Volatility Factors Research Articles

Forecasting volatility using double shrinkage methods

In this paper, we propose and evaluate a shrinkage based methodology that is designed to improve the accuracy of volatility forecasts. Our approach is based on a two-step procedure for extracting latent common volatility factors from a large dimensional and high-frequency dataset. In the first step, we apply either least absolute shrinkage operator (LASSO) or the elastic net (EN) shrinkage on estimated integrated volatilities, in order to select a subset of assets that are informative about the target asset. In the second step, we utilize (sparse) principal component analysis on the selected assets, in order to estimate latent return factors, which are in turn used to construct latent volatility factors. Our two-step method is found to yield more accurate volatility predictions than a variety of alternative models based on approaches such as direct application of (S)PCA and direct application of LASSO or EN shrinkage, when comparing out-of-sample R2s and mean absolute forecasting errors, and when implementing predictive accuracy tests. Additionally model confidence sets are found to contain models solely based on our two-step approach. These forecasting gains are found to be robust to the use of original or log-scale realized volatility models, different data sampling frequencies, and different forecasting sub-periods.

An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.

Anticipated Volatility, Unanticipated Volatility Shocks, Excess Monthly Returns and Stock Market Behaviour of Pakistan

An investor considers various factors to choose the financial assets. The portfolio theory suggests that risk, return, taxes, information and liquidity are vital factors in portfolio choice. The study is based on risk premium, uncertainty, shocks and volatility of Pakistan stock exchange market. The study has used monthly time series data of returns of ten sectors of Pakistan stock market ranging from 2006 to 2014 to measure the anticipated and unanticipated factors of risk, return and uncertainty. Using CAPM, it is pointed out that volatility factor is present and high in overall stock market and the level of volatility in different sectors of the market moves in the same direction which suggest that speculative activities are widely spread in every sector and in overall market as well.

Simultaneous hedging strategy for price and volume risks in electricity businesses using energy and weather derivatives

In general, electric utilities face intrinsic risks as their revenues depend on high volatility factors including price and volume of sales/procurements. Aiming for an effective strategy to control those risks, we construct a hedging portfolio based on energy and weather derivatives, which can minimize the revenue fluctuations. To this end, we provide unique methods by applying nonparametric regression techniques to synthesize the payoff functions of derivatives that change with time, based on tensor product spline functions. The proposed methodology enables us to incorporate two dimensional smoothing conditions of the underlying asset price and expiration date with a yearly cyclical trend. Moreover, we show that the applied method of Analysis of Variance (ANOVA) decomposition can separate deterministic time trends from the original multivariate payoff functions, and hence, a simultaneous estimation of multiple derivatives payoff functions is achieved. By assuming that revenues have yearly cyclical trends even when viewed at the rate of annual change, we also introduce a spline function with cross variables to consider such a mixed effect. In addition, we propose new standardized derivatives with the square of the temperature prediction error as the underlying asset. Empirical analysis using data from both the Japan Electric Power Exchange (JEPX) and PJM in the U.S. demonstrates the significant hedging effect and supports the versatility of the proposed modeling approach.

A Discrete-Time Hedging Framework with Multiple Factors and Fat Tails: On What Matters

This article presents a quadratic hedging framework for a general class of discrete-time affine multi-factor models and investigates the extent to which multi-component volatility factors, fat tails, and a non-monotonic pricing kernel can improve the hedging performance. A semi-explicit hedging formula is derived for our general framework which applies to a myriad of the option pricing models proposed in the discrete-time literature. We conduct an extensive empirical study of the impact of modelling features on the hedging effectiveness of S&P 500 options. Overall, we find that fat tails can be credited for half of the hedging improvement observed, while a second volatility factor and a non-monotonic pricing kernel each contribute to a quarter of this improvement. Moreover, our study indicates that the added value of these features for hedging is different than for pricing. A robustness analysis further shows that a similar conclusion can be reached when considering the Dow Jones Industrial Average. Finally, the use of a hedging-based loss function in the estimation process is investigated in an additional robustness test, and this choice has a rather marginal impact on hedging performance.

