Options Exchange Research Articles

An analytical approach to the pricing of an exchange option with default risk under a stochastic volatility model

The exchange option, which has two correlated underlying assets, is one of the most popular exotic options in the over-the-counter markets. This paper studies the valuation of exchange options with default risk of option issuer, where default is allowed only at maturity. Moreover, we consider three underlying assets with stochastic volatilities and assume that fast mean-reverting processes determine the stochastic volatilities. Based on the partial differential equation approach, we derive the analytical pricing formula of the exchange option price with default risk using the asymptotic expansion. To verify the accuracy and efficiency of our pricing formula, we compare the results by our pricing formula with those by Monte Carlo simulation, which is considered a benchmark. In addition, we provide several graphs to illustrate the properties of the option for significant parameters.

Options trading strategies for the price coverage of the algerian oil barrel sahara blend

This paper focuses on the problem of hedging the risk of financial loss generated by the fall in the price of the Algerian oil barrel Sahara Blend, with the aim of proposing an approach allowing the reduction of financial losses, following unfavorable movements in oil prices. After a reminder on the strategic importance of the energy sector for the Algerian economy, we will evaluate the derivative products using different models. We present some option hedging strategies. Our results indicate that the use of options would effectively cushion a possible drop in the price of Sahara Blend, provided that the choice of the option exchange strategy is optimal. The study covers the period from 12/31/2019 to 06/30/2021, and the data is available on the websites www.oilprice.com and www.fred.stlouisfed.org. Finally, the results indicate that taking into account the cost of hedging and gains at maturity, bear spread and traddle strategies remain the most optimal.

Forecasting stock indices with the COVID-19 infection rate as an exogenous variable.

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. In this study, the effect of covariates has been analyzed for three stock indices: S & P 500, Morgan Stanley Capital International (MSCI) world stock index, and the Chicago Board Options Exchange (CBOE) Volatility Index (VIX). Results show that parametric approaches can be good forecasting models for the S & P 500 index and the VIX index. On the other hand, a random walk model can be adopted to forecast the MSCI index. Moreover, among the three random walk forecasting methods for the MSCI index, the naïve method provides the best forecasting model.

Global financial stress index and long-term volatility forecast for international stock markets

In this study, we examine the long-term predictive role of the global financial stress index (GFSI) on equity market volatility and provide a comprehensive analysis using GFSI for the realized volatilities of 21 international stock indices. By focusing on the out-of-sample analysis, we show that GFSI has strong predictive information in forecasting the long-term realized volatilities for most of these equity indices, and it performs better than the Chicago Board Options Exchange volatility index (VIX), the United States economic policy uncertainty (USEPU), global economic policy uncertainty (GEPU), and geopolitical risk (GPR). In terms of the predictive performance during the COVID-19 pandemic, we further show the significantly effective role of GFSI for the long-term realized volatilities of equity markets. In dealing with the high-level global financial stress, our study helps policymakers from many countries to prevent large market fluctuations and decrease economic damage, and facilitate market participants to form better risk-aversion investment strategies.

Dampening Global Financial Shocks: Can Macroprudential Regulation Help (More than Capital Controls)?

AbstractWe show that macroprudential regulation significantly dampens the impact of global financial shocks on emerging markets. Specifically, a tighter level of regulation reduces the sensitivity of GDP growth to capital flow shocks and movements in the Chicago Board Options Exchange's VIX. A broad set of macroprudential tools contributes to this result, including measures targeting bank capital and liquidity, foreign currency mismatches, and risky credit. We also find that tighter macroprudential regulation allows monetary policy to respond more countercyclically to global financial shocks. This could be an important channel through which macroprudential regulation enhances macro‐economic stability. We do not find evidence that capital controls provide similar benefits.

The volatility index and volatility risk premium in China

We introduce the China Volatility Index (CNVIX), a model-free volatility index for the Chinese equity market based on ETF options. To construct the CNVIX, we extend the Chicago Board Options Exchange (CBOE) methodology in the emerging Chinese options market. We examine the leverage effect and volatility feedback effect between the CNVIX and the underlying asset, as well as the CNVIX’s return forecastability. Our findings indicate a significant negative asymmetric leverage effect, insignificant volatility feedback effect in the CNVIX, and a positive mean volatility risk premium (VRP), which can forecast the underlying asset’s returns over various horizons.

S&P 500 volatility, volatility regimes, and economic uncertainty

AbstractWe assess the relationship between regime‐dependent volatility in S&P 500, economic policy uncertainty, the S&P 500 bull and bear sentiment spread (bb_sp), as well as the Chicago Board Options Exchange's VIX over the period 2000–2018. Our findings from two‐covariate GARCH–MIDAS (GM) methodology, regime switching Markov Chain, and quantile regressions suggest that the association of realized volatility and sentiment varies across high‐ and low‐volatility regimes and depends on investors’ sensitivity toward incidents of market uncertainties under these regimes. The findings suggest that these indicators may not be useful in volatility forecasting, especially under high‐volatility regimes.

