Volatility Uncertainty Research Articles

VUCA Approach towards Supply Chain

Volatility, Uncertainty Complexity and Ambiguity are the words increasingly used in the supply chain in the current business environment, and the impact it has on business performance in the supply chain is numerous towards improvement. VUCA the terms became synonymous, in supply chain, thus became increasingly important to the leaders in supply chain, to seek the business to operate efficiently and effectively in supply chain. Supply chain is predictable, and today the global market is full of volatile uncertainty and ambiguity, as organizations have to be carefully strategized and be ready for changes in the market and keep the supply chain moving forward.

Robust utility maximization of terminal wealth with drift and volatility uncertainty

ABSTRACT We give explicit solutions for utility maximization of terminal wealth problem in the presence of Knightian uncertainty in continuous time . We assume there is uncertainty on both drift and volatility of the underlying stocks, which induce nonequivalent measures on canonical space of continuous paths Ω. We take that the uncertainty set resides in compact sets that are time dependent. In this framework, we solve the robust optimization problem with logarithmic, power and exponential utility functions, explicitly. Numerical simulations revealing the effects of uncertainty on the dynamics are also presented.

Modeling and Prediction of the Volatility of the Freight Rate in the Roadway Freight Market of China

The trucking sector is an essential part of the logistic system in China, carrying more than 80% of its goods. The complexity of the trucking market leads to tremendous uncertainty in the market volatility. Hence, in this highly competitive and vital market, trend forecasting is extremely difficult owing to the volatility of the freight rate. Consequently, there is interest in accurately forecasting the freight volatility for truck transportation. In this study, to represent the degree of variation of a freight rate series in the trucking sector over time, we first introduce truck rate volatility (TRV). This investigation utilizes the generalized autoregressive conditional heteroskedasticity (GARCH) family of methods to estimate the dynamic time-varying TRV using the real trucking industry transaction data obtained from an online freight exchange (OFEX) platform. It explores the ability of forecasting with and without reestimation at each step of the conventional GARCH models, a neural network exponential GARCH (NN-EGARCH) model, and a traditional forecasting technique, the autoregressive integrated moving average (ARIMA) approach. The empirical results from the southwest China trucking data indicate that the asymmetric GARCH-type models capture the characteristics of the TRV better than those with Gaussian distributions and that the leverage effects are observed in the TRV. Furthermore, the NN-EGARCH performs better in in-sample forecasting than other methods, whereas ARIMA performs similarly in out-of-sample TRV forecasting with reestimation. However, the Diebold–Mariano test indicates the better forecasting ability of ARIMA than the NN-EGARCH in the out-of-sample periods. The findings of this study can benefit truckers and shippers to capture the tendency change of the market to conduct their business plan, increase their look-to-buy rate, and avoid market risk.

Transport Plans with Domain Constraints

This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex transport constraints in addition to having given initial and terminal marginals. Several applications are provided: martingale measures with volatility uncertainty, optimal transport with capacity constraints, and Skorokhod embedding with bounded times. Next, we extend this result to multi-marginal constraints. Finally, we consider an optimal transport problem with constraints and obtain its Kantorovich duality. A corollary of this result is a monotonicity principle which gives a geometric way of identifying the optimizer.

Critical Review: Current Research Issues on Crypto-currency and its Application in Financial Sectors

This research article is based on the current research issues on crypto-currency and its application in financial sectors. The researcher stated that crypto- currency attracted a significant attention and has been adopted in numerous applications, such as smart grid and Internet of Things (IoT) have a high scalability barrier for block-chain which limits its ability to support services with frequent transactions. In this research paper the researcher emphasized that some of the significant research issues on decentralized, peer to peer financial networks face risks such as fraud and suspicious trading, volatile price fluctuations, regularity uncertainty, and instability in financial sectors. This research study shows the huge growth of online users, virtual word concept which are created a new business phenomenon to facilitate financial activities such as buying, selling and trading. This research article analyzed the user’s expectation of future of crypto-currency in financial sectors at different significant level. In addition to this the researcher also discussed a statistical report which is adopted from the Statistic to provide a future direction of money regulatory in financial sector.