Stock market volatility and jumps in times of uncertainty

In this paper we examine the predictive power of latent macroeconomic uncertainty on US stock market volatility and jump tail risk. We find that increasing macroeconomic uncertainty predicts a subsequent rise in volatility and price jumps in the US equity market. Our analysis shows that the latent macroeconomic uncertainty measure of Jurado et al. (2015) has the most significant and long-lasting impact on US stock market volatility and jumps in the equity market when compared to the respective impact of the VIX and other popular observable uncertainty proxies. Our study is the first to show that the latent macroeconomic uncertainty factor outperforms the VIX when forecasting volatility and jumps after the 2007 US Great Recession. We additionally find that latent macroeconomic uncertainty is a common forecasting factor of volatility and jumps of the intraday returns of S&P 500 constituents and has higher predictive power on the volatility and jumps of the equities which belong to the financial sector. Overall, our empirical analysis shows that stock market volatility is significantly affected by the rising degree of unpredictability in the macroeconomy, while it is relatively immune to shocks in observable uncertainty proxies.

Planning and Operation of Renewable Energy Power System Based on Energy Internet Technology

Under the leaping development pattern of renewable energy brought by carbon emission targets, large-scale wind and solar power in high proportion of renewable energy power system has brought volatility and uncontrollable factors, and generation-grid coordination has become a technical problem for the safe and stable operation. The concept of energy Internet introduces Internet technology into the field of energy consumption application, which provides a new way for the full coupling of various energy consumption patterns, and is an effective method to solve the problem of safe and stable operation of high proportion renewable energy power system. Based on energy Internet technology, this paper designs the sustainable development architecture of generation-grid coordination from three aspects of planning, operation and mechanism, which lays a solid foundation for the development of generation-grid coordination in the future ' two high ' power system.

The volatility puzzle of the low-risk anomaly

This paper shows that leading theories of the low-risk anomaly fail to provide a complete explanation for the anomaly’s conditional performance. Sharpe ratios and abnormal returns of betting-against-beta (BAB) and -idiosyncratic volatility (BAV) factors rise significantly following low volatility months, even when controlling for lottery preferences, and in the case of BAB, also controlling for mispricing and limits to arbitrage. Moreover, the leverage constraints theory counterfactually predicts that market and BAB Sharpe ratios increase with volatility. We also show that highly active institutions shift from high- to low-beta stocks as volatility increases, suggesting their demand contributes to the performance of BAB.

What Uncertainties Matter to Cryptocurrencies?

We contribute to financial literature by identifying economic uncertainties salient to the price dynamics of cryptocurrencies. To measure this relationship, we examine the common stochastic trends between cryptocurrencies and major text-based uncertainty indices — categorical and broad — from the US and major developed economies from 2015 to 2021. Results from static factor tests using high-dimensional stochastic volatility factor models indicate trivial associations between global uncertainties and the crypto-sphere. Further investigation from dynamic implied correlation matrices, however, suggest that this phenomenon has undergone several changes over time. In fact, ties between US-based monetary policy uncertainties were non-trivial between 2015 to 2016. Following the post-Trump election bull run, the association faded, before reappearing upon the advent of the COVID-19 pandemic. Uncertainties surrounding some decisions by US government register minor significance. Uncertainties from European countries and China show some influence, with Japan registering an inverse relationship. Causality tests largely approbate these results, while underscoring an important point that while the pricing dynamics of cryptocurrencies may be independent from global uncertainties, their second moments remain attached to global trends. In sum, volatility in the crypto market is impacted by global uncertainties; prices are less so.

Research on path planning of cleaning robot based on an improved ant colony algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

Possibility of implementing a cluster based on the territorial public self-government that includes sports, retailers and consumer services

The economic crisis has had a negative impact on the clustering processes. Nevertheless, in the current conditions there is a prospect of changing the very ideology of cluster models. It is very likely that the high impact of the volatility factor in the business environment will determine a qualitatively new format for the implementation of cluster initiatives. In most cases, the population of a particular territory is not directly involved in the process of creating a cluster but is considered as a labour force capable of achieving the goals of clustering. However, it is impossible to say that cluster projects cannot be initiated on the principle of “bottom-up development” where the population performs are not only the functions of “personnel” but also determines the feasibility of forming a project. This ensures open an equal access to the resources of absolutely all cluster participants and the population acts as an initiator, coordinator, investor and source of labour resources. In this regard, we think that it is appropriate and possible to consider the prospect of creating cluster (and similar ones) projects by the example of territorial public self-governments.

