Regime-Switching Behaviour In US Equity Indices: Two State Model With Kalman Filter Tracking And Finite State Machine Trading System

This thesis presents a time varying regime-switching model for US equity index daily returns. The parameters of the model are estimated recursively with the Kalman lter. We demonstrate our model and parameter estimation technique are effective by demonstrating improvements in model t compared to alternate models. Information from our model is used to build a Finite State Machine trading system with back-tested performance in excess of 15,000% above a buy and hold strategy for the DOW Jones Industrial average from 1928-2012. Similar results are found for both the S&P 500 index and the NASDAQ Composite index over a long period. Our model succeeds at identifying pro table investment opportunities and improving model t with a minimum of parameters.

Regime-Switching Behaviour In US Equity Indices: Two State Model With Kalman Filter Tracking And Finite State Machine Trading System

This thesis presents a time varying regime-switching model for US equity index daily returns. The parameters of the model are estimated recursively with the Kalman lter. We demonstrate our model and parameter estimation technique are effective by demonstrating improvements in model t compared to alternate models. Information from our model is used to build a Finite State Machine trading system with back-tested performance in excess of 15,000% above a buy and hold strategy for the DOW Jones Industrial average from 1928-2012. Similar results are found for both the S&P 500 index and the NASDAQ Composite index over a long period. Our model succeeds at identifying pro table investment opportunities and improving model t with a minimum of parameters.

Blockchain ETFs: dynamic correlations and hedging capabilities

PurposeIn this paper, the authors examine the interconnectedness of four blockchain exchange-traded funds (ETFs) with other financial markets, such as stocks and cryptocurrencies.Design/methodology/approachA multivariate dynamic conditional correlation model is used to model the relationship of blockchain ETFs with equity and cryptocurrency markets. Risk-minimizing hedge ratios are calculated following the methods used in studies by Kroner and Sultan (1993) and Sadorsky (2012).FindingsThe empirical results show a high degree of correlation of blockchain ETF returns with returns of the NASDAQ Composite Index, while the level of comovement with Bitcoin is relatively low.Research limitations/implicationsThe results imply that blockchain ETFs may be suitable for hedging purposes in a portfolio holding Bitcoin. Furthermore, investing in blockchain ETFs appears similar to investing in NASDAQ.Originality/valueTo the best of the authors’ knowledge, no studies have investigated the dynamic relationship of blockchain ETFs and other financial assets.

ARE STOCK MARKETS AND CRYPTOCURRENCIES CONNECTED?

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

Risk and Policy Uncertainty on Stock–Bond Return Correlations: Evidence from the US Markets

This paper investigates dynamic correlations of stock–bond returns for different stock indices and bond maturities. Evidence in the US shows that stock–bond relations are time-varying and display a negative trend. The stock–bond correlations are negatively correlated with implied volatilities in stock and bond markets. Tests show that stock–bond relations are positively correlated with economic policy uncertainty, however, are negatively correlated with the monetary policy and fiscal policy uncertainties. Correlation coefficients between stock and bond returns are positively related to total policy uncertainty for returns of the Dow-Jones Industrial Average (DJIA) and the S&P 500 Value stock index (VALUE), but negatively correlated with returns of S&P500 (Total market), the NASDAQ Composite Index (NASDAQ), and the RUSSELL 2000 (RUSSELL).

A Hypothesis Test Method for Detecting Multifractal Scaling, Applied to Bitcoin Prices

Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.

MODELLING AND FORECASTING STOCK MARKET VOLATILITY OF NASDAQ COMPOSITE INDEX

On the NASDAQ Composite Index from March 1971 to April 2019 it appears that the data is not stationary. For this reason, differentiation is needed by finding the value of stock returns from the NASDAQ Composite Index data from March 1971 to April 2019. After differentiating by looking for return values, the next analysis can be done, namely looking for the ARIMA model. Finding an ARIMA model using conventional analysis will require a long analysis time. So to shorten the analysis process using the EViews 10 statistical program. The results obtained after using the EViews program are getting the ARIMA model (8.0,6). The ARIMA model (8,0,6) was chosen because it has the smallest AIC value of 12,664073. This can be used as a reference later that the ARIMA model (8.0,6) is the best model in conducting forecasting. After that, the GARCH model is continued which aims to determine the ARIMA-GARCH model combination model. From the results of the analysis, it is known that the best model for forecasting the return value of the NASDAQ Composite Index is a combination of ARIMA (8.0,6)-EGARCH (1,1) models, which from the results of this analysis are known for fluctuating return values and index values for NASDAQ for one year in the future it is stagnant and does not show a trend.

THE ECONOMETRIC ANALYSIS OF THE DYNAMICS OF ETHEREUM IN THE SHORT-TERM PERIOD

The article presents an econometric analysis of the effect of stock indicators, such as Comex Gold futures, Dow Jones Industrial Average index and NASDAQ Composite, on the Ethereum cryptocurrency dynamics in the 100-day period. As part of the study, an econometric model of the dynamics of e-currency was built. The survey results show that when the Comex gold futures price changes by 1% on average, the Ethereum price changes by 5.01% in the same direction, when the Dow Jones Industrial Average index changes by 1%, the Ethereum price is 10.897%, and when the NASDAQ Composite index changes, the Ethereum price will change in the opposite direction to 3.59%

The relationship between the Nasdaq Composite Index and energy futures markets

This paper sheds light on the relationship between the Nasdaq Composite Index and a newly proposed Energy Futures Conditions Index (EFCI). While various financial conditions indices provide information about the financial stability of a country, the existence of an energy condition index, using futures markets, is scarce. Using weekly data over the period 1992–2017, this paper introduces an energy futures index using principal component analysis and test its predictability over the Nasdaq Composite Index. The EFCI captures 95% of the variability inherent in crude oil, heating oil and natural gas futures’ total reportable positions. Stability in forecast errors over different lags suggests a one week lag is sufficient to forecast weekly Nasdaq Composite Index. 95% prediction levels support that the estimated model captures actual equity market index values, except for the 2000 technology bubble. Distributions of level data were non-normal, not serially correlated and homoscedastic under the whole sample period, with diagnostics on pre and post technology bubble crisis showing mixed results. While differencing ensured homoscedastic errors in the forecasting model, Granger causality supported non-causality from both energy futures and equity markets, suggesting no evidence of cross market information flows.