High Market Volatility Research Articles

Rezerwy walutowe - „Rzecz Święta”

Celem opracowania jest uwypuklenie roli rezerw walutowych i wskazanie możliwości ich rzetelnego oraz prawidłowego gromadzenia i wykorzystania. Celami szczegółowymi są m.in. porównanie rezerw walutowych z innymi rodzajami rezerw bankowych oraz identyfikacja podstawowych rodzajów ryzyka związanych z zarządzaniem rezerwami walutowymi, w tym zmian w odpowiedzi na ostatni globalny kryzys finansowy, a ponadto określenie istoty i charakteru interwencji walutowych oraz identyfikacja ich rodzajów. W dobie zwiększania wydatków na cele polityczne i społeczne rośnie bowiem pokusa niewłaściwego wykorzystania rezerw walutowych. Do tego dochodzą wysokie globalne ryzyko polityczne oraz duża zmienność na rynkach finansowych, a także nowoczesne kanały (np. media społecznościowe) ataku na kursy walutowe. Czynniki te sprawiają, że zarządzanie rezerwami walutowymi nie należy do łatwych zadań i jest „wiedzą tajemną” banków centralnych.

Empirical investigation of herding in cryptocurrency market under different market regimes

PurposeOur study focuses on analyzing the trading behaviour of the investors who invest in these currencies to review their trading patterns which may help us to understand the price formation of cryptocurrencies in this market.Design/methodology/approachWe used Chang et al. (2000) measure to calculate herding that is based on cross-section absolute dispersion of stock returns (CSAD). We further analyse the nature of the same in different market regimes, that is up market, down market, high volatile market, low volatile market etc.FindingsApplying different methodologies both static and time varying, we find that herding is pronounced when the market is either passing through stress or has become highly volatile. Anti-herding is found in a less volatile market or in a bullish market.Practical implicationsOur results are also helpful for the policy makers in designing stricter regulations to provide safe investment environment to the investors.Originality/valueOur study in an extension of the literature in same direction and contribute in numerous ways. As the number of digital currencies is growing day by day and we have around 2,200 digital currencies trading across the world, we increased our sample size up to 100 most traded currencies. While majority of the studies cover the period 2015–2018, our study comprises the largest sample size starting from August 2013 to April 2019. We use the static model to find herding and simultaneously try to detect herding under different market regimes: up market and down market.

Quarterly earnings announcements and intra-industry information transfer from the Pacific to the Atlantic

In this study, we explore intra-industry information transfer of quarterly earnings announcements for six major developed markets including the United States. Using an event study methodology, we find that the average cumulative abnormal returns (CARs) of peer firms exhibit a positive and significant relationship with the CARs of announcing firms in the same country and industry over the three-day window surrounding quarterly earnings announcements. During periods of high past market volatility, earnings announcements demonstrate magnified information spillover effects on peer firms. Also, our results suggest that announcing firms with larger market capitalization generate greater intra-industry information transfer.

Macroeconomic responses to fiscal shocks in Portugal

PurposeThis study analyses the impact of fiscal shocks on GDP, inflation and interest rates in Portugal over 1995–2017.Design/methodology/approachMultipliers are estimated using a structural VAR (SVAR) a' la Blanchard and Perotti (2002) using OECD elasticities. Changes in direct and indirect taxes are considered for fiscal shocks on the revenue side and changes in public consumption, investment and transfers for fiscal shocks on the expenditure side.FindingsThe analysis finds small tax multipliers and larger government consumption multipliers for growth, while short-term responses to shocks in transfer and investment spending are found to be negligible. Fiscal shocks have an ambiguous impact on inflation, and fiscal shocks of an expansionary nature are found to trigger declines in interest rates. The results are robust to different orderings of variables, to the selection of an alternative time period which excludes the financial crisis and to an alternative estimation technique.Research limitations/implicationsA major limitation of the study relates to the relatively short time period which does not allow capturing the impact of possible structural breaks.Practical implicationsThis analysis is relevant for countries, like Portugal, that display high debt levels and volatile market sentiment and lack an independent monetary policy.Originality/valueOverall, the analysis of output multipliers compares well with some other studies conducted on the Portuguese economy and confirms the importance of the disposable income channel in the transmission of fiscal shocks to the rest of the economy. The study is one of the first to focus also on the implications of fiscal shocks on inflation and long-term interest rates. It is the first to apply the local projection method to estimate multipliers in Portugal.

