Search

Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate. [ABSTRACT FROM AUTHOR]

Purpose: The authors investigate the impact of the US stock market on the economies of the BRICS and major industrialized economies (G7). Design/methodology/approach: The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. Global vector autoregressive (GVAR) empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows. Findings: The authors summarize the results in four points: (1) financial integration variables increase the effect of the US stock market on the BRICS and G7, (2) the US shock produces similar responses in these groups regarding industrial production, stock markets and confidence but different responses regarding domestic currencies: in the BRICS, the authors detect appreciation of the currencies, while in the G7, the authors find depreciation, (3) G7 stock markets and policy rates are more sensitive to the US shock than the BRICS and (4) the estimates point out to heterogeneities such as the importance of industrial production to the transmission shock in Japan and China, the exchange rate to India, Japan and the UK, the interest rates to the Eurozone and the UK and confidence to Brazil, South Africa and Canada. Research limitations/implications: The results reinforce the importance of taking into account different levels of economic development. Originality/value: The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. GVAR empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows. [ABSTRACT FROM AUTHOR]

Amidst persistent inflationary pressures in Africa and central banks raising rates to counter inflation, leading to a shift from stocks to alternative assets and impacting stock return volatility, this paper investigates the influence of inflation on stock return volatility in five African countries using the GARCH-MIDAS approach, using mixed data of daily and monthly frequencies. We observe that inflation is important for stock return volatility, as higher inflation increases volatility and reduces returns. Our results provide a better understanding of the dynamics of stock market volatility while accounting for the role of inflation, using the natural frequencies of the variables. Our findings encourage monetary authorities to communicate their inflation expectations to the market participants/investors to moderate negative stock return volatility.

Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and 'short' data imply a 'ragged edge' at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published. [ABSTRACT FROM AUTHOR]

Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices. [ABSTRACT FROM AUTHOR]

We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic simulation, it is likely to suffer from multicollinearity. We show that a neural network‐based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management problem studied by Faraglia, Marcet, Oikonomou, and Scott (2019) to four maturities. We find that the optimal policy prescribes an active role for the newly added medium‐term maturities, enabling the planner to raise financial income without increasing its total borrowing in response to expenditure shocks. Through this mechanism, the government effectively subsidizes the private sector during recessions. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic threshold-minimum dominating set to represent the structure and evolution of relative prices in the economy. Due to the dynamic nature of the links between prices, the construction of a dynamic synthetic index is also proposed, and its properties and components are studied. The empirical results allow us to identify the most relevant nodes in the price network of the Uruguayan economy. With this set of prices, a synthetic index was generated that allows making more correlated projections with the original index and less error than indices of the same size used as benchmarks. The scope of these improvements in projections in the context of complex networks and their consequences in the design of anti-inflation policies are discussed. The empirical exercise reveals that -for the case of Uruguay- food prices are particularly relevant in this threshold-minimum dominating set; this result is discussed in the context of developing economies and globalized industries. [ABSTRACT FROM AUTHOR]

In the digital economy age, carbon emission reduction is perhaps the most crucial contribution affecting national activities and international negotiations. In a potential carbon reduction conflict, a rational participant expects to select a suitable strategic opinion to reach another state with improved benefit. Participants’ preferences reflect their priority ranking on multiple possible conflict solutions, directly determining the evolutionary direction and equilibrium solutions. Referring to the potential carbon reduction conflicts, Timed Petri Net for Conflict Analysis (TPNCA) is proposed as a graphical tool to explore participants’ rational preferences, state transformation, and conflict evolutionary equilibrium. The generation rule of TPNCA is designed according to carbon reduction co-operators’ rational preference knowledge. From the long-term perspective, dynamic unilateral improvement is proposed to reflect decision-makers’ strategic initiative willingness to reach end nodes. Dynamic equilibrium conditions are conducted to determine the final stability state for a conflict. This study reveals how participants’ preference knowledge and time constraints influence conflict evolutionary and dynamic equilibria. Climate group should positively engage in carbon reduction negotiations and flexibly adjust national tasks, according to real-time carbon reduction contributions. This study (1) reveals the influence of preference knowledge on conflict evolution, (2) explores the role of time constraints in determining a terminated node, (3) proposes dynamic equilibrium from the long-term perspective, and (4) helps guide participants’ strategic decision-making for mutually solving carbon reduction.

with the introduction of the euro as the domestic currency, economies have faced inflationary pressures caused by price convergence towards higher levels. During the last period of significant negative shocks from the environment, by entering the euro area, the negative effects of the likelihood of inflation caused by the introduction of the euro may be absent.

Abstract This is the first empirical study that analyzes the nature of business cycles in India using quarterly GDP growth data from 1998Q1 to 2022Q4 and the Markov Switching Auto-regressive model. In order to capture the impact of exclusion and inclusion of post pandemic period data, it has developed two alternative models. Based on these models, the study provides the forecast of quarterly GDP growth and cumulative real output losses due to the pandemic. It also provides alternative scenarios and trajectories of the Indian economy towards the US$ 5 trillion milestone. It is our hope that these results will be useful to policymakers and other stakeholders to understand the business cycle issue in India and take appropriate strategies to overcome the problems associated with these cycles. [ABSTRACT FROM AUTHOR]

Several studies have investigated the effect of oil price shocks on business cycles, production, prices, and labour market. The focus of this paper is primarily on the relationship between oil price changes and industrial production in G7 countries. To investigate the heterogeneity of impact, this paper investigates the effects of oil price changes based on the threshold role of net oil exports-to-GDP ratio. A panel smooth transition regression model is employed for analysis over two consecutive periods, namely, 1980–1999 and 2000–2017 as well as the combined period of 1980–2017. The results indicate that oil price hikes have a positive (negative) impact on oil-exporting (oil-importing) countries. The impact appears to be different across time periods, with the 1980–1999 sample exhibiting larger negative impact compared to 2000–2017. This hints on the effectiveness of energy diversification strategies employed post-2000 which resulted in lowering dependence on oil. [ABSTRACT FROM AUTHOR]

We focus on computing the geopolitical risk index (GPRI) for Nigeria, the largest economy in Africa. The country has faced crises, including Boko Haram insurgency, banditry, kidnapping, EndSARS, and regional secession, affecting trade relationships with other countries. Existing GPR indices do not include Africa, hence the need for this effort. The proposed index considers eight prominent Nigerian newspapers and relevant keywords on a daily basis from Jan 1, 2012, to June 19, 2023. We evaluate the predictability of the index by examining its relationship with Nigeria's currency and stock markets. Our findings reveal a remarkable positive relationship between the rising geopolitical tensions and the volatility of the currency and stock markets. Curbing geopolitical tensions in Nigeria is crucial for financial market stability.

Determinacy properties and conditions of equilibrium solution have been the subject of growing discussion and research in macroeconomics. Following in the footsteps of previous studies, we analyze determinacy in the baseline open-economy New Keynesian model developed by Gali and Monacelli in Rev Econ Stud 72(3):707–734 (Oxford University Press) (2005). We find that the open economy structure causes multifaceted behaviors in the system creating extra challenges for policy making. The degree of openness significantly affects determinacy properties of equilibrium under various forms and timing of monetary policy rules. Conditions for the uniqueness and local stability of equilibria are established. Determinacy diagrams are constructed to display the regions of unique and multiple equilibria. Numerical analyses are performed to confirm the theoretical results. Limit cycles and periodic behaviors are possible, but in some cases only for unrealistic parameter settings. Complex structures of open economies require rigorous policy design to achieve optimality. [ABSTRACT FROM AUTHOR]