02. Master's Thesis

Permanent URI for this collection

https://nur.nu.edu.kz/handle/123456789/8146

Browse

Browsing 02. Master's Thesis by Subject "Bitcoin"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemRestricted
    AN ASSESSMENT OF TRIANGULAR ARBITRAGE OPPORTUNITIES BETWEEN CRYPTOCURRENCY AND FIAT CURRENCY EXCHANGES. PREDICTORS AND FINANCIAL RATIONALE
    (Nazarbayev University, Graduate School of Business, 2021-12-21) Almen, Anar; Zhaipanov, Dosmukhamed; Dauletkhanuly, Yeldos
    This study assesses the eventual existence of triangular arbitrages between crypto and fiat currencies and analyzes economic factors which might contribute to the appearance of such opportunities. In particular, the authors look at core alphas of arbitrage based on crypto to fiat exchanges in two different crypto open source exchanges. By examining the existence of any statistically significant alphas, we find that this opportunity exists and might be exploitable for the US based exchange, Coinmarketcap. Similarly, triangular arbitrage exists, but are much smaller in the Coingecko exchange. This finding demonstrates the existence of discrepancies between exchanges. Furthermore, regression analyses indicate that Bitcoin is more sensitive to commodity prices changes as compared to Ethereum. In addition, we find that google searches related to cryptocurrencies are a strong predictor of arbitrage opportunities in triangular sets including Ethereum. In contrast, google trend aggregator data had almost no effect on Bitcoin containing exchange triplets. Additionally, proximity to the market plays a significant role in determining impact of central bank monthly T-bill rate on quotation volatility and subsequent arbitrage opportunities.
  • Thumbnail Image
    ItemOpen Access
    EVALUATION OF THE FORECASTING ABILITY OF RISK-NEUTRAL DENSITY IN BITCOIN OPTIONS
    (Nazarbayev University Graduate School of Business, 2024-12-12) Saparbekov, Dias
    This study assesses the out-of-sample forecasting capabilities of risk-neutral density models in Bitcoin options market, with a focus on the Normal Inverse Gaussian (NIG) density. Understanding forward-looking price dynamics becomes critical as cryptocurrencies continue to gain a reputation in financial markets. This research examines how the NIG model, with its capability to capture skewness and kurtosis, compares to the benchmark log-normal (LN) distribution. The analysis applies the likelihood ratio test to evaluate the predictive performance of the models. As a result, NIG model improves the accuracy of tail forecasts, outperforming LN in capturing extreme market movements, which holds implications for risk management and market timing in Bitcoin market.