Critical Review: Current Research Issues on Crypto-currency and its Application in Financial Sectors

This research article is based on the current research issues on crypto-currency and its application in financial sectors. The researcher stated that crypto- currency attracted a significant attention and has been adopted in numerous applications, such as smart grid and Internet of Things (IoT) have a high scalability barrier for block-chain which limits its ability to support services with frequent transactions. In this research paper the researcher emphasized that some of the significant research issues on decentralized, peer to peer financial networks face risks such as fraud and suspicious trading, volatile price fluctuations, regularity uncertainty, and instability in financial sectors. This research study shows the huge growth of online users, virtual word concept which are created a new business phenomenon to facilitate financial activities such as buying, selling and trading. This research article analyzed the user’s expectation of future of crypto-currency in financial sectors at different significant level. In addition to this the researcher also discussed a statistical report which is adopted from the Statistic to provide a future direction of money regulatory in financial sector.

Empirical Asset Pricing with Functional Factors

We propose a methodology to use functional factors in empirical asset pricing models. The term structure of interest rates and the implied volatility smile are just two common examples. Functional factors usually incorporate investors risk preferences that could be priced in the cross-section of stock returns. We derive a theory for pricing functional factors that encompasses the usual one for scalar valued factors. We then provide estimation algorithms for betas, risk prices and risk premia and show that they are asymptotically Gaussian and suggest the bootstrap as method to carry out inference. We apply our approach to extract additional information embedded in the implied variance curve. We argue that changes in the shape of the implied variance curve provide information about fear and uncertainty and we distinguish between the two. We show that convexity can be seen as a proxy for volatility uncertainty. This uncertainty could explain the premium earned by momentum strategies, which is seen as pricing anomaly in literature. The empirical results show that the S&P500 3-month implied variance curve is a pricing factor for momentum sorted portfolios and a positive risk premium is earned by the convexity of the implied variance curve.

The impact of uncertainty on the macro-financial linkage with international financial exposure

This paper investigates the international transmission of various sources of uncertainty between the financial sector and the real economy. I focus on calibrating a two-country DGSE model with an international financial exposure in the banking sector for the Euro Area (EA) and the United States (US). In particular, the model studies the transmission mechanism of macroeconomic, monetary policy, financial and stock market volatility uncertainty. The model assumes that uncertainty originates in the foreign economy (US), and studies the spillover effect to the domestic economy (EA). The international transmission mechanism is driven by the international financial exposures, where domestic banks hold foreign banks’ assets as a second market activity. The analysis suggests that the spillover effects from foreign uncertainty have in many cases a sizeable and persistent impact on output, lending and asset prices. In particular, I find that macroeconomic and monetary policy uncertainty drive a global business cycle, where uncertainty leads to a drop in home and foreign GDP.Finally, the paper investigates the effectiveness of macroprudential policy when there is higher uncertainty about the policy rate in a foreign economy. Results show that both domestic and cooperative regulations, where both countries implement caps to the loan-to-value ratio, are enable to reduce the negative impact on GDP that such a shock produces.

Effective Stochastic Volatility: Applications to ZABR-type Models

There are numerous models for specifying the uncertainty of future instantaneous volatility or variance, including the Heston, SABR and ZABR models. Often it is observed that a specific stochastic volatility model is chosen not for particular dynamical features, relevant for exotic payoff structures, but instead for convenience and ease of implementation. The SABR model, with its semi-closed form approximate solution for the prices of vanilla options, is a well-known example. In this article, we consider a general approach that includes all practically relevant stochastic volatility models and introduces new variants of the ZABR model. In particular, we consider the mean-reverting ZABR and free ZABR models. We use the method of deriving an effective partial differential equation for the density. This approach leads to the known approximation formula for the SABR model, but also provides expressions for arbitrage-free models. Numerical experiments illustrate our approach.

Changes in Market Uncertainty and Profitability of Idiosyncratic Volatility Anomaly in Korea

Purpose - The idiosyncratic volatility (IVOL) puzzle is the anomaly that lower IVOL stocks earn higher future returns. We examine if global market uncertainty and market fear affect idiosyncratic volatility anomaly profits in Korea. Design/methodology/approach - Change in market uncertainty and investor fear is measured by change in the volatility index (VIX). Stocks are sorted into quintile portfolios based on their IVOLs in each month. To measure the anomaly profit, the return spread between two extreme portfolios is computed. Risk-adjusted returns are also calculated relative to capital asset pricing model (CAPM) and the Fama and French model. Then, we compare the difference of the IVOL investing profits in the decreasing and increasing VIX periods. Findings - We find that in the months following a decrease in VIX, stocks with low IVOLs earn 2.5% higher profits than stocks with high IVOLs. However, no such anomaly arises in the months following an increase in the VIX. Research implications or Originality - This result implies that mispricing is more significant when investors’ fears regarding market uncertainty are abated. Also, investors can obtain the IVOL anomaly returns by buying low IVOL stocks when the VIX decreases and selling them the following month. This finding suggests that the Korean stock market is integrated with the global market.