Drivers and barriers to knowledge exchange through an envisioned online platform for transdisciplinary research projects

Transdisciplinary research requires improved knowledge exchange between science and practice. Such improvements include diversifying and scaling up knowledge accessing and sharing through online platforms. We conducted twenty interviews informed by behavioral science methods to clarify the aim, components, and participants' perspectives on the usefulness of the proposed components for an envisioned platform. Participants were members of a Dutch community of practice for river studies and a research programme into integrated and collaborative management. The proposed concept included storylines, data repositories, user profiles, interactive visualisations, and collaborative sessions. Interview results include drivers and barriers from prospective users that we translated into requirements to increase the potential adoption and effective use of online platforms with similar components. From the experiences with implementing these requirements, we provide recommendations for enabling primary drivers: (i) Combining online and offline interactions to provide various options for knowledge exchange between disciplines and organisations. (ii) Sharing the content and application of the research with a non-scientific audience. (iii) Reusing existing online platforms as much as possible without restricting any to improve the reuse of research methods and results. We further provide recommendations to overcome the main barriers: (i) Partnering with various communities to extend knowledge exchange. (ii) Following a participatory approach to improve the design and content while considering the time and resources that such a process entails. (iii) Providing flexible options to contribute and tailor overviews of available knowledge in different ways according to prospective users' roles in practice. (iv) Purposefully facilitating online interactions according to the transdisciplinary process-intended attributes.

A three-layer game theoretic-based strategy for optimal scheduling of microgrids by leveraging a dynamic demand response program designer to unlock the potential of smart buildings and electric vehicle fleets

The proliferation of the number of Smart Buildings (SBs) and the fleet of Electric Vehicles (EVs) in Distribution Systems (DSs) makes the need for new strategies to coordinate them with microgrid (MG) scheduling inevitable. Therefore, this article proposes a three-layer risk-averse game theoretic-based strategy to coordinate SBs and EV fleets with MGs scheduling. In the first layer of this strategy, a Demand Response Program (DRP) is designed for SBs where dynamic incentive tariffs are calculated based on the consumption pattern of subscribers. Then, in the second layer, the scheduling of SBs and EV fleets is done in a decentralized space and considering their participation in the designed DRP. Eventually, in the third layer, the operators of MGs have received the power exchange information of SBs in order to carry out their scheduling in accordance with it. In this layer, the Day-Ahead (DA) scheduling of MGs and DS is done through the implementation of a cooperative game theory. Fluctuations of uncertain operational parameters such as load demand, radiation and wind are embedded in the model by scenario-based technique where a Risk-Averse (RA) strategy is adopted to manage them. Running the proposed three-layer strategy on a 69-node DS containing four MGs showed that this strategy can use the potential of SBs and EV fleets to improve the voltage characteristics in the high-demand period and reduce total daily costs by 13.66% with designing a dynamic-tariff DRP. Moreover, the results reveal that MGs using the Peer-to-Peer (P2P) power exchange option have not only reduced the power losses in the system but also reduced the total daily costs by about 8%.

Isotope kinetics modeling in a circulating fuel system: a case study of the MBIR reactor loop

The paper presents the results of modeling of changes in the isotopic composition of the liquid-salt fuel circulating in the experimental channel of the MBIR reactor facility. The authors tested the ISTAR software environment adapted for solving burnup equations in problems with variable power levels. The loop channel parameters, including two heat exchanger options, were estimated to obtain the appropriate salt transit time through the loop channel zones. Two problems of a circulating fuel system (loop) modeling are considered, namely: (1) modeling the equilibrium salt isotope composition in such a system; and (2) developing a technique for modeling nonstationary isotope kinetics in the MBIR reactor loop. Non-stationary isotope kinetics can be modeled as sequential burnup of nuclides in the neutron field and decay during movement in the external circuit. The authors also developed an algorithm for modeling changes in the isotopic composition of fuel salt during its circulation, taking into account the sequential transfer of a given salt volume from the burnup zone to the zone outside the reactor core. Based on this algorithm, a software package was created using the Python 3.9 programming language and ISTAR modules. In addition, a description of the calculation methodology was given and some calculation results obtained using the software were presented. In the process of working with the program, it was found that, for the given times of the fuel being in each of the zones (2 and 200 seconds, respectively), modeling the change in the isotopic composition during the fuel campaign (500 days) will require the calculation of more than 500 thousand steps. In order to save time, it is necessary to find out whether it will be possible to reduce the number of calls to the neutronic calculation code due to a slight change in the isotopic composition of the fuel in the loop per one burnup step. Work is currently underway to optimize this process.

