1,720,979 research outputs found
A Wavelet Analysis of the Bitcoin- Hashrate Nexus Accounting for the Effects of Energy Commodities
Get PDFThis study investigates the relationship between the growth rates of Bitcoin and Bitcoin hashrate while controlling for the effect of energy commodities, specifically two-month futures on Brent crude oil, coal, and natural gas. Based on daily data from January 2013 until December 2022, we utilize the wavelet methodology to analyze dynamics both in time and frequency. Building on the previous work of Rehman and Kang (2021), this study extends the sample period and improves the replicability of their findings. Controlling for the effect of energy commodities, our analysis reveals several interesting results, highlighting the temporal and dynamic nature of these relationships. Our most significant observation that was discovered in both bi- and multivariate forms of the wavelet methodology is the low-frequency in-phase coherence between bitcoin's returns and hashrate growth rates, which persists from the beginning of 2020 until the end of our sample period in 2023, with hashrate growth rates leading bitcoin returns. These findings suggest that the link between the returns on bitcoin and hashrate growth rates while considering the impact of the energy commodities is complex and context-dependent, and further research is needed to fully understand the underlying mechanisms driving these relationships. Our study contributes to the existing literature on the Bitcoin-hashrate nexus by providing a more comprehensive analysis that accounts for the dynamic nature of these relationships, and by improving the replicability of previous research
Quantilograms: Concept and use in empirical finance
Get PDFAbstract
This paper is written to highlight a rather novel statistical methodology called Quantilogram/ Cross-Quantilogram/ Partial Cross-Quantilogram. The purpose is to make the method more accessible for someone without a master’s degree in statistics or a phd in finance, e.g. a master student of economics or someone working within finance. The method is very useful when working with financial data, since it does not require data to be normally distributed. Financial data are frequently known to not have finite fourth moments due to heavy tails. The cross-quantilogram can reveal nonlinear and/or asymmetric relationships under varying market conditions. It can detect directional predictability and tail dependency between two time series, for arbitrary lags, and model how the dependency varies over time. The method is based on a quantile hit process, where the quantilogram is the correlogram of this quantile hit process.
The paper uses quantilograms to explore two cases from empirical finance. The first case examines the cross-quantile dependence structure between Brent crude oil, S&P 500 and OSEBX, to see which of the former two has the most spillover effect on the latter. The paper reveals that both Brent and S&P 500 have spillover effects on OSEBX, with S&P 500 being the strongest influencer. S&P 500 shows positive predictability for OSEBX for most quantiles at lag 1. A partial cross-quantilogram reveals that S&P 500 has a moderating effect on the spillover effects from Brent to OSEBX, whereas Brent has negligible effect on the relationship between S&P 500 and OSEBX. In general, the effects are not very persistent.
The second case study explores the directional predictability between 3 stocks from the aerospace industry; Lockheed Martin, Intuitive Machines and Astrotech. The industry is very diverse, and this is reflected in the results from the analysis. There is a surprising lack of cross-quantile correlation between the three. We find the strongest connectedness between Lockheed Martin and Intuitive Machines, which makes sense considering that their business models have the most in common. A lack of positive correlation in the medium-to-lower quantiles for Astrotech and Intuitive Machines at lag 1 makes them good hedges for each other
The Impact of Offshore Wind Development on Electricity Prices
Get PDFThe goal of this thesis is to assess the impact implementing OWPG in the areas Utsira Nord
and Sørlige Nordsjø II would have on the mean level and volatility of historical daily dayahead electricity prices in NO2, during the time period 1st of January 2016 to 31st of
December 2019. Using simulated OWPG data for Utsira Nord and Sørlige Nordsjø II.
Motivated by the approach of Ketterer (2014), Generalized Autoregressive Conditional
Heteroskedastic (GARCH) and exponential GARCH models are employed. This study
extends current literature by including the exogenous regressors onshore WPG, OWPG,
total consumption in NO2 and total net export in NO2 in the conditional mean and
conditional variance equation. Our results suggest that an EGARCH model better captures
the complex characteristics of electricity prices better than the standard GARCH model.
