Multivariate risk aversion utility,application to ESG investments

This paper uses the concept of multivariate or multi-attributive utility to attach different risk aversion levels to different sources of wealth (e.g. sectors, stocks, asset classes). In this context, we address the topic of environmental, social, and corporate governance (ESG) investments from the perspective of an investor with different risk aversion levels to green and brown stocks. We obtain closed-form solutions for the optimal allocations, value function, and wealth equivalent losses (WEL) from suboptimal choices. The numerical analysis demonstrates the significant increase, of up to 33%, in green investments when accounting for a differential in risk aversions levels, with up to 65% in WEL when using same risk-aversion levels.¹     

Option prices for risk-neutral density estimation using nonparametric methods through big data and large-scale problems

Option pricing theory determines the structure of call and put option pricing functions. In nonparametric risk-neutral density estimation based on kernel functions, local constraints cannot induce a second derivative function that must integrate one. Convexity and monotonicity of pricing functions also cannot be enforced. A large-scale (optimization) approach is proposed for the risk-neutral density estimation, imposing an enlarged set of no-arbitrage constraints. We considered simulations using Heston's model and hypergeometric functions. The method is applied to samples of intraday data from VIX and S&P500 indexes.     

Nowcasting German GDP: Foreign factors,financial markets,and model averaging

This paper develops a nowcasting model for the German economy. The model outperforms a number of alternatives and produces forecasts not only for GDP but also for other key variables. We show that the inclusion of a foreign factor improves the model’s performance, while financial variables do not. Additionally, a comprehensive model averaging exercise reveals that factor extraction in a single model delivers slightly better results than averaging across models. Finally, we estimate a “news” index for the German economy in order to assess the overall performance of the model beyond forecast errors in GDP. The index is constructed as a weighted average of the nowcast errors related to each variable included in the model.     

DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations

This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances and correlations of daily returns based on measures of realized variances and correlations built from intraday data. Formulas for multi-step forecasts of conditional variances and correlations are provided. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecasts the scalar HEAVY models outperform the scalar BEKK-HEAVY model based on realized covariances and the scalar BEKK, DCC, and DECO multivariate GARCH models based exclusively on daily data.     

Rebuilding or refurbishing: Heterogeneity effects of urban renewal strategy

This study investigates the heterogeneous policy effects of urban renewal strategies in shantytown renewal in Beijing. Using the spatial difference-in-difference approach, this study identifies the renewal approaches for each shantytown to analyze how each strategy affects housing prices and population density within the policy target area and surrounding area. The empirical results suggest that current urban renewal actions in Beijing have not achieved the expected policy goals and imply significant heterogeneity between refurbishing and rebuilding shantytowns in improving building value. Rebuilding projects have positive renewal effects and externalities, driving housing prices to increase by 5.86%–7.25% within the shantytown and a 1-km radius. Furthermore, the dynamic analysis shows that these impacts are prolonged, with housing prices maintaining an upward trend for 2–6 years after rebuilding. However, there is no causality between refurbishing dilapidated areas and increasing building values.     

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.     

Population density,urbanisation and agricultural mechanisation in modern Ghana

Farmers' technological choices take place within farming systems that are shaped by population pressure, connectivity to urban markets and agro-ecological conditions. The relationship between these drivers and agricultural technology use is ambiguous. On the one hand, population growth can increase the supply of labour, driving down wage rates and reducing the incentives for mechanisation. On the other hand, rural–urban movements of people can reduce rural labour supply while simultaneously driving up the demand for food and hence the demand for mechanisation. Past theories of agricultural mechanisation have explained the low adoption of agricultural machinery in land-abundant cereal production systems of SSA in terms of these drivers. However, recent empirical observations find extensive adoption of mechanised ploughing technology by small, medium and large-scale farmers in Ghana. Examining the Ghanaian experience can thus shed new light on theories of mechanisation. A large household survey dataset covering eight districts is combined with geo-spatial data on population, urban proximity and agro-ecological factors to consider whether the existing theories are able to explain farm-level adoption decisions in this context. The analysis finds that a farmer's decision to use agricultural machinery is associated with lower population density and proximity to urban centres. In northern Ghana, these drivers of technological change are as important as farm household characteristics in understanding cross-sectional patterns of machinery adoption.     

近距离无线电台邻道干扰的功率谱解析

指挥控制系统的无线通信设备数量多、工作频率范围窄，相互之间存在严重的邻道干扰。针对近距离电台间邻道干扰问题，提出了基于功率谱解析的分析方法。首先理论推导了信号原功率谱密度函数与展宽延拓后功率谱密度的严格数学关系，在此基础上研究邻道干扰产生机制，进而分析不同邻道的干扰强度，得到了邻道功率与频率之间的关系式，推导了进入接收机的信干噪比（SINR）；最后，通过仿真验证了所提出的分析方法的可行性。理论计算与仿真结果相差在0.5 dB以内，为进一步有效规划指控系统频谱和提高利用效率提供了重要依据。     

Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models

Many static and dynamic models exist to forecast Value-at-Risk and other quantile-related metrics used in financial risk management. Industry practice favours simpler, static models such as historical simulation or its variants. Most academic research focuses on dynamic models in the GARCH family. While numerous studies examine the accuracy of multivariate models for forecasting risk metrics, there is little research on accurately predicting the entire multivariate distribution. However, this is an essential element of asset pricing or portfolio optimization problems having non-analytic solutions. We approach this highly complex problem using various proper multivariate scoring rules to evaluate forecasts of eight-dimensional multivariate distributions: exchange rates, interest rates and commodity futures. This way, we test the performance of static models, namely, empirical distribution functions and a new factor-quantile model with commonly used dynamic models in the asymmetric multivariate GARCH class.