The Impact of Gold, Oil Prices, and their Associated Implied Volatilities on Performance of Pakistan’s Stock Market

This study examined the asymmetrical implications of gold and oil prices, as well as the implied volatility that goes along with them, on the performance of Pakistan's stock market. It was used Chicago Board of Options Exchange’s GVZ and OVX index for measuring volatility in gold market and oil market respectively due to more uncertainty in commodity markets and also took historical price movements, investor’s expectations regarding global prices into account. The monthly data used from October 2009 to January 2023. An ARDL approach was employed for checking short term and long term asymmetrical. The results of ARDL revealed that oil volatility index have significant impact of Pakistan’s stock market in short-run and long-run. The Gold Volatility Index shows significant impact after one month on both stock market capitalization and KSE 100 indexes in the short run. Fluctuations in worldwide commodity prices have a significant influence on underdeveloped economies like Pakistan and have a negative effect on the entire economy. The volatility and fluctuations in oil prices lead to higher production costs, which create uncertainty in companies' cash flows.

Do the macro and global economic factors drive the nonperforming loans in GCC economies?

Purpose The current study aims to investigate the determinants of nonperforming loans (NPLs) in the GCC economies during the period spanning 2000 to 2018. It also examines whether the worldwide financial crisis of 2007–2008, which brought the issue of non–performing loans to the greater attention of academics and policymakers, had a substantial impact on NPLs in this region. Design/methodology/approach The sample consists of 53 conventional banks from GCC countries, and the basic data for the study is obtained from various sources such as Bankscope, IMF World Economic Outlook, World Bank and Chicago Board of Options Exchange Market Volatility Index. The estimations were done by dynamic panel data regression modeling using system generalized methods of moments. Findings The findings reveal that both, the non-oil real GDP growth rate and inflation have favorable effects on NPLs. On the other hand, domestic credit to the private sector and the volatility index have an adverse effect on NPLs. Furthermore, the period-wise analysis shows that the relevance and significance of the determinants of NPLs vary between the precrisis and postcrisis periods. It is also reflected through the intercept dummy, which is found to be significant, indicating that the financial crisis, as a global economic factor, had a significant impact on NPLs. A number of robustness tests are applied, which indicate that the results are mostly robust and consistent in terms of the significance of the explanatory variables and the direction of their relationship with the dependent variable. Practical implications Policymakers and bank authorities must strive to maintain a healthy economy and implement macroprudential policies to improve the financial stability of banks and reduce credit risk. Originality/value To the best of the authors’ knowledge, this is likely the first study that empirically investigates the influence of the financial crisis on NPLs in the context of GCC economies. In addition, the research spans 19 years to produce more conclusive results.

Pricing Kernels and Risk Premia implied in Bitcoin Options

Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class.

Examination of the impacts of the immediate interest rate of the United States and the VIX on the Dow Jones Islamic Market Index

AbstractThis study examines the effects of the market volatility index of the Chicago Board Options Exchange (VIX) and the immediate interest rate of the United States on the Dow Jones Islamic Market Index (DJIMI) using quantile‐based techniques and wavelet coherence (WTC) analysis with monthly data for the period January 2010to May 2021. A quantile cointegration model indicated that the relationship between the VIX and the DJIMI can be valid in the long term since the estimated coefficients are negative and statistically significant across the quantiles 0.05 and 0.50, while a quantile autoregressive model revealed that large negative and positive changes in the VIX and the immediate interest rate of the United States do not have a significant impact on the DJIMI in the short term. Allowing the role of regime changes, it was found by the quantile regression model that an increase in the VIX lowers the performance in the DJIMI, supported by the WTC. It was also underlined that the DJIMI may not benefit from the positive financial conditions. According to the quantile regression models, the immediate interest rate of the US has asymmetrical effects, and the stabilizing effect of the increase/decrease is valid during bearish/bullish market conditions in the DJIMI.

Valuing Exchange Options under an Ornstein-Uhlenbeck Covariance Model

In this paper we study the pricing of exchange options between two underlying assets whose dynamic show a stochastic correlation with random jumps. In particular, we consider a Ornstein-Uhlenbeck covariance model, with Levy Background Noise Processes driven by Inverse Gaussian subordinators. We use expansions in terms of Taylor polynomials and cubic splines to approximately compute the price of the derivative contract. Our findings show that the later approach provides an efficient way to compute the price when compared with a Monte Carlo method, while maintaining an equivalent degree of accuracy.

