Narrow Results

This study examines the correlation between oil price fluctuation and absolute business development in Pakistan. Our study focusses on three economic sectors, agriculture and livestock, manufacturing and electricity production and transportation from 1980 to 2018 using the autoregressive distributed lag, with linear regression to evaluate the (time series or panel) data (please elaborate the frequency of data as well either it is daily, weekly, monthly, quarterly or yearly data). Our findings reveal negative impact of oil price on the economic development overall, and manufacturing, electricity production and livestock sectors individually; while, there is positive relationship observed with communication and transport sectors. There is need for policymaker’s attention on highly oil-dependent sectors to run their operations. Empirical findings suggest a 30% shortage of oil supply responsible for the highest fluctuated structure of oil pricing, which suddenly increases the projected welfare loss through a 40% reduction in gross domestic product. This study suggests that the country should maintain a minimum 100-day strategic petroleum reserves to hedge any adverse effect of oil price fluctuation on economic and social welfare losses.

This paper analyzes the effects of U.S. monetary policy on sovereign credit default swap (CDS) markets in a total of 66 countries including both advanced and emerging market economies at the monthly time horizon from 2001 to 2016. We employ a four-variable vector autoregression (VAR) model to estimate the monetary policy shock and examine the pass-through of U.S. monetary policy shocks to sovereign CDS markets. We find that the effect of monetary policy shocks on CDS markets is strong, especially during the European sovereign debt crisis and the period the U.S. monetary policy rate was near zero. Our analysis indicates that expansionary U.S. monetary policy leads to the widening of the sovereign credit spreads and the heightening of the CDS market volatility.

The tail of the distribution for the "mean cluster size" (susceptibility) at the site percolation threshold is found by Monte Carlo simulations to be exponential. Similar distributions might be expected for the market volatility, if stock price fluctuations are described by Cont–Bouchaud percolation.

In Ref. 1, a new model for the description of the financial markets dynamics has been proposed. Traders move on a two dimensional lattice and interact by means of mechanisms of mutual influence. In the present paper, we present results from large-scale simulations of the same model enhanced by the introduction of rational traders modeled as moving-averages followers. The dynamics now accounts for log-normal distribution of volatility which is consistent with some observation of real financial indexes⁷ at least for the central part of the distribution.

We present results of an extension of the market model introduced by Bornholdt to high dimensions. Three and four dimensions are shown to behave similar to two, for suitable parameters.

In the folklore of emerging markets, there is a popular belief that bubbles are inevitable. In this paper, our objective is to estimate a state-space model for rational bubbles in selected Asian economies with the aid of the Kalman Filter. For each economy, we derive a possible picture of the bubble formation process that is implied by the state-space formulation. The estimation is based on the rational valuation formula for stock prices. Our results provide a possible way of defining the presence of rational bubbles in the stock markets of Taiwan, Singapore, Korea, and Malaysia.

This paper examines the key characteristics of Singapore's exchange rate-centered monetary policy; in particular, its managed float regime which incorporates key features of the basket, band and crawl system popularized by Williamson (1998, 1999). We assess how the flexibility accorded by this framework has been advantageous in facilitating adjustment to various shocks to the economy. A characterization of the countercyclical nature of Singapore's exchange rate policy is also offered, with reference to recent work on the monetary policy reaction function and estimates of Singapore's behavioral equilibrium exchange rate. We also review previous econometric analysis which provides evidence that Singapore's managed float system may have helped to mitigate the spillover effects of such increased volatility into the real economy. The track record of Singapore's managed float regime over the past two decades suggests that intermediate regimes are a viable alternative to the so-called "corner solutions", especially when supported by consistent macroeconomic and microeconomic policies as well as strong institutions.

Trading in commodity derivatives on exchange platforms is an instrument to achieve price discovery and better price-risk management besides helping the macroeconomy with better resource allocation. In the 2008–2009 budget, the Indian government proposed to impose a commodity transaction tax (CTT) amounting to 0.017% of trading value. In this context, we examine the relationship between trading activity, volatility and transaction cost for five most traded commodities in India. Results suggest that there exists a negative relationship between transaction cost and liquidity and a positive relationship between transaction cost and volatility. Further, the results of structural model support the results of VAR analysis. Therefore, if the government imposes CTT, it would lead to higher volatility and lower trading activity affecting market efficiency and liquidity.

