Predictability of the Realised Volatility of International Stock Markets Amid Uncertainty Related to Infectious Diseases

In the context of the great turmoil in the financial markets caused by the COVID-19 pandemic, the predictability of daily infectious diseases-related uncertainty (EMVID) for international stock markets volatilities is examined using heterogeneous autoregressive realised variance (HAR-RV) models. A recursive estimation approach in the short-, medium- and long-run out-of-sample predictability is considered and the main findings show that the EMVID index plays a significant role in forecasting the volatility of international stock markets. Furthermore, the results suggest that the most vulnerable stock markets to EMVID are those in Singapore, Portugal and The Netherlands. The implications of these results for investors and portfolio managers amid high levels of uncertainty resulting from infectious diseases are discussed.

Star Tracker Geometric Calibration Through Full-State Estimation Including Attitude

A star tracker calibration method using star images is presented in this paper. Unlike previous works, the proposed method estimates all parameters and the attitudes at once in a single least-squares formulation for the optimal calibration, which can be easily converted to a recursive estimation form. In addition, this paper presents a method to estimate the overall star tracker performance for attitude determination from the calibration results. Since the proposed method uses star images only, it can be applied to both on-orbit and ground star tracker calibration. The simulations show improvements in calibration performance about four times compared to the previous calibration method. The calibration experiments with actual star images are conducted to test its application.

Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

This paper investigates the application of adaptive slope-seeking strategies to dual-input single output dynamic processes. While the classical objective of extremum seeking control is to drive a process performance index to its optimum, this paper also considers slope seeking, which allows driving the performance index to a desired level (which is thus sub-optimal). Moreover, the consideration of more than one input signal allows minimizing the input energy thanks to the degrees of freedom offered by the additional inputs. The actual process is assumed to be locally approachable by a Hammerstein model, combining a nonlinear static map with a linear dynamic model. The proposed strategy is based on the interplay of three components: (i) a recursive estimation algorithm providing the model parameters and the performance index gradient, (ii) a slope generator using the static map parameter estimates to convert the performance index setpoint into slope setpoints, and (iii) an adaptive controller driving the process to the desired setpoint. The performance of the slope strategy is assessed in simulation in an application example related to lipid productivity optimization in continuous cultures of micro-algae by acting on both the incident light intensity and the dilution rate. It is also validated in experimental studies where biomass production in a continuous photo-bioreactor is targeted.

German forecasters’ narratives: How informative are German business cycle forecast reports?

Based on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

Domestic Savings, Investment, and Economic Growth: an Empirical Evidence from Nepal using VEC Model

This study examines the relationship between domestic savings, investment, and economic growth in Nepal by using time series data covering from the period 1975 to 2019. The vector error correction model (VECM) has been used to investigate the long-run and short-run causal relationship between the variables. The Johansen cointegration test results confirmed that there is a long-run relationship between savings, investment, and economic growth. Therefore, further analysis has to be set for the VEC model to analyze both long-run and shortrun causality. The VECM equation result is -0.1363 which is the adjustment speed of disequilibrium towards the equilibrium in long run. The coeffi cient of savings and investments are positive with economic growth which also indicates that both variables have a positive impact on economic growth in the short run. The results of the Jarque-Bera test show the residual distribution is normally distributed. For the model stability test, the study performed recursive estimation applying CUSUM and CUSUM of square, and both tests move within the 5 percent level of upper and lesser bound significance indicating that the model is stable over the observation period. Overall, the study suggests that in Nepal, domestic savings and investment growth have a positive contribution to economic growth. The central bank, planning commission, and ministry of finance should focus on stimulating the capital formation and productive sector investment for sustainable economic growth in Nepal.