Research on systemic risk contagion of Chinese financial institutions based on GARCH-VMD-Copula-CoVaR model

With the development of China’s financial market, the risk contagion effect among financial institutions is increasing and becoming more complicated. Few literatures have explored the risk transmission paths of Chinese financial institutions at different frequencies. In order to make up for the gaps in this research field, variable mode decomposition (VMD) technology is introduced in this paper, combined with the Copula-GARCH model to construct the GARCH-VMD-Copula-CoVaR model, which describes the risk contagion paths of major financial institutions in the Chinese financial market at different frequencies (long-term, medium-term and short-term). The research results show that risk dependence and contagion between financial institutions have the characteristics of bidirectionality, asymmetry and time-varying in all frequency studies, and there are differences in different frequencies

Unveiling commodities-financial markets intersections from a bibliometric perspective

The prominence of commodity markets within the domains of empirical finance and energy economics is well established, largely due to oil's importance and its relationship with other commodities and financial markets. In this study, we present a bibliometric examination of 437 journal articles addressing the phenomenon of commodity connectedness, spanning the period from 1994 to 2022. The research methods include a blend of qualitative and quantitative approaches, incorporating bibliometrics and content analysis. Based on the findings of the analysis, four primary research streams have been identified within the literature concerning commodity connectedness, namely (1) commodity interconnectivity, (2) the relationship between traditional commodities, renewable energy, and cryptocurrencies, (3) the relationship between oil and stock markets, and (4) studies utilizing copula methods to examine the interconnectivity between oil and financial markets. We proposed 15 future research questions for further investigation in the domain of commodity connectedness, including topics such as the impact of the post-COVID era and global uncertainties on commodity markets, how commodities can address the issue of climate change, the exponential growth of cryptocurrencies as a new financial asset, and the impact of the ongoing Russia-Ukraine conflict on commodity and financial markets

Optimal Asset Allocation Under Linear Loss Aversion

Growing experimental evidence suggests that loss aversion plays an important role in asset allocation decisions. We study the asset allocation of a linear loss-averse (LA) investor and compare the optimal LA portfolio to the more traditional optimal mean-variance (MV) and conditional value-at-risk (CVaR) portfolios. First we derive conditions under which the LA problem is equivalent to the MV and CVaR problems. Then we analytically solve the twoasset problem, where one asset is risk-free, assuming binomial or normal asset returns. In addition we run simulation experiments to study LA investment under more realistic assumptions. In particular, we investigate the impact of different dependence structures, which can be of symmetric (Gaussian copula) or asymmetric (Clayton copula) type. Finally, using 13 EU and US assets, we implement the trading strategy of an LA investor assuming assets are reallocated on a monthly basis and find that LA portfolios clearly outperform MV and CVaR portfolios.LOss aversion, portfolio optimization, MV and CVaR portfolios, copula, investment strategy

Factors behind the performance of green bond markets

The market for green bonds has grown dramatically over the past several years, necessitating an understanding of the variables that might forecast its performance. Studies on how the green bond market interacts with other markets are widely discussed in the literature, but little is known about the variables that improve predictions of green bond returns. In this study, we use data on commodity and financial asset prices, as well as speculative factors, to predict the returns on green bonds using the Feasible Quasi-Generalized Least Squares (FQGLS) and the causality-in-quantiles estimators. The findings demonstrate that most factors are significant predictors of the returns on green bonds, with speculative factors having a detrimental predictive influence, and commodity and financial asset prices having a mixed predictive impact. When asymmetries are taken into account, the asymmetric predictive model performs better at predicting the returns on green bonds than its symmetric counterpart in most instances. Finally, all the factors, except investors' sentiment, affect the returns on green bonds in a variety of market situations. The interdependence among the global financial and commodity markets, as well as economic uncertainties justify the established predictive influence, since green bonds are a component of the broader investment bonds

Two Essays on Investor Emotions and Their Effects in Financial Markets

This dissertation provides empirical evidences on media-based investor emotions in predicting stock return, conditional volatility, and stock and bond return comovements. We first studied the interaction between US media content and the US stock market returns and volatility. We utilize propriety investor sentiment measures developed by Thompson Reuters MarketPsych. We select four measures of investor sentiment that reflect both pessimism and optimism of small investors. Our objective is two-fold. First, we examine the ability of these sentiment measures to predict market returns. For this purpose, we use dynamic Vector Auto-Regressive models. Second, we are interested in exploring the effects of these sentiment measures on the market returns and volatility. For this purpose, we utilize a Threshold-GARCH model. Next, we investigated the effect of investor emotions (fear, gloom, joy and optimism) in financial futures markets by using Thompson Reuters MarketPsych Indices. The purpose of this study is three fold. First, we investigate the extent of usefulness of informational content of our sentiment measures in predicting stock futures and treasures futures returns using daily data for different measures of emotional sentiments. Second, we investigate whether emotion sentiments affect financial futures returns and volatilities. Third, we explore the role of emotion sentiment factors in volatility transmission in financial futures markets. To the best of our knowledge, this is the first study that extensively explores the role of investors’ sentiment in the most liquid contracts (S&P 500 futures and 10-year Treasury notes) in futures markets