Inverse volatility problem for currency options

In transactions associated with future-oriented financial instruments, such as options, a huge amount of data is available buried inside of which is the market's best guess as to what the future holds. We consider here the possibility of extracting future foreign exchange volatility information from foreign exchange option data with the aid of a new computational inverse algorithm using minimization of a convex functional.
 See also https://ejde.math.txstate.edu/special/02/k1/abstr.html

The Impact of Exchange Rate Fluctuation on Toyota Financial Performance and Its Hedging Strategy

In today's globalised economy, the majority of businesses operate in many nations, utilising foreign exchange for imports and exports to expose them to foreign exchange risk. Every business is more or less affected by foreign exchange risk, which is made up of transaction risk, translation risk, and economic risk. By analyzing Toyota's financial reports and comparing financial data of multiple fiscal years (2018 is the base year), including Toyota's derivatives trading volume, foreign exchange income, exchange rate fluctuations of Japanese yen relative to other major currencies, and the average transaction price of currency trading, this study studied the role of financial derivatives in Toyota's foreign exchange risk management. The research results show that Toyota's derivatives transactions in 2018, including forward foreign exchange future contracts and foreign exchange options, strongly offset the foreign exchange risk caused by the appreciation of the yen against the US dollar and the euro. In addition to the derivatives transactions, Toyota also used other risk management policies to reduce its foreign exchange risk. Although the use of financial derivatives can hedge risks, but it will also bring losses to the enterprise. In today's volatile global business environment, each enterprise must choose the appropriate risk management tools for it.

Exchange options with stochastic liquidity risk

In this article, we account for the liquidity risk in the underlying assets when pricing European exchange options, which has not been considered in the literature. An Ornstein–Uhlenbeck process with the mean-reversion property is selected to model the market liquidity risk, whose impacts on the underlying assets are assumed to be imposed with a liquidity discount factor. Under this framework, we develop a simplified approach to obtain the pricing formula, a distinguishing feature of which is that it does not require any numeraire change. We first transform the exchange option’s pricing PDE into a pricing PDE of the European vanilla option, which is then solved in closed-form using the characteristic function approach. Finally, we present accuracy tests and sensitivity analysis to demonstrate the correctness of the formula and the influence of introducing the liquidity risk on exchange options, respectively.

War build-up and stock returns: evidence from Russian and Ukrainian stock markets

PurposeThis study examined the impact of war/conflict-related news on the Russian and Ukrainian stock markets in the build-up and beginning of the war that sparked in the year 2022.Design/methodology/approachIn order to examine the impact of war-related news on stock returns, data were gathered from the United States (US) and Russian stock indices, oil price and volatile index (VIX) from Yahoo.finance; Ukrainian stock values from pfts.ua website and daily related news retrieved from nexis.com were analysed. The data were gathered from January 1, 2022 to February 24, 2022. Seeming unrealated regressions (SUR) and exponential generalised autoregressive conditional heteroscedastic (EGARCH) models were carried out to determine the formulated correlations. This study controlled the oil price, US stock returns, Chicago Board Options Exchange (CBOE) VIX and difference in stock returns of Russia and Ukraine.FindingsThe results are presented two-fold: first, war-related news between the two countries enhanced volatility and caused a significant decline in the stock market indices for both countries. Second, the Russian stock market faced a steeper decline in the build-up and the actual beginning of the war than the Ukrainian stock market. Notably, the Russian markets feared the adverse economic consequences that stemmed from the sanctions the US and the Western world imposed.Research limitations/implicationsAs this study was based on early evidence, future studies with a longer window may provide better insights. This present study is restricted to the stock returns of the countries directly involved in the build-up towards war. Studies focusing on the impact of other asset classes, currencies, commodities and global stock markets might offer holistic insights.Practical implicationsThe study outcomes suggest that global portfolio investors should stay away from stock markets of the war-raged countries and equity markets in general, but instead look for safe-haven assets.Originality/valueThe paper evaluates stock markets' performance during the pre-war period, considering the context of this historical war between the neighbours. It is important to understand this issue as this war is subject to sanctions by the US and leads to a global supply chain crisis.

The role of the COVID-19 pandemic in US market volatility: Evidence from the VIX index

We examine how the implied volatility in the US financial market has been affected by the COVID-19 pandemic. We decompose the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) into two implied volatility conditions (i.e., low and high), and COVID-19 pandemic cases and deaths into two categories (i.e., low and high). Our novel quantile-on-quantile regression approach allows us to better examine the dynamic relationship between the COVID-19 pandemic and implied volatility. Our empirical results show that increased death rates tend to increase fear in the US financial market. Specifically. we find that high COVID-19 cases have a significant impact on implied volatility under high uncertainty conditions, but low COVID-19 cases appear to have no impact on implied volatility in the US market. Our findings offer support to the US policy response by the Federal Reserve Board and the government to limit the instability effect of the COVID-19 shock on the financial markets.