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In this study, the causal relationships between asymmetric information in Borsa Istanbul and volatility index are investigated using daily data from 2003-2022. We first construct the asymmetric information series using the method of Llorente et al. (2002) and then apply the asymmetric causality test developed by Hatemi-J (2012) to investigate the causality between these variables. The approach captures nonlinear effects in the series by dividing them into positive and negative shocks. The results of the symmetric causality test indicate that there is no bidirectional causality between asymmetric information in Borsa İstanbul and volatility index. However, the results of the asymmetric causality test show that positive financial volatility shocks do cause positive asymmetric information shocks. An increase in financial volatility causes an increase in asymmetric information. Overall, this study highlights the asymmetric relationship between volatility index and asymmetric information in Borsa Istanbul, providing valuable insights for investors and policymakers in the Türkiye. [ABSTRACT FROM AUTHOR]

We use the nonlinear autoregressive distributed lag model to assess the asymmetric relationship between the Chicago Board Options Exchange's volatility index (VIX) and volatility-of-volatility index (VVIX) over the period January 2007 to March 2020. To control for potentially confounding factors, we include measures for economic policy uncertainty and the volatility risk premium. There are three key findings. First, we find that there is an asymmetric long run cointegrating positive relationship running from VVIX to VIX. In this long run relationship, VIX is more responsive to deceases in VVIX than increases in VVIX. Second, we also find an asymmetric short run relationship between VIX and VVIX. However, in contrast to the long run results, for the short run VIX is more responsive to increases in VVIX than decreases in VVIX. Third, consistent with other studies, we find that in the long run VIX rises with greater economic policy uncertainty but falls with increases in the volatility risk premium. We discuss the implications of our findings for practitioners in risk and portfolio management, derivative pricing, and trading. [ABSTRACT FROM AUTHOR]

For reducing investment risks, predicting the volatility of financial markets is crucial. We propose a method for effectively embedding social media posts to facilitate accurate predictions of financial market trends. While discussions on social media inherently consist of paired information – a topic and its sentiment mood – most conventional studies have produced embeddings focusing only on either topic or sentiment information. This approach tends to neglect the intertwined nature of topic and sentiment, thereby overlooking potentially valuable information for market predictions. In this study, we overcome this challenge by introducing a novel document embedding technique that explicitly leverages both topics and sentiments collaboratively for market forecasting. The obtained embeddings are co-trained with financial time series data in a machine learning model, and their efficacy is evaluated through a market prediction task. Benchmarking against various existing document embedding techniques, our model demonstrated superior performance in terms of F-1 Score and Matthews correlation coefficient. The proposed model was further assessed from a practical viewpoint, utilizing investment simulations based on its predictions. These simulations confirmed the model's potential to generate profits even during heightened market volatility, demonstrating its effectiveness as a real-world investment risk mitigation model. Model interpretation using SHapley Additive exPlanations revealed that while some topic-sentiment pairs on social media consistently contribute to market forecasting, others have only a transient impact. The SHapley Additive exPlanations experiment was also compared to the ablation model and showed that the proposed embedding allows for effective prediction by treating topics and sentiment jointly. [Display omitted] • Propose a social media text embedding method for financial market forecasting. • Combines social media text and price to predict volatility index(VI). • Performs well compared with traditional embedding methods. • The proposed embedding achieves an excellent interpretation. • Provides the link between social media topic-sentiment pairs and market trends. [ABSTRACT FROM AUTHOR]

This paper explores how fear sentiment affects the price of Bitcoin by employing the rolling-window Granger causality tests. The analysis reveals negative influences from the volatility index (VIX) to Bitcoin price (BTC), which ascertains that Bitcoin can not be considered a haven in fear sentiment. Due to the liquidity in economic downside risks, BTC may decrease with high VIX to hedge losses, increasing during low VIX periods. The empirical results conflict with the intertemporal capital asset pricing model, which underlines that the increasing VIX can promote the price of Bitcoin. In turn, BTC positively impacts VIX, which shows that Bitcoin price can be treated as the main indicator for a more comprehensive analysis of the fear index. Under severe global uncertainty and changeable fluctuation of market sentiment, investors can optimize investment decisions based on market fear sentiment. The government can also consider VIX to grasp the trend of BTC to participate in cryptocurrency speculation effectively. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between the Ibovespa returns and the implied volatility index in Brazil (IVol-BR). We analyzed whether the level of the IVol-BR is related to the present and future Ibovespa returns. OLS and quantile regressions were used to investigate possible differences in the relationships along the distribution of the IVol-BR. To test the robustness of the model, we applied an alternative proxy for volatility, estimated from a GARCH (1,1) model. An asymmetrical relationship was found in Brazilian investors' response to different moments of market volatility, suggesting that negative Ibovespa returns have a stronger relationship with the IVol-BR than positive returns. The coefficients were higher at the extremes of the IVol-BR distribution. These results suggest that the Brazilian market reacts more strongly to bad news than to good news, in line with the ideas developed in the behavioral finance field. [ABSTRACT FROM AUTHOR]