Do Earnings Announcements Lift All Boats? Evidence from the Pacific to the Atlantic

In this study, we explore intra-industry information transfer of quarterly earnings announcements for six major developed markets including the United States. Using an event study methodology, we find that the average cumulative abnormal returns (CARs) of peer firms exhibit a positive and significant relationship with the CARs of announcing firms in the same country and industry over the three-day window surrounding quarterly earnings announcements. During periods of high past market volatility, earnings announcements demonstrate magnified information spillover effects on peer firms. Also, our results suggest that announcing firms with larger market capitalization generate greater intra-industry information transfer.

Analysis of Investors Herding Behaviour: An Empirical Study from Istanbul Stock Exchange

This study investigates the existence of herding behavior in Istanbul Stock Exchange using a narrow index for the period between January 1, 2000 and December 31, 2018. Besides, the study tests herding behavior of investors during high and low volatility periods. The data is collected from Finnet and it contains daily, weekly and monthly return data. The study uses both CSSD and CSAD models. The finding of the study shows that herding is more prevalent when the market falls and from the three data type it is more prevalent in the daily data. This shows that investors behave rationally when the market rises and they become irrational when the market falls. On the other hand, the level of herding is considerably significant during high market volatility periods. Therefore, we conclude that there is aymmetrical investor behavior in Istanbul Stock Exchange.

Impacts of Global-Economic-Policy Uncertainty on Emerging Stock Market: Evidence from Linear and Non-Linear Models

Global economic policy uncertainty (GEPU) is one of important phenomena in the global economy; it can impact on the overall economic performance and stock market per-formance, regardless of the status of the world economy. Thus, this paper empirically investigates the impact of global economic policy uncertainty on the Malaysian stock market over the period from 10:2003 to 2017:03. Using the GARCH model, the study demonstrates that global policy uncertainty affects the Malaysian stock market negatively. Similarly, the SVAR model also shows results consistent with the GARCH estimation. Nevertheless, the Markov switching estimation uncovers that global policy uncertainty has negative impacts on stock market performance in both low and high volatile market states. The impact is, however, greater during the high volatile state. Hence, the relationship between global economic policy uncertainty and stock market returns tends to be asymmetric. The overall empirical results infer that global economic policy uncertainty has some implications for asset pricing.

Carbon financial markets underlying climate risk management, pricing and forecasting: Fundamental analysis

Climate Change (CC) is a major issue of our century. Controlling the constraints of Greenhouse Gas (GHG) emissions through transformation into opportunities, in an organization to increase industrial production, has become a necessity. The main reason for this adoption was the effectiveness of energy management and responsible linkages that are being developed to determine the issues and opportunities of carbon finance for organizations. Through analysis of the European Union Emissions Trading Scheme (EU ETS) and the Clean Development Mechanism (CDM), this article presents and demonstrates a variety of determinants of CO2 prices (EUA) to be used in econometric techniques. This paper details the main carbon price drivers related to institutional decisions, energy prices, and weather events. Our study focuses on price changes in the EUA, being the most liquid carbon asset. In this regard, we highlighted the daily spot price of the EUA to highlight the daily changes affecting this price, given the high volatility in this Carbon financial market. The treatments of the determinants of CO2 prices (EUA) can be used to analyze the evolving and expanding Carbon financial markets sphere. It features stylized facts about Carbon financial markets from an economics and management perspective, as well as covering key aspects of pricing strategies (institutional decisions, energy prices, and extreme weather events), risk, and portfolio management. Aimed at those with fundamental analysis, the CO2 prices within the framework of the EU ETS depend on several determinants. This paper constitutes an introduction to emission trading and an overview of the regulations governing Carbon financial markets. First, we detail the price changes in the EUA and primary energy prices. Second, we introduce the main characteristics of emissions trading, be it in terms of spatial and temporal limits, Clean Dark Spread, Clean Spark Spread, and Switch Price. Third, we provide a descriptive analysis of atmospheric variables, structural variations, and the Subprime crisis and their impacts on the price development of EU CO2 allowances. Keywords: Fundamental analysis, European Union Emissions Trading Scheme, Clean Development Mechanism (CDM), Determinants of CO2 prices (EUA), Climate risk management.

