liquidity predict in-sample the payoffs of dynamically re-balanced carry trades, as evidenced by individual and joint p-values in monthly predictive regressions at horizons up to six months. Predictability is further supported through out-of-sample metrics, and a predictability-based decision rule produces sizable improvements in the Sharpe ratios and skewness profile of carry trade payoffs. Our evidence also indicates that predictability can be traced to the long legs of the carry trades and their currency components. We test the theoretical restrictions that an asset pricing model, with average currency returns and the mimicking portfolio for the innovations in currency volatility as risk factors, imposes on the coefficients in predictive regressions

An important strand of research in macro-finance investigates which factors impede enterprise investment, and what is their aggregate economic cost. In this paper, we make two contributions to this literature. The first contribution is methodological: we introduce a novel framework to calibrate macroeconomic models with firm-level distortions using enterprise survey micro-data. The core of our innovation is to explicitly model the firms' decisions to report in the survey the distortions they face. Our second contribution is to apply our method across seven countries to characterize the distribution of these distortions and estimate the gross domestic product (GDP) loss induced by distortionary red tape. Our estimates are based on a dynamic general equilibrium model with heterogeneous firms whose capital investment decisions are distorted by red tape. We find that the aggregate cost of red tape varies widely across the countries in our dataset, with an average cost of 0.8% of annual GDP. Our framework opens up a new range of applications for enterprise surveys in macro-financial modeling and policy analysis

Can any multifactor model be interpreted as a variant of the Intertemporal CAPM (ICAPM)? The ICAPM places restrictions on time-series and cross-sectional behavior of state variables and factors. If a state variable forecasts positive (negative) changes in investment opportunities in time-series regressions, its innovation should earn a positive (negative) risk price in the cross-sectional test of the respective multifactor model. Second, the market (covariance) price of risk must be economically plausible as an estimate of the coefficient of relative risk aversion (RRA). We apply our ICAPM criteria to eight popular multifactor models and the results show that most models do not satisfy the ICAPM restrictions. Specifically, the “hedging” risk prices have the wrong sign and the estimates of RRA are not economically plausible. Overall, the Fama and French (1993) and Carhart (1997) models perform the best in consistently meeting the ICAPM restrictions. The remaining models, which represent some of the most relevant examples presented in the empirical asset pricing literature, can still empirically explain the size, value, and momentum anomalies, but they are generally inconsistent with the ICAPM

This paper revisits the association between firm value (as proxied by Tobin’s Q) and whether the firm has a staggered board. As is well known, in the cross-section firms with a staggered board tend to have a lower value. Using a comprehensive sample for 1978 – 2011, we show an opposite result in the time series: firms that adopt a staggered board increase in firm value, while de-staggering is associated with a decrease in firm value. We further show that the decision to adopt a staggered board seems endogenous, and related to an ex ante lower firm value, which helps reconciling the existing cross-sectional results to our novel time series results. To explain our new results, we explore potential incentive problems in the shareholder-manager relationship. Short-term oriented shareholders may generate myopic incentives for the firm to underinvest in risky long-term projects. In this case, a staggered board may helpfully insulate the board from opportunistic shareholder pressure. Consistent with this, we find that the adoption of a staggered board has a stronger positive association with firm value for firms where such incentive problems are likely more severe: firms with more R&D, more intangible assets, more innovative and larger and thus likely more complex firms

We advocate the use of excess returns rather than yields or log prices in analysing the risk neutral dynamics of the term structure. We show that under standard assumptions, excess returns are affine in the risk neutral innovations in the factors. This framework has several important advantages. First, it allows for an easy estimation of models that are more flexible than the AR(1). Indeed, we estimate models with more general dynamics, like ARFIMA(p, d, q), almost as easily as AR(1). Second, within our framework the dimension of the unrestricted model is the same for the AR(1) as it is for the richer models, and does not expand in line with the state vector as it does in a yield or log price framework. This makes it appropriate to test all of these risk neutral dynamic specifications against the same OLS unrestricted alternative. Our results for the US Treasury bond market show that the unrestricted model is preferred to the AR(1) by the Bayesian Information Criterion, but the opposite conclusion is reached for more flexible models. A final advantage of the excess returns framework is that the pricing errors are much lower than for the equivalent log price system