Volatility indexes provide a tool for investors to speculate and trade on market sentiment regarding future volatility. The risk of trading on volatility indexes can be measured by their second moments, namely, variance and correlation. This study considers the four representative volatility indexes published by the CBOE: stock market volatility index (VIX), crude oil volatility index (OVX), foreign exchange rate volatility index (EVZ), and gold price volatility index (GVZ). To examine their risk, we develop an extended multivariate Markov switching ARCH (MSARCH) model in which regime-switching variances, correlations, and variance-correlation relations are designed. Our empirical sample consists of the four volatility indexes from June 2008 to April 2020 for 612 weekly observations (Wednesday to Wednesday). For the conditional variances, we find evidence of regime-switching processes (switching between low and high volatility regimes) for the individual volatility index returns, with the exception of the GVZ. The estimated probability of the high volatility regime may be used to track economic distress and uncertainty shocks. These results provide evidence for volatility-of-volatility risk. For the conditional correlations, we find a regime-switching relation between variances and correlations. That is, the highest correlation appears when the paired volatility markets are simultaneously experiencing a state of high volatility. By contrast, when the paired volatility markets are encountering different volatility states, the correlation is weaker. These results indicate that the volatility-of-volatility risk is a factor affecting the dynamics of correlations between volatility indexes. [ABSTRACT FROM AUTHOR]

Volatility, which is one of the leading indicators of the investment atmosphere in the capital markets, affects the developing country markets significantly. Turkey, with its performance in recent years, is nominated as a member of BRICS group formed with emerging economies shaping the global economy. In this study, it is aimed to reveal the effect of volatility index, accepted as international volatility indicator, on BRICS countries and Turkey stock markets. Within this context; the effect of volatility index, exchange rate, consumer confidence index and index of economic freedom on composite stock market index were analyzed by panel data analysis methods. According to the results of the analysis, it has been determined that the volatility index and exchange rate have a negative effect and the economic freedom index has a positive effect on the stock market index. No significant relationship has been found between the consumer confidence index and the stock market index. [ABSTRACT FROM AUTHOR]

Transformed diffusions (TDs) have become increasingly popular in financial modeling for their model flexibility and tractability. While existing TD models are predominately one-factor models, empirical evidence often prefers models with multiple factors. We propose a novel distribution-driven nonlinear multifactor TD model with latent components. Our model is a transformation of a underlying multivariate Ornstein–Uhlenbeck (MVOU) process, where the transformation function is endogenously specified by a flexible parametric stationary distribution of the observed variable. Computationally efficient exact likelihood inference can be implemented for our model using a modified Kalman filter algorithm and the transformed affine structure also allows us to price derivatives in semi-closed form. We compare the proposed multifactor model with existing TD models for modeling VIX and pricing VIX futures. Our results show that the proposed model outperforms all existing TD models both in the sample and out of the sample consistently across all categories and scenarios of our comparison. [ABSTRACT FROM AUTHOR]

A parsimonious generalization of the Heston model is proposed where the volatility-of-volatility is assumed to be stochastic. We follow the perturbation technique of Fouque et al [Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives, 2011, Cambridge University Press] to derive a first-order approximation of the price of options on a stock and its volatility index. This approximation is given by Heston’s quasi-closed formula and some of its Greeks. It can be efficiently calculated since it requires to compute only Fourier integrals and the solution of simple ODE systems. We exemplify the calibration of the model with S&P 500 and VIX data. [ABSTRACT FROM AUTHOR]

Biases in standard variance swap rates (VSRs) can induce substantial deviations below market rates. Defining realized variance as the sum of squared price (not log-price) changes yields an ‘arithmetic’ variance swap with no such biases. Its fair value has advantages over the standard VSR: no discrete monitoring or jump biases; and the same value applies for any monitoring frequency, even irregular monitoring and to any underlying, including those taking zero or negative values. We derive the fair value for the arithmetic variance swap and compare it with the standard VSR by: analysing errors introduced by interpolation and integration techniques; numerical experiments for approximation accuracy; and using 23 years of FTSE 100 options data to explore the empirical properties of arithmetic variance (and higher moment) swaps. The FTSE 100 variance risk has a strong negative correlation with the implied third moment, which can be captured using a higher moment arithmetic swap. [ABSTRACT FROM PUBLISHER]

