Search Results (9)

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

We aim to analyze the dynamics of multiple financial assets with variable volatility. Instead of a standard analysis based on the Black–Scholes model, we proceed with the multidimensional Volterra model, which allows us to treat volatility as a stochastic process. Taking advantage of the long memory function of this type of model, we analyze the reproduced movements using recent algorithms in the field of functional data analysis (FDA). In fact, we develop, in particular, new risk tools based on the asymmetric least squares loss function. We build an estimator using the multifunctional kernel (MK) method and then establish its asymptotic properties. The multidimensionality of the Volterra process is explored through the dispersion component of the convergence rate, while the nonparametric path of the risk tool affects the bias component. An empirical analysis is conducted to demonstrate the ease of implementation of our proposed approach. Additionally, an application on real data is presented to compare the effectiveness of expectile-based measures with Value at Risk (VaR) in financial risk management for multiple assets. Full article

In the past few decades, India’s tech industry has boomed, making it a leader in the digital world. Today, India has many big tech companies, well-trained software developers, and cutting-edge technology like AI and cloud computing. This success shows India’s innovative spirit and makes the country a good example for other developing nations. However, global portfolio managers often overlook potential diversification opportunities beyond India’s dynamic stock market. This study investigates the viability of MINT (Mexico, Indonesia, Nigeria, and Turkey) as diversification targets, specifically analyzing spillover effects and volatility dynamics between their stock markets and that of India. Leveraging vector autoregressions (VARs) and dynamic conditional correlation (DCC)–GARCH models, we uncover intricate relationships. Further, DCC–GARCH analysis reveals varying degrees of volatility spillover, offering valuable insights for risk management. Our findings suggest that MINT economies, particularly Mexico and Turkey, hold promise for Indian portfolio diversification. By strategically incorporating these markets, investors can potentially mitigate India-specific risks and enhance portfolio returns. We urge global portfolio managers to consider Turkey as a viable diversification avenue, acknowledging the nuanced market growth dynamics highlighted in this study. Full article

Tropical cyclone rapid intensification (RI) prediction still remains a big international challenge in numerical weather prediction. Hurricane Matthew (2016) underwent extreme and non-classic RI, intensifying from a Category 1 storm to a Category 5 hurricane within 24 h under a strong vertical shear environment. However, most models failed to capture this RI, and limited or no inner core, and outflow observations were assimilated in the NWS operational HWRF Model before the onset of RI for Matthew (2016). The goals of the study are to (1) explore the best way to assimilate the High-Density Observations (HDOB, including FL and SFMR) and AMV data; (2) study the impact of assimilating these observations on the analysis of both the inner-core and outflow structures; and (3) examine the impact of assimilating these data on the prediction of RI for Matthew. The main results are as follows: (1) With proper pre-processing of the HDOB observations and by using a 4DEnVar method, the inner-core structure analysis was improved. And the RI prediction is more consistent with the best track without spin-down for the first 24 h. Assimilating CIMMS AMV observations on top of the HDOB observations further improves both the track and intensity forecasts. Specifically, both the magnitude and timing of the peak intensity are further improved. (2) Diagnostics are conducted to understand how the assimilation of these different types of observations impacts RI prediction. Without assimilating HODB and AMV data, baseline experimentover-predict the intensification rate during the first 18 h, but under-predict RI after 18 h. However, the assimilation of FL and SFMR and CIMMS AMV correctly weakens the upper-level outflow and improves the shear-relative structure of the inner-core vortex, such as reducing the low-level moisture in the downshear left quadrant. The deep convection on the downshear side is weaker than baseline for the first 18 h but keeps enhancing, later moving cyclonically to the USL quadrant, and then causes more subsidence warming, maximizing in the USL quadrant and the maximum wind increases faster. Moreover, the rapid intensification rate is much more consistent with the best track and the forecast skill of RI is improved. Therefore, 4DEnVar assimilation with proper pre-processing of the high-density observations can indeed correct the shear-relative moisture and structural distributions of both the inner core and environment for TCs imbedded in the stronger shear, which is important for shear-TC RI prediction. Full article

Search engines have been the primary tool for online information search before traveling. Timely detection and the control of peak tourist flows in scenic areas prevent safety hazards and the overconsumption of tourism resources due to excessive tourist clustering. This study focuses on the spatial-temporal interactions between the pre-trip stage and the after-arrival stage to investigate online information search behavior. Big data obtained from mobile roaming and search engines provide precise data on daytime and city scales, which enabled this paper to examine the relationship between daily tourist arrivals and their pre-trip searching from 40 cities within the Yangtze River Delta urban agglomeration. This study had several original results. First, tourists generally search for tourist information 2–8 days before arriving at destinations, while tourist volume and SVI from source cities show distance attenuation. Second, SVI is a precursor to changes in tourist volume. The precursory time rises with the increase of traffic time spatially. Third, we validated a VAR model and improved its accuracy by constructing it based on the spatial-temporal differentiation of search features. These findings would enhance the management and preservation of tourism resources and promote the sustainable development of tourism destinations. Full article

With the global acknowledgment of the Kyoto Protocol came the carbon derivatives such as carbon futures, options, and swap contracts. The innovative carbon derivatives are complex in design and contain risks that are difficult to predict and avoid. The global Carbon Market should have higher requirements in the supervision laws and regulations. To this end, the financial system theories and the financial characteristics of carbon derivatives are expounded. The three-dimensional structural modeling technique of systems engineering is introduced to construct the Carbon Market framework. The proposed framework factors for the organization, product, and policy dimensions of the Carbon Market are also described. Additionally, this model explains the market organization, the instruments and media connecting market supply and demand, and government regulation measures. In particular, the supervision and management aspects of the policy dimension are introduced in detail. The Carbon Market and relevant law systems in the United States, the European Union, and India are mainly studied and compared. Based on the comparison results, the necessity of market supervision is explained. Finally, the Big Vector Autoregression model is used to study the relationship between the Carbon Market, energy market, and financial market. After the introduction of the National Carbon Market, the correlation between the energy market and the financial market has become relatively complex but also presents a certain degree of asymmetry. According to the above results, the paper proposes to use the “regulatory sandbox” mechanism to improve the regulation of the subject and object of the carbon financial and legal relationship and try to carry out regulatory innovation for the risks of the entire carbon market. Full article

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance. Full article

The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation. Full article

The study of the lead-lag relationship between the Hong Kong offshore Renminbi (CNH) spot market and onshore (CNY) spot market is of great importance for its wide application in market risk management. In this paper, we study the correlation between the CNH and CNY spot markets in the contexts of daily closing price change and the 2011–2016 Bid-Ask spread (BAS). We test the existence of causality relation between CNH/CNY pairwise change and BAS by using the conventional method of vector auto-regression (VAR) model in the observation period. Furthermore, we detect the local lead-lag dependence relationships between CNH/CNY pairwise change and BAS by using a non-parametric approach-adjusted Thermal Optimal Path (TOP) method. Through introducing a Pruning and Path segmentation algorithm, we address the problem of computation infeasibility that may be encountered in application of the existing TOP method for the detection of lead-lag relationship between two time series with long time duration. Theoretical analyses and simulation results are presented to verify validity of adjusted TOP method in the setting of big time-series data set. This study also provides some interesting findings: (1) the offshore CNH market is informationally integrated with the onshore CNY market from two aspects of closing price change over two consecutive single days and BAS used as a proxy for market liquidity; (2) Local dependency between the two markets changes with economic conditions changing, which would facilitate both investor and policy maker decision making. Full article