This study applies the panel smooth transition regression (PSTR) model to investigate the non-linear dynamic relationship between bond fund flows and investment volatility in Taiwan. Our empirical results are as follows. (1) A bond fund's net flow and volatility present a non-linear relationship, (2) Investors' behavior is different under the volatility threshold value and the control variables of asset of funds, management fees and the Sharpe indicator, (3) The different risk attributes of bond funds produce completely different investor behavior. In sum, the threshold of volatility is an important index to look at when investing in bond funds.

Along with the international trade and economic ties, international stock markets are performing increasingly closely. This paper investigates the volatilities and the return co-movements among three stock markets in mainland China, Hong Kong, and the United States, from January 1, 2007, to July 5, 2019. We use the MIDAS framework to separately characterize short-term and long-term features. The results reveal that different market volatilities have different sensitivities to the same events. After the second half of 2016, the volatility of China’s stock market gradually dropped below that of the other two markets. As for market co-movements, the return correlation between China and Hong Kong rose sharply after 2007. Although the co-movements for return rates among these three stock markets possess mutual dynamic synchronization features, deviations exist occasionally due to the emotional transfer of funds in the international market when a significant economic or financial event occurs. The analysis suggests that countries should stabilize the financial investment environment and guard against hot money activities.

General researches show that all kinds of random risk information and periodic information in the financial system are mainly transmitted to the asset price through influencing the volatility, thus impacting the whole market. So can the periodic information and random factors in the price be transmitted to the volatility in reverse and cause volatility changes? Hence, in this paper, we investigate the stochastic resonance of volatility which is influenced by price periodic information in financial market, based on our proposed periodic Brownian Motion model and absolute return volatility. The parameter estimation of the periodic Brownian Motion model is obtained by minimizing the mean square deviation between the theoretical and empirical return distributions for the CSI300 data set. The good agreements of the probability density functions of the price returns, realized volatility (RV) at 5 minutes, RV at 15 minutes and absolute return volatility between theoretical and empirical calculation are found. After simulating the absolute return volatility and signal power amplification (SPA) of volatility via periodic Brownian Motion model, the results indicated that (i) single and double inverse resonance phenomena can be observed in the function of SPA versus random information intensity or economic growth rate; (ii) multiple inverse resonance phenomena can be also observed for SPA versus frequency of periodic information. The results imply that the transmission of stochastic factors and periodic information is not only from the volatility to the price, but also from the price to the volatility.

Time series prediction is of primary importance in a variety of applications from several science fields, like engineering, finance, earth sciences, etc. Time series prediction can be divided in to two main tasks, point and interval estimation. Estimating prediction intervals, is in some cases more important than point estimation mainly because it indicates the likely uncertainty in the prediction process. Recently, the sieve bootstrap method has been successfully used in prediction of nonlinear time series. In this work, we study the performance of the prediction intervals based on the sieve bootstrap technique, which does not require the distributional assumption of normality as most techniques that are found in the literature. The construction of prediction intervals in the presence of different types of outliers is not robust from a distributional point of view, leading to an undesirable increase in the length of the prediction intervals. In the analysis of time series, it is common to have irregular observations that have different types of outliers. For this reason, we propose the construction of prediction intervals for returns based on the winsorized residual and bootstrap techniques for time series prediction. We propose a novel, simple and distribution free resampling technique for developing robust prediction intervals for returns and volatilities for GARCH models. The proposed procedure is illustrated by an application to known synthetic and real-time series.