EXPORTING IRANIAN PHARMACEUTICAL PRODUCTS TO RUSSIA’S MARKET: POTENTIALS AND BARRIERS

The pharmaceutical market is one of the most important and lucrative industries in the trade world. The expansion of non-oil exports and shifting away from the mono-product economy and its problems (high volatility in the oil market and, consequently, the country's revenue, as well as the issue of sanctions on Iran's oil sales by the West) have made it inevitable for Iran to expand its export of pharmaceutical products. Meanwhile, the Russian Federation, for historical reasons, has a high potential to become a destination for Iran's pharmaceutical products due to the geographical proximity of the two countries and the possibility of direct shipment of goods to Russia via the Caspian Sea, as well as due to the fact that Russia is a large consuming market with the population of 145 million and high volume of pharmaceutical imports (over $ 10.5 billion in 2018). The article analyzes the pharmaceutical export potential of Iran to the Russian Federation using the indicators of revealed comparative advantage index, cosine index and trade plan index. The findings of this study indicate the high potential of Iranian pharmaceutical products for export to the Russian consuming market and the lack of utilization and the neglect of the Iranian pharmaceutical manufacturers and exporters from the Russian market. As policy implications, the authors recommend making pharmaceutical products a priority for export to the Russian market, which will lead to more efficient use of Iran's productive potential.

Asymmetric Momentum Threshold Effect of Copper Futures Returns on Spot Returns Volatility in London Metals Exchange under High Volatility

This paper discusses the asymmetric momentum threshold effect of copper futures returns on spot returns volatility in the London Metal Exchange. Referring the Threshold Autoregressive (TAR) and Momentum Threshold Autoregressive (MTAR) models, this study utilizes a Hybrid MTAR-GARCH model to test the asymmetric momentum threshold effects of LME copper futures returns on spot returns volatility. It is revealed that there are indeed asymmetric momentum threshold effects of LME copper futures returns on spot returns volatility. This finding would be beneficial to financial decision-making concerning copper price hedging, arbitrage and investment amidst high volatility market conditions.

Option Pricing with Random Risk Aversion

Based on a standard general equilibrium economy, we develop a framework for pricing European options where the risk aversion parameter is state dependent and aggregate wealth and the underlying asset have a bi-variate transformed-normal distribution. Our results show that the pricing kernel may become non-monotonic for high levels of volatility and low levels of skewness of the risk aversion parameter. Also, as the volatility of the risk aversion parameter increases, the (Black and Scholes) implied volatility shifts upwards but its shape remains the same, which implies that the volatility of the risk aversion parameter does not change the shape of the risk neutral distribution. Finally, an empirical example shows that the estimated volatility of the risk aversion parameter tends to be low in periods of high market volatility and vice-versa.

Rational and irrational approaches to investors’ transactions in financial markets

Under high financial market volatility, the balance in contradictions between rational and irrational approaches to investors’ transactions shifts towards the domination of the behavioral component. However, further instability of financial markets casts doubt on the fairness of asset pricing as one of the main target indicators of the market economy. The article deals with the research of two completely opposite theories of investors’ behaviour in financial and commodity markets. The authors connect rational investors’ behaviour with the basic theories of quantitative finance, and their irrational actions with the varieties of behavioural finance. The methodological foundation of the research includes classical theories of asset valuation and modern theories of finance with the dominant role of psychology in the monetary model of the world economy development. The research methods encompass analysis and comparison of the market uncertainty and market efficiency levels, detailing and summarising the negative impact of cognitive psychology on pricing in the context of existing information asymmetry in financial markets. The findings are of theoretical and practical importance and can be used both to develop and implement measures in the field of financial (stock) market regulation, and examine the causes of poor engagement of the Russian population in the investment process.

Investors’ financial attention frequency and trading activity

Based on data on users' daily adoption of securities service mobile applications, we measure investors' financial attention frequency, which reflects how often they use such apps, to obtain information on their frequency of opening these apps and online duration. We find that financial attention frequency shows a clear ostrich effect, suggesting that investors acquire financial information less frequently following periods of low market returns and high market volatility. In addition, it significantly promotes trading activity in the market. Further, this driving force remains after a series of robustness tests controlling for other market factors such as investor attention and sentiment and after addressing endogeneity concerns. Finally, financial attention frequency also increases individuals' net buying transactions.