Optimisation and application research of ant colony algorithm in vehicle routing problem

In this paper, an improved ant colony algorithm based on ant system is proposed in order to solve vehicle routing problem. When choosing the path, the 2-opt method is used to explore the reasonable selection of the parameters of the algorithm for vehicle routing problem taking the path savings among customers as heuristic information. The performance of ant colony algorithm is affected by the information heuristic factor α, expectation heuristic factor β and pheromone volatile factor ρ. The method breaks through empirically setting the ant colony algorithm parameter values. By calculation, the optimal parameters of the ant colony algorithm in solving the vehicle routing problem are: α e [1.0, 1.7], β e [4.5, 8.5], ρ e [0.5, 0.6]. At last, an exploration is established to find the optimal solution by combining three parameters and the ant colony algorithm will have a better effect in the actual optimisation problem.

Lean manufacturing implementation and performance: the role of economic volatility in an emerging economy

PurposeThe purpose of this study is to explore the mediation effect of volatile economic conditions on performance benefits of successful kean manufacturing implementation (LMI). The mediating factor of economic volatility (EV) is constructed based on four macroeconomic dimensions – supplier uncertainty, market demand fluctuations, governmental policy changes and peer competition.Design/methodology/approachAn attempt is made to build an exhaustive list of the internal operational manifests grouped into one human and three technical input factors. Similarly the benefits accrued are collated under two performance measures – customer satisfaction (CS) and organizational goal satisfaction (OGS). Based on data from the Indian manufacturing sector, structural equation modelling (SEM) and ordinary least square (OLS) analyses are carried out to validate the proposed model.FindingsResults of the structural model validate the first six hypotheses posited in the model. Results of OLS further reveal the mediation effect of EV having negative impact on LMI–CS and LMI–OGS nexus.Practical implicationsThis research offers a fair understanding of the internal operational lean factors and the effect of volatile macroeconomic conditions on lean benefits. The structural model will aid the academicians and lean implementers comprehend the dimensional structure underlying the lean practices and beliefs. This work further helps to understand the moderation effect of environmental complexity on the output measures of LMI in the Indian manufacturing sector.Originality/valueThis work is one of the very first empirical analyses of lean performance under contingent economic conditions. The paper presents a valuable recommendation to practitioners for considering the dynamism of external economic environment instead of simply adopting standalone internal lean parameters, if satisfactory levels of performance in terms of CS and OGS are to be achieved.

Common Idiosyncratic Volatility and Carry Trade Returns

I provide new evidence that incomplete consumption risk sharing across countries is an important determinant of carry trade returns. I show that there is a strong co-movement in idiosyncratic volatilities over time, and that shocks to the common idiosyncratic volatility (CIV) factor, defined as the equally weighted average of the idiosyncratic volatilities in the cross-section, are priced. I find that high-interest rate currencies deliver low returns when the CIV increases, which are bad times for investors. Low-interest rate currencies provide a hedge by yielding positive returns. CIV shocks remain an empirically powerful risk factor in explaining the cross-section of carry trade returns after controlling for global foreign exchange (FX) volatility risk. Furthermore, CIV risk is correlated with cross-country income risk faced by households. My findings are consistent with a heterogeneous-agent model with persistent, uninsurable idiosyncratic shocks in consumption growth. The calibrated model quantitatively accounts for the cross-sectional differences in average returns across CIV-beta sorted portfolios for plausible market prices of CIV risk.

소상공인 사업변동성 요인과 사회안전망에 관한 연구

소상공인 사업변동성 요인과 사회안전망에 관한 연구

Factor Exposure Variation and Mutual Fund Performance

We investigated the relationship between a mutual fund’s variation in factor exposures and its future performance. Using a dynamic state-space version of the Carhart (1997) four-factor model to capture factor variations, we found that funds with volatile factor exposures underperform funds with stable factor exposures by 147 bps a year. This underperformance is explained neither by volatile factor loadings of a fund’s equity holdings nor by a fund’s forced trading through investor flows. We conclude that fund managers voluntarily attempt to time factors but are unsuccessful at doing so.