Estimating the connectedness of commodity futures using a network approach

AbstractUsing a network approach of variance decompositions, we measure the connectedness of 18 commodity futures and characterize both static and dynamic connectedness. Our results show that metal futures are net transmitters of shocks to other futures, and agricultural futures are vulnerable to shocks from the others. Furthermore, almost two‐thirds of the volatility uncertainty for commodity futures are due to the connectedness of shocks across the futures market. Dynamically, we find connectedness always increases in times of turmoil. An analysis of connectedness networks suggests that investors could be forewarned that the connectedness of various classes of futures could threaten their portfolios.

Technology for Sustainable HRM: An Empirical Research of Health Care Sector

In a highly competitive world of today characterised by VUCA (Volatility Uncertainty Complexity and Ambiguity) environment organizations are striving to achieve excellence with standard business practices. Given the international mantra of cost quality & service companies need to adopt technology in a proactive manner by collaborating with IT department. Traditionally HR and IT have operated as distinct units. But with the changing times there is an urgent need to adopt technology for improving the productivity of human resources thereby contributing to the sustainable organizational development. So a close collaboration between these two departments is called for. Some of the HR professionals assume that technology will fix all their problems i.e. By moving to cloud the outdated HR operating model and disconnected data sourcing issues will get resolved. But HR professionals will be committing a grave mistake if they think that the technology will be a panacea to all HR problems. In other words, the key is about how the technology is applied and not the acquisition of technology. In the prevailing competitive environment there is every need to understand appreciate the behaviour of the employees so that required initiatives could be taken for obtaining outstanding performance from these valuable human resources. In this regard technological tools like HR Analytics(HRA) Artificial Intelligence (AI) come very handy for getting valuable insights into human behaviour. Further application of these tools helps in effective decision making thereby contributing to the accomplishment of organization goals. Application of HRA and AI apart from facilitating decision making also helps in integrating Human Resource with other business activities. The paper focusses on understanding how HR analytics helps in sustainable human resource management by providing insights into elementary HR processes and behaviours. It also correlates well with current HRA/AI trends in general and health care sector in particular. Needless to say this will be a ready reference for any future study into role of data analytics/Artificial intelligence in Human Resource Management.

Unspanned Risks, Negative Local Time Risk Premiums, and Empirical Consistency of Models of Interest-Rate Claims

We formalize the notion of local time risk premiums in the context of a theory in which the pricing kernel is a general diffusion process with spanned and unspanned components. We derive results pertaining to expected excess returns of options on bond futures. These results are organized around our finding that average returns of out-of-the-money puts and calls on bond futures are both negative, the consistency of which is examined through unconditional and conditional exercises. Our theoretical reconciliation warrants negative local time risk premiums, and our treatment considers models with market incompleteness, volatility uncertainty, and time-varying sign of futures risk premium.

The PDEs and Numerical Scheme for Derivatives under Uncertainty Volatility

We use the stochastic differential equations (SDE) driven by G-Brownian motion to describe the basic assets (such as stocks) price processes with volatility uncertainty. We give the estimation method of the SDE’s parameters. Then, by the nonlinear Feynman-Kac formula, we get the partial differential equations satisfied by the derivatives. At last, we give a numerical scheme to solve the nonlinear partial differential equations.

On properties of solutions to Black\u2013Scholes\u2013Barenblatt equations

This paper is concerned with the Black–Scholes–Barenblatt equation partial _{t}u+r(xpartial _{x}u-u)+G(x^{2}partial _{xx}u)=0, where G(alpha )=frac{1}{2}(overline{sigma}^{2}-underline{sigma}^{2})|alpha |+frac{1}{2}(overline{sigma}^{2}+underline{sigma}^{2})alpha , alpha in mathbb{R}. This equation is usually used for derivative pricing in the financial market with volatility uncertainty. We discuss a strict comparison theorem for Black–Scholes–Barenblatt equations, and study strict sub-additivity of their solutions with respect to terminal conditions.

