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    题名: 加密貨幣風險管理： Lévy過程與時變波動率的應用
    Cryptocurrency Risk Management Using Lévy Processes and Time-Varying Volatility
    作者: 吳海棠
    Wu, Hai-Tang
    贡献者: 岳夢蘭
    Yueh, Meng-Lan
    吳海棠
    Wu, Hai-Tang
    关键词: 加密貨幣
    最大概似估計
    風險值
    平均風險值
    Cryptocurrency
    Quasi maximum likelihood estimation
    Value-at-risk
    Average value-at-risk
    日期: 2024
    上传时间: 2024-07-01 12:38:36 (UTC+8)
    摘要: 加密貨幣已經成為金融領域的一股革命性力量，通過其去中心化的架構和區塊鏈技術重塑了傳統金融。儘管引起了廣泛的關注，但由於其固有的波動性和缺乏內在價值資訊，加密貨幣在風險管理和估值方面面臨著獨特的挑戰。本文提出了一種新穎的方法，採用Lévy-GJR-GARCH模型來分析三種主要加密貨幣——比特幣、以太幣和瑞波幣的風險。

    通過資料分析，我們觀察到加密貨幣報酬的不同特徵，包括波動率聚類、偏態和超額峰度，偏離了正態分佈的假設。利用Lévy-GJR-GARCH模型，將Lévy過程與GJR-GARCH模型結合起來，我們能夠準確捕捉這些實證現象。我們的研究結果強調了將非正態分佈特徵（如無限跳躍過程）納入模型中以有效地建模加密貨幣回報和波動性的重要性。

    此外，通過採用兩步擬最大概似估計方法，我們證明了無限跳躍模型在評估尾部風險和極端損失方面優於標準和有限跳躍模型。對我們的模型預測前一天的風險值（VaR）和平均風險價值（AVaR）的回溯測試結果表明，在市場波動較大的情況下，具有無限跳躍的模型提供了更為保守的風險估計。

    本研究通過為投資者、機構和監管機構提供強大的工具，推動了加密貨幣市場風險管理策略的進展。通過準確捕捉加密貨幣回報的動態，並結合創新的分佈框架，我們的研究增強了對市場行為的理解，並促進了更為明智的決策，在應對加密貨幣投資的複雜性時提供了更多資訊。
    Cryptocurrencies have emerged as a transformative force in the financial landscape, reshaping traditional finance with their decentralized architecture and blockchain technology. Despite garnering significant attention, they present unique challenges in risk modeling and valuation due to their inherent volatility and lack of intrinsic value information. This paper proposes a novel approach by employing the Lévy-GJR-GARCH model to analyze the risk of three major cryptocurrencies—Bitcoin, Ethereum, and Ripple.

    Through extensive data analysis, we observe distinct features in cryptocurrency returns, including volatility clustering, skewness, and excess kurtosis, deviating from the assumptions of normal distributions. Leveraging the Lévy-GJR-GARCH model, which integrates flexible Lévy processes with GJR-GARCH for time-varying volatility, we accurately capture these empirical phenomena. Our findings underscore the importance of incorporating non-normal distributional characteristics, such as infinite jump processes, to effectively model cryptocurrency returns and volatility.

    Furthermore, employing a two-step quasi-maximum likelihood estimation method, we demonstrate the superiority of infinite jump models over standard and finite activity models in assessing tail risks and extreme losses. Backtesting our models for one-day-ahead forecasts of Value-at-Risk (VaR) and Average Value-at-Risk (AVaR) reveals that models with infinite jumps provide more conservative risk estimates, particularly during volatile market conditions.

    This research contributes to advancing risk management strategies in cryptocurrency markets by providing robust tools for investors, institutions, and regulators. By accurately capturing the dynamics of cryptocurrency returns and incorporating innovative distributional frameworks, our study enhances understanding of market behaviors and facilitates more informed decision-making in navigating the complexities of cryptocurrency investments.
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    描述: 博士
    國立政治大學
    財務管理學系
    108357502
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108357502
    数据类型: thesis
    显示于类别:[財務管理學系] 學位論文

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