Abstract
The recent evolution of cryptocurrencies has been characterized by bubble-like behavior and extreme volatility. While it is difficult to assess an intrinsic value to a specific ...cryptocurrency, one can employ recently proposed bubble tests that rely on recursive applications of classical unit root tests. This paper extends this approach to the case where volatility is time varying, assuming a deterministic long-run component that may take into account a decrease of unconditional volatility when the cryptocurrency matures with a higher market dissemination. Volatility also includes a stochastic short-run component to capture volatility clustering. The wild bootstrap is shown to correctly adjust the size properties of the bubble test, which retains good power properties. In an empirical application using 11 of the largest cryptocurrencies and the CRIX index, the general evidence in favor of bubbles is confirmed, but much less pronounced than under constant volatility.
As the COVID-19 pandemic has had a profound impact on public health and global economies in 2020; it is crucial to understand how it developed and spread in time and space. This paper contributes to ...the growing literature by considering the dynamics of country-wise growth rates of infection numbers. Low-order serial correlation of growth rates is predominantly negative with cycles of two to four days for most countries. The results of fitted spatial autoregressive models suggest that there is high degree of spillover between countries. Forecast variances of many countries, in particular those with a high absolute number of infections, can to a large extent be explained by structural innovations of other countries. A better understanding of the serial and spatial dynamics of the spread of the pandemic may contribute to an improved containment and risk management.
We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation ...process, it can be viewed as a generalization of multivariate stochastic volatility models. Despite the complexity of the model, the decoupling of marginals and dependence parameters facilitates estimation. We propose estimation in two steps, where first the parameters of the marginal distributions are estimated, and then those of the copula. Parameters of the latent processes (volatilities and dependence) are estimated using efficient importance sampling. We discuss goodness-of-fit tests and ways to forecast the dependence parameter. For two bivariate stock index series, we show that the proposed model outperforms standard competing models.
The class of multivariate GARCH models is widely used to quantify and monitor volatility and correlation dynamics of financial time series. While many specifications have been proposed in the ...literature, these models are typically silent about the system inherent transmission of implied orthogonalized shocks to vector returns. In a framework of non-Gaussian independent structural shocks, this paper proposes a loss statistic, based on higher order co-moments, to discriminate in a data-driven way between alternative structural assumptions about the transmission scheme, and hence identify the structural model. Consistency of identification is shown theoretically and via a simulation study. In its structural form, a four dimensional system comprising US and Latin American stock market returns points to a substantial volatility transmission from the US to the Latin American markets. The identified structural model improves the estimation of classical measures of portfolio risk, as well as corresponding variations.
A model for dynamic independent component analysis is introduced where the dynamics are driven by the score of the pseudo likelihood with respect to the rotation angle of model innovations. While ...conditional second moments are invariant with respect to rotations, higher conditional moments are not, which may have important implications for applications. The pseudo maximum likelihood estimator of the model is shown to be consistent and asymptotically normally distributed. A simulation study reports good finite sample properties of the estimator, including the case of a misspecification of the innovation density. In an application to a bivariate exchange rate series of the Euro and the British Pound against the U.S. Dollar, it is shown that the model-implied conditional portfolio kurtosis largely aligns with narratives on financial stress as a result of the global financial crisis in 2008, the European sovereign debt crisis (2010-2013) and early rumors signalling the United Kingdom to leave the European Union (2017). These insights are consistent with a recently proposed model that associates portfolio kurtosis with a geopolitical risk factor.
Volatility impulse response functions (VIRFs) have been introduced to unravel the effects of shocks on (co-)variances for the case of classical multivariate GARCH specifications. This paper proposes ...generalized VIRFs for the case of asymmetric specifications which capture stylized features such as the leverage effect. In a bivariate application comprising a global equity index and gold prices, we show that generalized VIRFs can be used to reassess the role of gold as a safe-haven asset.
•Impulse response functions assess the impact of shocks on (co-)volatilities•Asymmetry arises when positive and negative shocks have different impacts•Asymmetric effects of empirical shocks in equity and gold volatilities•Empirical shocks to co-volatilities decay faster when allowing for asymmetry.
Efficient light sources compatible to complementary metal oxide semiconductor (CMOS) technology are key components for low-cost, compact mid-infrared gas sensing systems. In this work we present an ...on-chip narrowband thermal light source for the mid-infrared wavelength range by combining microelectromechanical system (MEMS) heaters with metamaterial perfect emitter structures. Exhibiting a resonance quality factor of 15.7 at the center wavelength of 3.96 μm and an emissivity of 0.99, the demonstrated emitter is a spectrally narrow and efficient light source. We show temperature-stable (resonance wavelength shift 0.04 nm/°C) and angular-independent emission characteristics up to angles of 50° and provide an equivalent circuit model illustrating the structure’s resonance behavior. Owing to its spectrally tailored, nondispersive emission, additional filter elements in a free-space optical gas sensing setup become obsolete. In a proof-of-concept demonstration of such a filter-free gas sensing system with CO2 concentrations in the range of 0–50000 ppm, we observe a 5-fold increase in relative sensitivity compared to the use of a conventional blackbody emitter. Our light source is fully compatible with standard CMOS processes and tunable in emission wavelength through the mid-infrared wavelength band. It paves the way for a new class of highly integrated, low-cost optical gas sensors.
One of the implications of the intertemporal capital asset pricing model is that the risk premium of the market portfolio is a linear function of its variance. Yet, estimation theory of classical ...GARCH-in-mean models with linear-in-variance risk premium requires strong assumptions and is incomplete. We show that exponential-type GARCH models such as EGARCH or Log-GARCH are more natural in dealing with linear-in-variance risk premia. For the popular and more difficult case of EGARCH-in-mean, we derive conditions for the existence of a unique stationary and ergodic solution and invertibility following a stochastic recurrence equation approach. We then show consistency and asymptotic normality of the quasi-maximum likelihood estimator under weak moment assumptions. An empirical application estimates the dynamic risk premia of a variety of stock indices using both EGARCH-M and Log-GARCH-M models.
Supplementary materials
for this article are available online.
We propose a Kronecker product model for correlation or covariance matrices in the large dimensional case. The number of parameters of the model increases logarithmically with the dimension of the ...matrix. We propose a minimum distance (MD) estimator based on a log-linear property of the model, as well as a one-step estimator, which is a one-step approximation to the quasi-maximum likelihood estimator (QMLE). We establish rates of convergence and central limit theorems (CLT) for our estimators in the large dimensional case. A specification test and tools for Kronecker product model selection and inference are provided. In a Monte Carlo study where a Kronecker product model is correctly specified, our estimators exhibit superior performance. In an empirical application to portfolio choice for S&P500 daily returns, we demonstrate that certain Kronecker product models are good approximations to the general covariance matrix.
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