The Price of the Smile and Variance Risk Premia

Using a new specification of multifactor volatility, we estimate the hidden risk factors spanning S&P 500 index (SPX) implied volatility surfaces and the risk premia of volatility-sensitive payoffs. SPX implied volatility surfaces are well-explained by three dependent state variables reflecting (i) short- and long-term implied volatility risks and (ii) short-term implied skewness risk. The more persistent volatility factor and the skewness factor support a downward sloping term structure of variance risk premia in normal times, whereas the most transient volatility factor accounts for an upward sloping term structure in periods of distress. Our volatility specification based on a matrix state process is instrumental to obtaining a tractable and flexible model for the joint dynamics of returns and volatilities, which improves pricing performance and risk premium modeling with respect to recent three-factor specifications based on standard state spaces. This paper was accepted by Gustavo Manso, finance.

The Effect of Fleet Size, Seaborne Volume on BDI Volatility in Dry Bulk Shipping Market

Chen, L.F.; Xie, X.L., and Wang, W., 2020. The effect of fleet size, seaborne volume on BDI volatility in dry bulk shipping market. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 993–997. Coconut Creek (Florida), ISSN 0749-0208.In this article, a method based on variational mode decomposition (VMD) and multiple linear regression (MLR) is developed for Baltic Dry Index (BDI) volatility. The original BDI series is decomposed into several independent intrinsic mode functions (IMFS) through VMD first. The long-term trend is obtained to represent the basic freight rate. MLR model is used to analyze the influencing factors of BDI volatility. It is found that seaborne volume is the main cause of the volatility of BDI when the shipping market is in equilibrium. If the market is in a recession, the volatility of BDI is mainly determined by fleet size. Oil price, GDP and CIP have a very small impact on the volatility of BDI. The results contribute to understand the systematic risk of shipping market.

An interactive dispatching strategy for micro energy grids considering multi-energy flexible conversion based on the three-level optimization perspective

The study considered the optimization problem of micro energy grids (MEGs) participating in a bidding game with a utility energy grid. To begin, multi-level bidding game frameworks made of multi-MEGs were designed at different stages (day-ahead, intra-day, and real-time), and a three-stage optimization model with multiple game status was present in this paper. Secondly, the paper proposed an improved ant colony optimization algorithm based on the adaptive adjustment of pheromone volatile factor and transfer probability. The IEEE37 node distribution system and the eight-node natural gas system are selected for example analysis. The results show that: (1) the proposed multi-level bidding game strategy can establish the optimal scheme of multi-MEGs operation at different stages of day-ahead, intra-day, and real-time; (2) the improved ant colony optimization can consider the differences of the cooperative game and non-cooperative game among different entities, balance the multi-objective appeals of energy supply cost, output fluctuation of a wind power plant (WPP) and photovoltaic power (PV), bidding revenue, and reserve cost, which could help obtain the global optimum solution; (3) the price-based demand response is more effective in converting renewable power into different forms of energy, and in participating in bidding transactions with the utility energy grid. A more liberalized energy market will bring higher bidding revenue for multi-MEGs.

Host and Pathogen Communication in the Respiratory Tract: Mechanisms and Models of a Complex Signaling Microenvironment.

Chronic lung diseases are a leading cause of morbidity and mortality across the globe, encompassing a diverse range of conditions from infections with pathogenic microorganisms to underlying genetic disorders. The respiratory tract represents an active interface with the external environment having the primary immune function of resisting pathogen intrusion and maintaining homeostasis in response to the myriad of stimuli encountered within its microenvironment. To perform these vital functions and prevent lung disorders, a chemical and biological cross-talk occurs in the complex milieu of the lung that mediates and regulates the numerous cellular processes contributing to lung health. In this review, we will focus on the role of cross-talk in chronic lung infections, and discuss how different cell types and signaling pathways contribute to the chronicity of infection(s) and prevent effective immune clearance of pathogens. In the lung microenvironment, pathogens have developed the capacity to evade mucosal immunity using different mechanisms or virulence factors, leading to colonization and infection of the host; such mechanisms include the release of soluble and volatile factors, as well as contact dependent (juxtracrine) interactions. We explore the diverse modes of communication between the host and pathogen in the lung tissue milieu in the context of chronic lung infections. Lastly, we review current methods and approaches used to model and study these host-pathogen interactions in vitro, and the role of these technological platforms in advancing our knowledge about chronic lung diseases.