C11 - Bayesian Analysis: GeneralReturn

We investigate an impact of oil-price shocks on GDP and exchange rate dynamics in resource-heterogeneous economies. We employ a Markov regime-switching version of a vector autoregressive (VAR) model to allow for regime shifts, non-linear effects and timevarying parameters of the VAR process. Empirically we use quarterly data series in oil exporting, metal-exporting, and less-resource-intensive economies. On average, real GDP in oil-exporting economies exhibits substantial contraction, while for metal exporters there is a significant real GDP expansion suggesting an offsetting effect of metal exports on oil imports. We find that currency appreciation state is more persistent in oil- and metal exporting economies while less-resource-intensive economies remain longer in a currency depreciation state. Further evidence suggests existence of the counteracting forces such as foreign exchange interventions by authorities in oil-exporting economies. It also emerges that currency appreciation in oil-exporting economies is driven largely by economic performance rather than oil price  movement.

Aim of this paper is to use Machine Learning algorithm called XGBoost developed by  Tianqi Chen and Carlos Guestrin in 2016 to predict future development of the Bitcoin (BTC) price and build an algorithmic trading strategy based on the predictions from the model. For the final algorithmic strategy, six XGBoost models are estimated in total, estimating following n-th day BTC Close predictions: 1,2,5,10,20,30. Bayesian optimization techniques are used twice during the development of the trading strategy. First, when appropriate hyperparameters of the XGBoost model are selected. Second, for the optimization of each model prediction weight, in order to obtain the most profitable trading strategy. The paper shows, that even though the XGBoost model has several limitations, it can fairly accurately predict future development of the BTC price, even for further predictions. The paper aims specifically for the potential of algorithmic trading during the COVID-19 period, where BTC cryptocurrency suffered extremely volatile period, reaching its new all-time highest prices as well as 50% losses during few consecutive months. The applied trading strategy shows promising results, as it beats the B&H strategy both from the perspective of total profit, Sharpe ratio or Sortino ratio.

We analyze the impact of oil price shocks on the macroeconomic fundamentals in a panel of emerging economies from three regions and with different resource endowments. The existing literature on emerging economies remains inconclusive on how regional factors and resource characteristics affect the response of macroeconomic variables against oil price shocks. We show that (i) exports in Europe and Central Asia are more oil-driven than East Asia and the Pacific, and that (ii) policy-makers in East Asia and the Pacific should be concerned with real exchange appreciation following a positive oil shock to mitigate loss in non-oil export market. Analysis by resource-endowment further reveal that in less-resource-intensive economies oil price shock cause large variation consumption, and a negative and persistent impact on real GDP. In mineral-exporting economies, real GDP and interest rates are largely driven by oil price shocks. The response of real GDP in mineralexporting economies is short-lived. In oil exporting economies, it is only real GDP that has a large variation in response to oil price shock. For policy making, our findings underscores the need for customized policy responses to oil price shocks depending on resourceendowments as we confirm that a ”uniform-policy cannot fit all” economies.