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What Variables Impact the Price's Jumping Behavior? Evidence from European Carbon Markets and Bitcoin Markets

為了解決Benz S class 2022的問題，作者何啟文 這樣論述：

The behavior of asset prices has long been a popular issue of debate in the field of financial research, as well as a significant direction in market microstructure research. When financial asset prices are influenced by a variety of causes, asset values jump, and these jumping behaviors frequently

result in changes in market structure. The prevalence of jumping behaviors adds to the uncertainty and makes it difficult to measure market structure methodologies.The occurrence of numerous events in the capital market will have varying degrees of influence on the market, resulting in anomalous ch

anges in asset values and even big price increases. The worldwide market has been influenced by several financial crises, particularly in recent years, and the carbon emission market and digital currency market, both of which are emerging markets, are more prone to anomalous swings and jumps.Press (

1976) set asset price changes as discrete events and set the intensity of jumps as a constant distribution, and the number of jumps was subject to a fixed constant Poisson distribution of the complex event model, and then cox and Ross (1976) and Merton (1976) introduced the jump process to study the

phenomenon of jumps in capital markets.Ball and Torous (1983) address the jump behavior of stock prices in terms of the jump-diffusion process and the hypothetical unit size of the leap. Akgiray and Booth (1983) extended the jump-diffusion model by developing a hybrid GARCH model of jumps, in which

the GARCH process discusses the normal fluctuation of asset prices and the jump process discusses the abnormal fluctuation of asset prices, which can effectively describe the market's price fluctuation behavior.Consequently, the hybrid GARCH jump model still does not reflect the jump behavior of th

e real market. Pan (1997) suggested a jump GARCH model with a binomial tree structure utilizing an ARCH process to better fit the real market condition. Other researchers, such as Das (1998) and Fortune (1999), have modified the fixed jump parameters and developed stochastic jump models (1999). Chen

and Maheu (2002) introduced an ARJI model and discovered that the stock market exhibits considerable time variation in the distribution of jump intensity and size. Later, Maheu and Mccurdy (2004) and Daal (2007) investigated several forms of jump models.The EU carbon emissions trading market is cur

rently the most mature for carbon emissions trading, having evolved over decades since 2005. Bitcoin has also evolved over the decades since its inception in 2009, and it is now the world's most well-known and traded digital currency market. However, both markets, like other capital markets, are sub

ject to varying degrees of price jumps due to discrete random events that occur from time to time. As a result, it is critical to investigate abnormal price jump behavior and the factors that influence the price of carbon and digital money assets when they are subject to shocks. It is useful for ass

et pricing and risk management in the commodity market. As a result, this paper chooses the international carbon emission market and the bitcoin market. The paper discusses the abnormal price jump behavior and the factors that influence the price of emerging financial commodity assets during shocks.

The first chapter discusses the risk of a sharp increase in the price of carbon emissions trading in the GJR-GARCH-Jump model, as well as whether the price of carbon dioxide is influenced by energy and financial markets. We discovered a significant increase in the price return of CO2 emissions by ex

amining the price jump in the carbon rights trading market. Without taking into account the possibility of time-varying jump strength, the GJR-GARCH model with time-varying jump strength best captures the time-series dynamics of returns. GJR-GARCH models can overestimate the conditional variance (i.

e., risk) of the price return on CO2 emissions by underestimating asymmetric volatility and ignoring the jump effect. Furthermore, the Euro Stoxx 50, coal prices, natural gas prices, and trading volumes are the primary forces driving CO2 price returns.The second chapter examines the relationship bet

ween bitcoin price and investor sentiment, as well as when to relax the parameters of the GJR-GARCH-Jump model to constrain the model's fit in the context. As a result, we find that the GARCH-Jump model is best suited to describe the risk of frequent jumps in bitcoin prices, because the jump risk co

mponent, not the GARCH component, is the main contributor to bitcoin price volatility. Although the GARCH-Jump model provides the best fit, this is primarily because the intensity of jumps varies over time and does not understate the risk of price fluctuations. We also discovered that the volume of

Bitcoin transactions, the number of Bitcoin unique addresses, and the trend of Bitcoin Google searches are the primary drivers of Bitcoin price increases.Keywords: Financial factor, carbon emissions trading price volatility, Bitcoin