Systematic risk in the biopharmaceutical sector: a multiscale approach

It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models.

Dose Correlation of Panax ginseng and Atractylodes macrocephala Koidz. Drug Pairs in the Chinese Medicine Prescription Based on the Copula Function

Objective. Panax ginseng and Atractylodes macrocephala Koidz. (AMK) are widely used in treating various diseases; however, research is insufficient on measuring the relationship that exists by combining this drug pair using the copula function. Methods. In this study, 279 traditional Chinese medicine prescriptions containing the Panax ginseng and AMK drug pair were extracted from the prescription database for three types of screened indications, namely, diabetes mellitus, diarrhea, and insomnia. Following the principle of dose conversion, each dynasty unit was uniformly converted into a modern unit. Then, the kernel density distribution of Panax ginseng and AMK was fitted with their empirical distribution functions. Finally, the optimal copula function was selected from the copula function family as a t-copula function. Results. The empirical distribution and probability density functions of Panax ginseng and AMK were obtained. From the results, their Kendall rank correlation coefficients with indications of diabetes mellitus, insomnia, and diarrhea were 0.8689, 0.7858, and 0.7403, whereas their Spearman rank correlation coefficients were 0.9563, 0.9276, and 0.8958. Results also indicated that the use of the t-copula function can better reflect the correlation between Panax ginseng and AMK doses. Conclusion. From the three indications, the dose between Panax ginseng and AMK was positively correlated. This study, therefore, confirms the medicinal principle of Chinese medicine “combining” from the perspective of mathematical statistics. Results from this study are practical to evaluate the correlation between the drug pair doses, Panax ginseng and AMK, using the copula function model, which provides a new model for the scientific explanation of compatibility for Chinese medicines. This study also provides a methodological basis for more drug measurement studies and clinical medications.

Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C-Vine Copula and Time-Varying t-Copula

Internet money funds (IMFs) are the most widely involved products in the Internet financial products market. This research utilized the C-vine copula model to study the risk dependence structure of IMFs and then introduces the time-varying t-copula model to analyze the risk spillover of diverse IMFs. The results show the following: (1) The risks of Internet-based IMFs, bank-based IMFs, and fund-based IMFs have obvious dependence structure, and the degree of risk dependence among different categories of IMFs is significantly different. (2) There are risk spillover effects among diverse IMFs, and their risk dependence relationship is characterized by cyclical feature. (3) The risk spillover effect among diverse IMFs is pronounced, and dynamic risk dependence between IMFs is characterized by synchronization.

Spillovers and bivariate portfolios of gold-backed cryptocurrencies and gold during the COVID-19 outbreak

Purpose This paper aims to analyze the time-varying connectedness of gold-backed cryptocurrencies and gold. This study determines the volatility spillovers in these two asset classes and the performance of bivariate portfolios based on net pairwise spillovers. Design/methodology/approach This research uses two Islamic and four conventional gold-backed cryptocurrencies and gold as variables. GJR-GARCH method under corrected DCC (cDCC) of Aielli (2013) evaluates the dynamic connectedness. Additionally, the spillovers are created using the dynamic connectedness of Diebold and Yilmaz (2012). A network-based spillover of Diebold and Yılmaz, (2014) is also made. A dynamic optimal weights strategy optimized with DCC-t-Copula determines bivariate portfolios’ performances. In general, there are 21 bivariate portfolios. Findings The outbreak of COVID-19 increases the dynamic connectedness of gold and gold-backed cryptocurrencies, which indicates a contagion effect. The results show that gold is the net volatility receiver during the COVID-19 pandemic. Moreover, a portfolio composed of gold and gold-backed cryptocurrency provides high profitability performance but zero hedge effectiveness under optimal weights strategy. Practical implications According to bivariate portfolios based on net pairwise spillovers, gold-backed cryptocurrencies' investors should not add gold to their portfolio during the pandemic because it is a net receiver of risk from the cryptocurrencies. Originality/value To the best of the author’s knowledge, this is the first paper to create bivariate portfolios composed of gold-backed cryptocurrencies and their underlying asset using DCC-t-Copula.

Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange

This paper studies how to construct and compare various optimal portfolio frameworks for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.

Multivariate Skew t-Distribution: Asymptotics for Parameter Estimators and Extension to Skew t-Copula

Symmetric elliptical distributions have been intensively used in data modeling and robustness studies. The area of applications was considerably widened after transforming elliptical distributions into the skew elliptical ones that preserve several good properties of the corresponding symmetric distributions and increase possibilities of data modeling. We consider three-parameter p-variate skew t-distribution where p-vector μ is the location parameter, Σ:p×p is the positive definite scale parameter, p-vector α is the skewness or shape parameter, and the number of degrees of freedom ν is fixed. Special attention is paid to the two-parameter distribution when μ=0 that is useful for construction of the skew t-copula. Expressions of the parameters are presented through the moments and parameter estimates are found by the method of moments. Asymptotic normality is established for the estimators of Σ and α. Convergence to the asymptotic distributions is examined in simulation experiments.

Multivariate bilateral gamma, copulas, CoSkews and CoKurtosis

Correlation graphs are introduced to delineate the levels observed in data and models for return and squared return correlations. A sample of 2048 representative pairs of equity assets is selected from a possible collection of [Formula: see text] pairs by quantization. Five copulas are estimated and simulated on these pairs of returns, the Gaussian, t-copula, Clayton, Gumbel and Frank. Additionally, the multivariate bilateral gamma (MBG) model that introduces dependence via common time changes is also fit and simulated. Results of fit statistics on returns, CoSkew and CoKurtosis pairs are reported. The general ordering of the models is MBG, t-copula, followed by the Gaussian, Frank, Gumbel and Clayton copulas.

Risk Analysis for Short-Term Operation of the Power Generation in Cascade Reservoirs Considering Multivariate Reservoir Inflow Forecast Errors

In the short-term operation of the power generation of cascade reservoirs, uncertainty factors such as inflow forecast errors could cause various types of risks. The inflow to a downstream reservoir is not only affected by inflow forecast errors from upstream reservoirs but also the forecast errors associated with inflow to the stream segment between the reservoirs, such as from a tributary. The inflow forecast errors of different forecast periods may also be correlated. To address this multivariate problem, the inflow forecast error variables were jointly fitted in this study using the Gaussian mixture model (GMM) and a t-Copula function based on the analysis of the error distribution characteristics in different forecast periods. Therefore, a stochastic model that coupled with the GMM and t-Copula to calculate inflow forecast errors in multiple forecast periods was established. Furthermore, according to the simulation results of the stochastic model and the predicted runoff series, a set of simulated runoff processes were obtained. Then they were combined with the existing power generation plan to carry out the risk analysis for short-term operation of the power generation in a cascade reservoir. The approach was evaluated using the Jinguan cascade hydropower system within the Yalong River basin as a case study. For this case study, the risk analysis for short-term operation of the power generation was analyzed based on stochastic simulation of the inflow forecast errors; the results show the feasibility and effectiveness of the proposed methods.