The ARMAX-EGARCHX model yields that OWPG does not have a significant impact on the
conditional mean and conditional variance of historical daily day-ahead electricity prices
in NO2. Thus, the results from the fitted ARMAX-EGARCHX model suggest that OWPG fails
to contribute significantly to the merit order effect and does not significantly affect
volatility. This thesis contributes to current literature by advancing the knowledge of how
intermittent renewables impact electricity prices
The Relationship between the EU ETS and Energy Commodities under Extreme Market Conditions
Get PDFClimate change is becoming a more and more severe problem. Over many years, governments and organizations have introduced and proposed several measures to limit global warming. One of which is the Kyoto Protocol which was introduced by the UN as a measure to limit greenhouse gas emissions by introducing carbon trading. Carbon trading is a concept where those responsible for the emissions pay the price of their negative impact on climate. Fossil fuels, i.e., oil, gas, and coal are commodities that heavily influence the climate negatively. Therefore, this thesis aims to research the relationship between the returns of European Union Allowances and the returns of energy commodities under extreme market conditions. Employing quantile regression method, we study these relationships at different quantile levels. Through our research we found that there is a significant relationship between the variables, but that the relationship varies across different quantile levels. In addition, results differ when conducting the analyses with each commodity separately against carbon returns, then when all energy commodities are included. Overall, our thesis findings contribute to the existing literature regarding the relationship between carbon and energy markets under extreme market conditions
Information Asymmetry and Performance of Technical Analysis During Times of Crises in Scandinavian Markets
Get PDFThis thesis investigates the probability of informed trading (PIN) during crisis periods across the Norwegian, Swedish, and Danish stock exchanges. The focus is on the financial crisis of 2008, the COVID-19 pandemic, and the Russia-Ukraine war. Utilizing the framework of Hung and Lai (2022), this study examines the effectiveness of the technical analysis indicators Moving Average Crossover (MAC) and Moving Average Convergence Divergence (MACD) conducted on the Oslo Stock Exchange. The crises are sectioned into pre-, during, and post-crisis periods, estimating PIN for each phase on the separate exchanges to identify patterns across the different markets. Our results indicate a higher probability of informed trading during the financial crises compared to the health or geopolitical crises, with various PIN values observed among the Scandinavian markets. Furthermore, we investigate the performance of technical analysis during periods of varying information asymmetry. The findings suggest that using a MAC strategy before and during a financial crisis outperforms a Buy-and-Hold (BH) strategy. However, there are no significant outperformance measures for the health and geopolitical crises. The thesis contributes to the understanding of market behavior during uncertainties in financial markets, highlighting the information asymmetry in disruptive time periods. Despite having limitations, the research paves the way for future research to refine our findings and explore further market contexts
Information Asymmetry and Performance of Technical Analysis During Times of Crises in Scandinavian Markets
Get PDFThis thesis investigates the probability of informed trading (PIN) during crisis periods across
the Norwegian, Swedish, and Danish stock exchanges. The focus is on the financial crisis
of 2008, the COVID-19 pandemic, and the Russia-Ukraine war. Utilizing the framework
of Hung and Lai (2022), this study examines the effectiveness of the technical analysis
indicators Moving Average Crossover (MAC) and Moving Average Convergence Divergence
(MACD) conducted on the Oslo Stock Exchange. The crises are sectioned into pre-, during,
and post-crisis periods, estimating PIN for each phase on the separate exchanges to identify
patterns across the different markets. Our results indicate a higher probability of informed
trading during the financial crises compared to the health or geopolitical crises, with various
PIN values observed among the Scandinavian markets. Furthermore, we investigate the
performance of technical analysis during periods of varying information asymmetry. The
findings suggest that using a MAC strategy before and during a financial crisis outperforms
a Buy-and-Hold (BH) strategy. However, there are no significant outperformance measures
for the health and geopolitical crises. The thesis contributes to the understanding of market
behavior during uncertainties in financial markets, highlighting the information asymmetry
in disruptive time periods. Despite having limitations, the research paves the way for future
research to refine our findings and explore further market contexts
The Instantaneous Impact of Geopolitical Risk on Financial Assets: Evidence from a Copula-Based Analysis of End of Day Prices Across Risk Regimes.
Get PDFRising geopolitical risk have a significant impact on the market economy and may threaten financial stability. This study is two folded and investigates the instantaneous effect of geopolitical risk on the end of day prices of various financial assets across different risk regimes, low, medium and high. To this end, we employ a bivariate copula methodology to analyse the dependence structure between daily geopolitical risk (GPR), the action-related subindex (GPA), and the threat-related sub-index (GPT). Each paired in couplings with financial assets representing the technology-, aerospace and defence-, commodities-, crude oil-, and the broader overall market. This allows us to capture the intrinsic dependence between the variables, independent of their marginal distributions. The sample period spans from August 1 st, 2007, to March 30th, 2025, with missing financial data handled using multiple imputations through Random Sample from Observed Values, carried out by the mice package in R.