This paper studies the cross-correlations of 67 stock market indices in the past 5 years. In order to capture the interaction of the stock markets, we propose to take a complex network approach to analyzing the interdependence of the individual stock markets. Specifically, stock markets are considered as network nodes, and the network links (weights of links) are defined by the cross-correlations between market indices over a period of time (time window). Thus, the resulting network provides information about the interdependence of individual markets, with the network links representing the extents to which the markets are correlated. If we allow the time window to move in forward time and construct a network for each time window over a long period of time, we are able to capture the dynamics of the network. In our study, all networks are constructed from raw data of market indices, and our aim is to investigate how network properties can be used to infer market behavior. By examining the variation of the network parameters as time elapses, we show that stock markets of different countries have time-varying interaction, and that developed markets tend to demonstrate similar behavior while emerging markets are statistically independent of each other. Furthermore, we observe synchronization in the network of stock markets, which is an important universal property observed in many physical and man-made networks. Specifically, we show that stock markets of different countries generally behave in a synchronous manner when they experience fluctuation, which is especially notable in the developed markets. This work exposes the interdependence of stock markets in the world and proposes a complex network approach to identifying some salient global behavior of the interconnecting markets.

We present a mathematical model for stock market volatility flocking. Our proposed model consists of geometric Brownian motions with time-varying volatilities coupled with Cucker–Smale (C–S) flocking and regime switching mechanisms. For all-to-all interactions, we assume that all assets' volatilities are coupled to each other with a constant interaction weight, and we show that the common volatility emerges asymptotically and discuss its financial applications. We also provide several numerical simulations and compare them to existing analytical results.

A two-phase phenomenon in three financial exchange prices is studied. To understand the underlying mechanism for the formation of market prices, we perform the multifractal analysis and the detrended fluctuation analysis in terms of time series of market prices. We also examine higher order temporal correlations for the market price. Although the multifractal properties of market prices are obtained, it cannot be reproduced the binomial multiplicative process through that was used to understand fully developed turbulence.

The multifractal spectrum calculated with wavelet transform modulus maxima (WTMM) provides information on the higher moments of market returns distribution and the multiplicative cascade of volatilities. This paper applies a wavelet based methodology for calculation of the multifractal spectrum of financial time series. WTMM methodology provides a better measure of risk changes compared to the structure function approach. It is well founded in applied mathematics and physics with little popularity among finance researchers.

Detrended fluctuation analysis (DFA) is used to examine long-range dependence in variations and volatilities of American treasury bills (TB) during periods of low and high movements in TB rates. Volatility series are estimated by generalized autoregressive conditional heteroskedasticity (GARCH) model under Gaussian, Student, and the generalized error distribution (GED) assumptions. The DFA-based Hurst exponents from 3-month, 6-month, and 1-year TB data indicates that in general the dynamics of the TB variations process is characterized by persistence during stable time period (before 2008 international financial crisis) and anti-persistence during unstable time period (post-2008 international financial crisis). For volatility series, it is found that; for stable period; 3-month volatility process is more likely random, 6-month volatility process is anti-persistent, and 1-year volatility process is persistent. For unstable period, estimation results show that the generating process is persistent for all maturities and for all distributional assumptions.

In this paper, we check for existence of multifractal in volatility of Moroccan family business stock returns and in volatility of Casablanca market index returns based on multifractal detrended fluctuation analysis (MF-DFA) technique. Empirical results show strong evidence of multifractal characteristics in volatility series of both family business stocks and market index. In addition, it is found that small variations in volatility of family business stocks are persistent, whilst small variations in volatility of market index are anti-persistent. However, large variations in family business volatility and market index volatility are both anti-persistent. Furthermore, multifractal spectral analysis based results show strong evidence that volatility in Moroccan family business companies exhibits more multifractality than volatility in the main stock market. These results may provide insightful information for risk managers concerned with family business stocks.

This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a nonlinear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behavior for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behavior than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the nonlinear cross-correlations excluding the pair Bitcoin versus Dogecoin $({\alpha }_{xy}(0)=-1.14\%)$. At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the nonlinear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.

A time series model for the FX dynamics is presented which takes into account structural peculiarities of the market, namely its heterogeneity and an information flow from long to short time horizons. The model emerges from an analogy between FX dynamics and hydrodynamic turbulence. The heterogeneity of the market is modeled in the form of a multiplicative cascade of time scales ranging from several minutes to a few months, analogous to the Kolmogorov cascade in turbulence.

The model reproduces well the important empirical characteristics of FX rates for major currencies, as the heavy-tailed distribution of returns, their change in shape with the increasing time interval, and the persistence of volatility.