INSIDER DEMAND AND INDUSTRY TRENDS

AbstractWe investigate the time‐series patterns in industry return predictability conditioned on insider demand from 1996 to 2016. Current insider demand within an industry is positively associated with higher future industry returns. This relation is primarily driven by the buy side of insider trades and is more pronounced during periods of economic recession and high market volatility where uncertainty and information asymmetry are relatively high. Our results are consistent with the notion that corporate insiders have an informational advantage that can be used as an indicator for industry portfolio selection.

Financial Attention Frequency and Trading

Based on data on users’ daily adoption of securities service mobile applications, we measure investors’ financial attention frequency, which reflects how often they use such apps, to obtain information on their frequency of opening these apps and online duration. We find that financial attention frequency shows a clear ostrich effect, suggesting that investors acquire financial information less frequently following periods of low market returns and high market volatility. In addition, it significantly promotes trading activity in the market. Further, this driving force remains after a series of robustness tests controlling for other market factors such as investor attention and sentiment and after addressing endogeneity concerns. Finally, financial attention frequency also increases individuals’ net buying transactions.

The Impact of Second Generation FDI Reforms on the Stock Market Development in India

The Governments took a series of initiatives as a measure of second-generation reforms in Foreign Direct Investment (FDI). The FDI reform initiatives had started since 1991 as foundation of Indian economy and the governments over the period contributed to emerge India as destination for Foreign Direct Investment in the world. These reforms played an important role in capital formation in stock markets and developments in the economy. This paper attempts to study the impact of second-generation economic reforms in FDI and its impact on Stock Market Development (SMD) in India. This paper uses a multivariate unrestricted VAR (Vector Autoregression) model to investigate the impact of the reforms in FDI on the development of stock market in India. The study used the quarterly data of FDI inflow, exchange rates and terms of trade (Exports/Imports) from 2004 to 2017 to find the long run impact of FDI reforms on the SMD. The SMD is the ratio of stock market capitalization to the Gross Domestic Product (GDP) of the country. The study uses the unrestricted VAR to generate impulse responses to find the impact of one standard deviation innovation change in one variable on other. Further, Unit Root Test, Granger causality test statistics and variance decomposition (VDC) respectively have been applied to identify variables stationarity, the causality and percent change in variance due to one standard deviation innovation in other variable. The findings of the study conclude that there were structural breaks in the data during 2007Q1 and 2011Q1 due to US financial crisis that lead to high volatility in the Indian stock market. Further, finding concluded that there is a bidirectional causality between foreign direct investment and the stock market development. Finally, study revealed that FDI and terms of trade are also having a bidirectional causality where shock in terms of trade brings a change of 25.15 percent in FDI inflows.

Machine Learning Versus Economic Restrictions: Evidence from Stock Return Predictability

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs due to high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk.

VIX and volatility forecasting: A new insight

This study uses a new fresh look to investigate the linkages between the CBOE VIX and stock market volatility. In this paper, we use 13 stock markets of the G20 to do our research and document several important findings. First, in most countries, the VIX and its large component have statistically significant effect and can lead to high stock market volatility. Compared to the R-squares, we find that large VIX has stronger explanation ability to the international stock markets volatility. Second, out-of-sample results indicate that the VIX can help to increase the forecasts accuracy, especially the large VIX. Additionally, the robust tests support our main conclusions.

Economic policy uncertainty and the Chinese stock market volatility: Novel evidence

In this study, we investigate the impact of global economic policy uncertainty (GEPU) on Chinese stock market volatility. More importantly, for the first time, we explore the effects of directional GEPU based on the changing directions of GEPU and Chinese economic policy uncertainty (EPU). We make several noteworthy findings. First, the in-sample estimated results show that up and down GEPU can lead to substantially high stock market volatility for China. Second, the out-of-sample estimated results support the contention that the GEPU index is helpful for predicting volatility. Moreover, compared to GEPU alone, directional GEPU can provide more useful information that can increase the forecast accuracy. Third, we empirically find that directional GEPU is more effective in predicting Chinese stock market volatility when GEPU and EPU rise in the same month.

Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.