Findings reveal that geopolitical risk has asymmetric effects. The adjusted closing prices of the equity benchmarks display negative intrinsic dependence with GPR and GPA on a day characterised by low risk. This shifts toward stronger positive dependence as geopolitical risk intensifies. Across the full distribution, the geopolitical threats exert the most pronounced influence on the end of day prices of the equity benchmarks. Tail dependence is generally absent across equity benchmarks, except during high-risk regimes. During high-risk regimes the end of day prices of S&P GSCI and crude oil exhibits strong upper tail dependence, particularly in response to threats concerning future geopolitical events. Conversely, in the low- and mediumrisk regimes these couplings frequently fit the Independence copula. The Bloomberg Commodity Index displays a distinct pattern, often characterised by independence across regimes. However, it demonstrates a negative intrinsic dependence during the high-risk regime, particularly in the context of realised or escalated geopolitical events
A Hybrid Model for Analyzing the Effect of the Carbon Border Adjustment Mechanism on the Historical Volatility of the EU ETS
Get PDFThe 1st of October 2023 the Carbon Boarder Adjustment Mechanism (CBAM) transitional phase began. The CBAM is a tool created by the European Union (EU) to reach the target of 55% reduction in greenhouse gas emission levels in 2030 compared with 1990 levels. The CBAM imposes customs on sectors at high risk of carbon leakage. This thesis aims to analyze the historical volatility of the EU Emissions Trading System (ETS) for the end of phase 3 and for phase 4 until the 29th of February 2024. The data consists of the price of indexes related to the CBAM and the price of natural gas. Motivated by a methodology created by Amirshahi and Lahmiri (2023), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), exponential GARCH, GlostenJagannathan-Runkle-GARCH models are created based on different assumptions for the distribution of the residuals. In addition, a model is created using Long Short-Term Memory (LSTM). The optimal GARCH-type model for each variable is used to create a hybrid GARCH-LSTM model. Model performance is compared based on the root mean squared error (RMSE) and the mean absolute error (MAE). The results show that the GARCH-LSTM model outperforms the alternatives in terms of RMSE and MAE. In addition, the model shows that the predictability increases in phase 4. The thesis provides EU with a tool to determine the right policies to ensure a carbon price that does not undermine the main goals of the CBAM
Predicting Bitcoin Returns Using Artificial Neural Networks - An Application of Large Datasets to Convolutional Neural Networks and Long Short-Term Memory Based Artificial Neural Networks in Finance.
Get PDFTime series forecasting is one of the foremost challenges studied in finance. In this thesis various Convolutional Neural Network and Long Short Term Memory Artificial Neural Network models are used to predict Bitcoin returns. Previous literature has explored using data from Sentiment analysis of Social Media, and Blockchain information in isolation. This thesis seeks to combine the predictive power of earlier smaller models into a larger model that better utilizes a broader category of features in time series prediction. The resulting models are able to predict Bitcoin returns well, beating out simpler methods that do not utilize Artificial Neural Networks
The Impact of Offshore Wind Development on Electricity Prices
Get PDFThe goal of this thesis is to assess the impact implementing OWPG in the areas Utsira Nord and Sørlige Nordsjø II would have on the mean level and volatility of historical daily day-ahead electricity prices in NO2, during the time period 1st of January 2016 to 31st of December 2019. Using simulated OWPG data for Utsira Nord and Sørlige Nordsjø II. Motivated by the approach of Ketterer (2014), Generalized Autoregressive Conditional Heteroskedastic (GARCH) and exponential GARCH models are employed. This study extends current literature by including the exogenous regressors onshore WPG, OWPG, total consumption in NO2 and total net export in NO2 in the conditional mean and conditional variance equation. Our results suggest that an EGARCH model better captures the complex characteristics of electricity prices better than the standard GARCH model. The ARMAX-EGARCHX model yields that OWPG does not have a significant impact on the conditional mean and conditional variance of historical daily day-ahead electricity prices in NO2. Thus, the results from the fitted ARMAX-EGARCHX model suggest that OWPG fails to contribute significantly to the merit order effect and does not significantly affect volatility. This thesis contributes to current literature by advancing the knowledge of how intermittent renewables impact electricity prices
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