Financial assets are volatile, and volatility becomes more intense in terms of size and rate of recurrence when markets are uncertain and growing rapidly. The fact that the recurrence rate increased during crisis periods, such as the IT bubble in the early 2000 and the global financial crisis that started in 2007, is a key finding in the literature. Estimating these results requires modeling a time series that can consider volatility clustering. However, the prominent model in finance and economics estimates that the average volatility increases when uncertainty increases. This modeling process needs to consider the asymmetry that financial assets and economic outcomes, such as gross domestic product (GDP) exhibit, which tend to fall drastically in a short period and increase steadily over a long period. To model these different behaviors, one must consider the asymmetric nature of the return, for example, when a stock has extremely low or extremely high returns in a day. 

To model this behavior, I used several methods in settings that could better explain what happens during market periods when there is higher uncertainty. The general finding is that correlations are higher when returns are in the lower quantiles, called the left tails. Thus, financial assets are positively correlated, especially during periods of increased uncertainty. It is not only clustering that one would try to explain, but another issue is the prediction of one asset’s effect on another. The effect of one asset on another asset is called the spillover effect. We tried to distinguish between events that happen during the same time that affect all assets. These events are called systematic risk, and the effects that one asset has on another asset is called systemic risk. Explaining the systemic risk typically has higher priority from a policy perspective, as systemic risk can be a driver for risk transmission from one asset to another, creating a chain of risk or a spiral of risk. Hence, the approaches I used can model that chain of risk and predict risk transmission while controlling for external factors that increase uncertainty. The results of this research show the connection between energy assets and renewable energy stocks in Papers 1 and 2. For instance, we found that there is a possibility of adjusting the European carbon emission cap and that renewable energy stocks positively correlate with energy commodities in the tails. Thus, renewable energy stocks follow a macroeconomic cycle. The findings of Paper 3 show the systemic and systematic nature of cross-country spillovers between emerging and developed financial markets, and that the spillover is time-varying with increasing spillovers in crisis periods. Paper 4 examines the Nordic banking sector. The results show that banks’ spillover to their local markets is due to their systemic importance and the strength of the spillover is related to the bank’s characteristics. In the final Paper, I studied the upside and downside movement asymmetry of stocks and found that betting on upside volatility is better than a portfolio perspective but comes at the cost of increased pricing errors. The empirical findings of this thesis significantly contribute to policymakers and institutional investors in portfolio diversification and risk management. 

This paper examines the quantile dependence between energy commodities (oil, coal, and natural gas) and the real housing returns of the nine US census divisions for the period 1991-2019. In contrast to the literature on the association between oil and housing markets, we contribute by studying the effect of additional commodities on the housing market returns. We use a cross-quantilogram and quantile regression approach and find regional variation in the impact of energy commodities on housing returns. The effect within the same region varies over the quantile distributions. In general, we observe that all energy commodities are negatively associated with real housing returns. Significant correlations are found more often when the oil and housing returns are in similar quantiles. Coal and natural gas show a stronger relationship with higher quantiles of housing returns. Further, the results for coal and natural gas remains relatively stable after controlling for macroeconomic variables.

The banking sectors in the Scandinavian countries are highly concentrated, typically undercapitalised and they have suffered through several crises since the 1990s. This article analyses the systemic risk in Denmark, Norway and Sweden focusing on the co-dependence in the tails of equity returns of an individual bank and the overall banking system. We use, partly in a new way, conditional cross-quantilograms (CQs) for this purpose. We find that the CQs are positive and statistically significant in the low and high quantiles indicating that the Scandinavian banks are systemically linked. The low-quantile dependence is relatively stronger compared with the magnitude of dependence in the other quantiles. These results hold even after controlling for equity market volatility and economic policy uncertainty. We further observe that the systemic risk was insignificant from the early-2000 to the outbreak of the global financial crisis (GFC). However, after the GFC and the euro zone crisis, the systemic risk has increased substantially. Finally, we find that bank size has a positive relationship with systemic risk (low-quantile dependence) while return on asset and loan to deposit ratio exhibit a negative influence. Furthermore, these relationships are asymmetric across quantiles.

This paper is the first tofullycharacterize the relationship among cross-market Bitcoin prices to provide a complete picture ofdirectional predictabilityof Bitcoin traded in various currencies across five developed markets. To exploit full-distributional dynamics, we employ Cross-quantilogram based Correlation and Dependence model to delve deep into the estimates an asymmetric tail dependence across quantiles would reflect on heterogeneous movement pattern of Bitcoin prices. A cross-quantilogram-based analysis reveals new empirical evidence of a heterogeneous tail dependence pattern: whereas Bitcoin-USD and the Northeast Asian market (viz., Japan) depicts a strong co-movement, smaller markets display weak connectedness and strong market-efficiency.

In this study, we examine the quantile dependence between Islamic and non-Islamic equity returns using the cross-quantilogram approach. We find that Islamic and non-Islamic equity markets are predominantly independent of each other when both markets are in normal (middle quantile) and bullish (upper quantile) states. However, when the markets are in a bearish state (lower quantile), a positive dependence emerges, which becomes stronger and persistent once the uncertainty measures are controlled for and during financial crises. We also show that investors can derive diversification and hedging benefits by strategically combining Islamic and nonIslamic equities in their portfolios.

We find that 10 emerging stock markets have high risk of contagion on the regional level but lower spillover with respect to the global markets, implying a potential for diversification benefits between emerging and global markets. Regional market integration seems to have been caused by trade integration, which has a policy implication for trade agreements systemic risk effects. We find that the geopolitical risk has no impact on either the return, or volatility spillovers. However, the general stock market risk (VIX) is connected to individual market volatilities, while the oil market is largely receiving the spillovers from the other markets.

We study the cross-quantile dependence of renewable energy (RE) stock returns on aggregate stock returns, changes in oil and gold prices, and exchange rates. Applying a recently developed cross-quantilogram approach, we provide two novel findings. First, although prior studies show that RE stock returns have a positive dependence on changes in oil prices and in the aggregate stock index, we find that the relationship is not symmetric across quantiles and that this asymmetry is higher in longer lags. Second, while the extant literature provides evidence that exchange rates and gold returns exert a positive influence on aggregate stock returns, we report that this positive influence on RE stock returns is observed only during extreme market conditions. These results are robust, (i) even after controlling for economic policy and equity market uncertainties, as well as (ii) in both a time-static full sample and recursive subsamples. (C) 2019 Elsevier B.V. All rights reserved.