Firm-Specific Risk-Neutral Distributions with Options and CDS

We propose a method to extract the risk-neutral distribution of firm-specific stock returns using both options and credit default swaps (CDS). Options and CDS provide information about the central part and the left tail of the distribution, respectively. Taken together, but not in isolation, options and CDS span the intermediate part of the distribution, which is driven by exposure to the risk of large, but not extreme, returns. Through a series of asset-pricing tests, we show that this intermediate-return risk carries a premium, particularly at times of heightened market stress. This paper was accepted by David Simchi-Levi, finance.

Consumption, aggregate wealth and expected stock returns: a quantile cointegration approach

This paper empirically examines the long-run relationship between consumption, asset wealth and labor income (i.e., cay) in the United States through the lens of a quantile cointegration approach. The advantage of using this approach is that it allows for a nonlinear relationship between these variables depending on the level of consumption. We estimate the coefficients using a Phillips–Hansen type fully modified quantile estimator to correct for the presence of endogeneity in the cointegrating relationship. To test for the null of cointegration at each quantile, we apply a quantile CUSUM test. Results show that: (i) consumption is more sensitive to changes in labor income than to changes in asset wealth for the entire distribution of consumption, (ii) the elasticity of consumption with respect to labor income (asset wealth) is larger at the right (left) tail of the consumption distribution than at the left (right) tail, (iii) the series are cointegrated around the median, but not in the tails of the distribution of consumption, (iv) using the estimated cay obtained for the right (left) tail of the distribution of consumption improves the long-run (short-run) forecast ability on real excess stock returns over a risk-free rate.

Investigating the Links between UK House Prices and Share Prices with Copulas

We investigate the nonlinear links between the housing and stock markets in the UK using copulas. Our empirical analysis is conducted at both the national and regional levels. We also examine how closely London house prices are linked to those in other parts of the UK. We find that (i) the dependence between the different markets exhibits significant time-variation, (ii) at the national level, the relationship between house prices and the stock market is characterised by left tail dependence, i.e., they are more likely to crash, rather than boom, together, (iii) although left tail dependence with the stock market is a prominent feature of some regions, it is by no means a universally shared characteristic, (iv) the dependence between property prices in London and other parts of the UK displays widespread regional variations.

The nexus between black and digital gold: evidence from US markets

In the context of the debate on cryptocurrencies as the ‘digital gold’, this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments.

Distributional differences in the time horizon of executive compensation

The aim of executive compensation plans is to incentivize executives to maximize long-term firm value. Past research shows that executives’ pay is determined by short-term stock performance to a substantial degree. This paper tests for distributional differences in the time horizon of the performance–pay relation, controlling for executive-firm fixed effects in a quantile regression framework. I identify short-term and long-term firm and industry performance using a filter and estimate distributional differences in the short-term and long-term performance–pay relation using method of moments–quantile regression (Machado and Santos Silva in J Econ 213:145–173, 2019). I find the right tail of the conditional total compensation distribution has a more long-term-oriented performance–pay relation than the left tail. By contrast, the right tail of the conditional accumulated wealth distribution has more short-term-oriented performance–pay relation than the left tail. Results show that asymmetry in short-term firm performance–pay relations may exist, but do not vary across the conditional distribution.