In times of turbulent financial markets, investors all around the globe seek for opportunities protecting their portfolios from devastating losses. Historically, commodities were regarded as a safe haven providing sound returns which offset potential losses arising from dropping equity prices in times of market turmoil. While sugar would have provided a proper hedge against crashing equity markets during the initiation of the 2007 bear market and the onset financial crisis, sugar prices dropped likewise equity during the outbreak of COVID-19 and the consequent market shock. The goal of the paper is to elaborate on the differences in sugar price dynamics during the aforementioned economic disruptions by employing a multiple linear regression approach using data from the last quarter 2007 as well as the first quarter of 2019. The findings suggest that the behavioral differences stem from the deep link between oil and sugar prices. While oil did not influence the price of sugar during the outbreak of the financial crisis, it had tremendous influence on sugar prices during the outbreak of the corona crisis. Currently, sugar provides a substantial upside for an investor's portfolio since the demand and supply-side shock on oil prices due to corona crisis as well as the Saudi-Russian oil price war drove oil prices and consequently sugar prices to a historic low. Sugar futures provide the advantage of offering a smaller contract size compared to oil futures, and even though both commodities trade in contango as of March 2020, the sugar future curve is by far not as steep as the oils. Resultingly, investors benefit from lower rollover costs while prospering from a potential surge in oil prices.

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.