• Design a novel multi-objective deep learning system. • Propose an improved multi-objective grey wolf optimizer. • The proposed system is developed for financial volatility index prediction. • The evaluations show superiority and feasibility of the proposed system. Advances in volatility index prediction based on computational intelligence have brought wide-ranging benefits to financial risk management. However, current studies in the field remain limited and need further improvement due to these research gaps: (1) ignoring the important role of probabilistic prediction in characterizing uncertainty risks; (2) relying on single-objective optimization algorithm to optimize prediction model, thereby ignoring the advantages of multi-objective optimization; (3) emphasizing nonlinear modeling, not considering both nonlinear and linear modeling simultaneously. Aiming to address these gaps, a novel multi-objective hybrid deep learning system, composed of a modified multi-objective optimizer, a clockwork recurrent neural network, and an improved mode decomposition method, is newly proposed to perform deterministic and probabilistic volatility index prediction. Concretely, the volatility index is decomposed into some modes using an advanced data decomposition method; further, the clockwork recurrent neural network is considered a prediction engine to model these modes, which has an excellent ability to model the long-term dependency for linear and nonlinear time series depending on its mechanism of temporal granularity, as compared to traditional recurrent neural networks; finally, the prediction results can be obtained by integrating the predictions from these modes, using the mode weights calculated by an improved multi-objective optimizer with the objectives of prediction accuracy and stability. To validate the performance of the proposed hybrid deep learning system, case studies and corresponding sensitivity and convergence analyses are carried out. From the perspective of the indicator mean absolute percentage error, the maximum improvements of our proposed system reach 67.50%, 75.82%, and 75.42% in Case I, Case II, and Case III, respectively, thus indicating its superiority and practical feasibility. [ABSTRACT FROM AUTHOR]

The traditional clustering methods based on the two-dimension information are not suitable for dealing with the panel data processing, in the consideration of the multiple information characteristics of panel data and according to the "absolute index", "incremental index" and "volatility index" of the panel data, this paper reconstructed the similarity measure distance function and Ward clustering algorithm, proposed an adaptive weighting clustering method for panel data. The provided algorithm not only can degenerate into the traditional "absolute distance" clustering algorithm, but also can conduct clustering prediction for the future category of panel data. Finally, examples show that this method is both effectiveness and flexibility. [ABSTRACT FROM AUTHOR]

In this work, we study asymmetric and day of the week effects in the Chicago Board Option Exchange's Volatility Index -VIX-from 01/02/2003 to 03/302007. We use GARCH models assuming Gaussian, t-Student and Generalized Error Distribution (GED) innovations. To Gaussian innovations, we use the Quasi-Maxima-Likelihood Method. The results show evidence in favor of asymmetric and day of the week effects. An ARCH model with GED errors and a ARCH model with t-Student Distribution fit well the data. We used exogenous variables for the mean of the process, representing the first day of the week, to explore the day of the week effects. Our results suggest that on Mondays the market volatility is higher than in other days. This supports the thesis that returns are negatively correlated with volatility. [ABSTRACT FROM AUTHOR]

This paper utilizes a theoretically-grounded model of information systems change together with data from 1891 maintenance projects to test the effects that four factors have on the volatility index of application software. The volatility index is a measure of the relative cost of doing maintenance on the deep structure of a system. Two factors were found to be associated with a higher volatility index, the age of the system and the size of the system. One factor was found to be associated with a lower volatility index, the use of higher-level languages of implementation. One factor was found to be unrelated to the volatility index, the time period when the change was made. Some factors may be under management control, e.g. the language of implementation, while others are usually system characteristics, e.g. system size. Knowledge of the effects of these factors could influence how researchers work with information systems and how information systems management does its work. [ABSTRACT FROM AUTHOR]

Integration of financial markets due to globalization generates new paradigms of financialization. And with HFT i.e. the high-frequency trading, financialization has distorted the relations of financial markets. HFT is based on highly complex financial products such as Index Options which are linked with volatility and it's forecasting. After the introduction of Volatility, VIX Index of Chicago Board of Options Exchange becomes the effective benchmark for stock market volatility now a day. Although VIX Index is a volatility measure derived from Standard and Poor 500 Index (SPX) option prices, traders are unaware of the inverse relationship between these markets. This study purpose is to understand the relationship between the two trading vehicles and increase the market awareness based on high order moment models which are used to mimic the behavior of these index options. It also explains the logic versus perception perspective in option pricing theory to develop theoretical foundations and consider it in future theory erection. Finding shows that SPX index is negatively correlated with VIX Index and financial markets have an inverse relationship between them. • To compute option price derivatives, i.e., the 'Greeks,' Monte Carlo simulation estimators are used. • For option pricing Comparison is made between the Black–Scholes and the stochastic volatility models with jumps. • To generate unbiased estimates of option price derivatives, compare the path wise and likelihood ratio approaches. • Try to get simulation estimators and numerical results for some path-dependent and path-independent options. • Possible suggestions for speculation strategies and risk management are briefly discussed. [ABSTRACT FROM AUTHOR]