Can uncertainty indices predict Bitcoin prices? A revisited analysis using partial and multivariate wavelet approaches

This paper uses bivariate and multivariate wavelet approaches to revisit the co-movements between the Volatility Uncertainty Index (VIX) and Bitcoin (BTC). This is achieved by accounting for the impacts of the three major global factors, namely the U.S. Economic Policy Uncertainty Index (EPU), the Crude Oil Volatility Index (OVX), and the Geopolitical Risk Index (GPR). To do this, we use Wavelet Coherence (WC), Cross Wavelet Transform (CWT), Power Wavelet Coherence (PWC), and Multiple Wavelet Coherence (MWC) approaches. The results show that the BTC-VIX relationship varies across time and at high and low frequencies. Moreover, we find positive (or in-phase) co-movements between both variables while a negative co-movement (out-of-phase) is observed at both high and low frequencies. VIX news has a prediction power on BTC price returns over different frequencies. The results of PWC and MWC show that OVX, EPU, and GPR factors affect the BTC-VIX nexus under different frequencies. Finally, correlations between BTC-uncertainty indices are dependent upon investment horizons. The results of our research are of interest and importance to investors, portfolio managers, and policy-makers, as the results have practical applications to inform their decision-making.

Market delay and [formula omitted]-expectations

We study super-replication of contingent claims in markets with delayed filtration. The first result in this paper reveals that in the Black–Scholes model with constant delay the super-replication price is prohibitively costly and leads to trivial buy-and-hold strategies. Our second result says that the scaling limit of super-replication prices for binomial models with a fixed number of times of delay H is equal to the G-expectation with volatility uncertainty interval [0,σH+1].

Volatility Uncertainty Quantification in a Stochastic Control Problem Applied to Energy

This work designs a methodology to quantify the uncertainty of a volatility parameter in a stochastic control problem arising in energy management. The difficulty lies in the non-linearity of the underlying scalar Hamilton-Jacobi-Bellman equation. We proceed by decomposing the unknown solution on a Hermite polynomial basis (of the unknown volatility), whose different coefficients are solution to a system of non-linear PDEs of the same kind. Numerical tests show that computing the first basis elements may be enough to get an accurate approximation with respect to the uncertain volatility parameter. We experiment the methodology in the context of swing contract (energy contract with flexibility in purchasing energy power), this allows to introduce the concept of Uncertainty Value Adjustment (UVA), whose aim is to value the risk of misspecification of the volatility model.

Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

High-dimensional partial differential equations (PDE) appear in a number of models from the financial industry, such as in derivative pricing models, credit valuation adjustment (CVA) models, or portfolio optimization models. The PDEs in such applications are high-dimensional as the dimension corresponds to the number of financial assets in a portfolio. Moreover, such PDEs are often fully nonlinear due to the need to incorporate certain nonlinear phenomena in the model such as default risks, transaction costs, volatility uncertainty (Knightian uncertainty), or trading constraints in the model. Such high-dimensional fully nonlinear PDEs are exceedingly difficult to solve as the computational effort for standard approximation methods grows exponentially with the dimension. In this work we propose a new method for solving high-dimensional fully nonlinear second-order PDEs. Our method can in particular be used to sample from high-dimensional nonlinear expectations. The method is based on (i) a connection between fully nonlinear second-order PDEs and second-order backward stochastic differential equations (2BSDEs), (ii) a merged formulation of the PDE and the 2BSDE problem, (iii) a temporal forward discretization of the 2BSDE and a spatial approximation via deep neural nets, and (iv) a stochastic gradient descent-type optimization procedure. Numerical results obtained using ${\rm T{\small ENSOR}F{\small LOW}}$ in ${\rm P{\small YTHON}}$ illustrate the efficiency and the accuracy of the method in the cases of a $100$-dimensional Black-Scholes-Barenblatt equation, a $100$-dimensional Hamilton-Jacobi-Bellman equation, and a nonlinear expectation of a $ 100 $-dimensional $ G $-Brownian motion.

The power of ethical investment in the context of political uncertainty

ABSTRACTIn this paper we analyse a set of socially responsible investment (SRI) indices against their conventional counterparts in the US context. Using a data set that spans the Obama and Trump administrations, we aim to identify whether performance and volatility patterns differ when markets are exposed to political uncertainty and the Global Financial Crisis (GFC). The findings suggest that SRI indices underperform conventional indices, and that the S&P 500 has a significant impact on their behaviour. The CBOE’s Volatility Index (VIX), the US Equity Related Economic Uncertainty Index (EEUi) and the impact of the economic policy uncertainty index (EPUi) are used to consider market volatility and political uncertainty, with VIX emerging as the best indicator to capture market uncertainty. The study signals a positive and significant impact on SRI indices during the first hundred days of the Obama administration with a lack of significant findings for the Trump administration for the period of study. The results for implied volatility reveal similar patterns across all indices.