Exact confidence sets and goodness-of-fit methods for stable distributions

Usual inference methods for stable distributions are typically based on limit distributions. But asymptotic approximations can easily be unreliable in such cases, for standard regularity conditions may not apply or may hold only weakly. This paper proposes finite-sample tests and confidence sets for tail thickness and asymmetry parameters (α$\alpha$ and β$\beta$) of stable distributions. The confidence sets are built by inverting exact goodness-of-fit tests for hypotheses which assign specific values to these parameters. We propose extensions of the Kolmogorov–Smirnov, Shapiro–Wilk and Filliben criteria, as well as the quantile-based statistics proposed by McCulloch (1986) in order to better capture tail behavior. The suggested criteria compare empirical goodness-of-fit or quantile-based measures with their hypothesized values. Since the distributions involved are quite complex and non-standard, the relevant hypothetical measures are approximated by simulation, and p$p$-values are obtained using Monte Carlo (MC) test techniques. The properties of the proposed procedures are investigated by simulation. In contrast with conventional wisdom, we find reliable results with sample sizes as small as 25. The proposed methodology is applied to daily electricity price data in the US over the period 2001–2006. The results show clearly that heavy kurtosis and asymmetry are prevalent in these series.     

Clinical outcomes and economic impact of transcatheter mitral leaflet repair in heart failure patients

Background: Mitral regurgitation (MR) is a common valvular heart disorder requiring intervention once it becomes severe. Transcatheter mitral repair with the MitraClip device is a safe and effective therapy for selected patients denied surgery. The authors sought to evaluate the clinical outcomes and economic impact of this therapy compared to medical management in heart-failure patients with symptomatic mitral regurgitation.

Methods and results: The study was comprised of two phases; an observational study of patients with heart failure and mitral regurgitation treated with either medical therapy or the MitraClip, and an economic model. Results of the observational study were used to estimate parameters for the decision model, which estimated costs, and benefits in a hypothetical cohort of patients with heart failure and moderate-to-severe mitral regurgitation treated with either standard medical therapy or MitraClip. The cohort of patients treated with the MitraClip was propensity matched to a population of heart failure patients, and their outcomes compared. At a mean follow-up of 22 months, all-cause mortality was 21% in the MitraClip cohort and 42% in the medical management cohort (p?=?.007). The decision model demonstrated that MitraClip increased life expectancy from 1.87–3.60 years and quality-adjusted life years (QALY) from 1.13–2.76 years. The incremental cost was $52,500 Canadian dollars, corresponding to an incremental cost-effectiveness ratio (ICER) of $32,300.00 per QALY gained. Results were sensitive to the survival benefit.

Conclusion: In heart failure patients with symptomatic moderate–severe mitral regurgitation, therapy with the MitraClip is associated with superior survival and is cost-effective compared to medical therapy.     

Asset-pricing anomalies and spanning: Multivariate and multifactor tests with heavy-tailed distributions

In this paper we propose a multivariate regression based assessment of the multifactor model first developed by Fama and French (1993). We study mean-variance efficiency and spanning, as well as factor relevance. In particular, we assess the relative contribution of the factors in accounting for asset pricing anomalies. Our tests are motivated by a finite-sample distributional theory, invariant to portfolio repackaging, and achieve size control exactly conditioning on observed factors, in normal and non-normal contexts. We focus on the multivariate normal and Student-t distributions, in which case we rely on the simulation procedure proposed and applied in Beaulieu et al. (2007). We also assess, from a finite-sample and multivariate test perspective, the specification and fit of the model and error distributions considered. In its most general form, the model considered includes six factors: the market portfolio, size, the ratio of book equity to market equity as well as term structure variables (a term premium and a default premium) and momentum. Portfolio returns (coming from assets traded at NYSE, AMEX and NASDAQ) from Fama and French's data base are analyzed on monthly frequencies from 1961–2000.Our results show the following. (1) Normality in model residuals becomes more dependable as a working hypothesis, over short time spans, when the book to market equity and size factors or when the momentum factor are accounted for. (2) Allowing for heavy tailed distributions empirically accommodates some stylized asset pricing anomalies. (3) Loadings on the term structure variables and the momentum factor seem (jointly, across portfolios) statistically insignificant at usual levels in many sub-periods. (4) Mean-variance efficiency is rejected in fewer subperiods allowing for non-normal errors in multi-factor settings; the book to market equity and size factors contribute importantly in reinforcing efficiency. (5) Enlarging the set of assets [from a one factor to a six factor model] does not reinforce the mean-variance spanning hypothesis, which is globally rejected at usual levels.     

MUTUAL FUND DAILY CONDITIONAL PERFORMANCE

The empirical finance literature reveals that conditional models estimated with monthly data generally improve fund performance. Furthermore, it has been shown that using daily instead of monthly returns in an unconditional framework increases the proportion of abnormal performances relative to timing. In this article, we study conditional performance estimated with daily data in a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) framework. Our daily conditional alphas and global performances with GARCH are significantly better than those estimated with other parametrizations and they persist over time. Finally, the proportion of abnormal timing performances diminishes significantly when conditional parametrizations are used.     

Does idiosyncratic risk matter in IPO long-run performance?

Review of Quantitative Finance and Accounting - This paper studies how firm-level idiosyncratic risk varies over time and affects both initial public offering (IPO) and matched non-IPO firms’...     

Exploring the finance-real economy link in U.S.: empirical evidence from panel unit root and cointegration analysis

The aim of this article is to analyze the relationships between common shocks affecting the real economy and those underlying co-fluctuations in U.S. financial markets. In order to do this, we test for links between these common factors and also use the econometric theory of non-stationary panel data to estimate the relationships. The estimates prove the existence of significant relationships between financial and macroeconomic factors. It is also shown that there are forces pulling U.S. financial markets to move with the real economy, as seen through nearly instantaneous adjustment to a new equilibrium.     

Firm-specific risk and IPO market cycles

This article characterizes the role of risk in the initial public offering (IPO) cycle. While most of the previous literature uses the volatility of IPO initial returns to measure risk, we focus on different risk measures, namely firm-level systematic and idiosyncratic volatilities and the market-wide implied volatility index (VIX), to assess their role in the IPO cycle. Our results shed new light on (1) which risk measure is important in the determination of IPO cycles, (2) the temporal pattern of each risk component across issuing firms and (3) the relationship between market-wide uncertainty and IPO risk. Our findings reveal a lead-lag relationship between IPO waves, VIX and the IPO systematic risk measure. We also highlight the fact that market-level uncertainty predicts IPO activity and the level of idiosyncratic risk of the next-period-issuing firms. Issuing firms’ systematic risk can only be predicted by the systematic risk of firms now proceeding to their offering. The main implication resulting from our study is that one can better anticipate ‘hot-issue’ markets, as well as the specific risk components of future new issues. This will help improve upon the regulatory environment, IPO investment decisions and IPO timing given market receptivity.     

Sharing a common resource in a sustainable development context: The case of a wood innovation system

This case study of the Aquitaine wood filière emphasizes the need to integrate a stronger consideration of natural resources in the analysis of innovation systems. The analysis focuses on eight eco-innovation projects representing the Aquitaine wood filière, and carried out under the aegis of the Industries and Maritime Pine of the Future ‘competitiveness cluster.’ We show that dependence on the wood natural resource can configure the limits, objectives and expected performance of such innovation systems. While previous approaches have considered similar innovation systems from territorial, sectoral or technological perspectives, we argue that centering the analysis on this natural resource better enables consideration of the technological and environmental tensions and risks that are likely to destabilize the system.