Past Volatility Research Articles

Post-Covid-19 Volatility of Exchange Rate Returns: A Case Study of Pakistan

The purpose of the study is to forecast the volatility for returns of the exchange rate of Pakistan concerning US dollars along with the impact of covid-19 so that we can find out the feasibility of holding this asset along with the risk and returns associated with it. For this purpose daily data has been taken from the State bank of Pakistan on a period from February 01, 2001, to June 30, 2021, where covid-19 is used as a dummy variable. Furthermore, in methodology, we applied GARCH models after finding the presence of the ARCH effect which is at ARCH (6) in the series. It is found in all GARCH models that the past volatility of the exchange rate returns has a statistically significant influence on the current volatility of the exchange rate means there is time-varying and time-correlated volatility associated with exchange rate returns. According to GARCH-M, GARCH-M (variance) (1,1) and GARCH-M (SD) (1,1) results it is concluded that average returns of exchange rate are small but significant and there is no risk factor associated with exchange rate returns but the past square residual terms have a significant impact on risk volatility. Furthermore, Both T-GARCH and E- GARCH depicts that the impact of covid-19, which is bad news, although has a significant impact but its magnitude has a lesser influence on exchange rate volatility than the good news.

Impact of rough stochastic volatility models on long-term life insurance pricing.

The Rough Fractional Stochastic Volatility (RFSV) model of Gatheral et al. (Quant Financ 18(6):933–949, 2014) is remarkably consistent with financial time series of past volatility data as well as with the observed implied volatility surface. Two tractable implementations are derived from the RFSV with the rBergomi model of Bayer et al. (Quant Financ 16(6):887–904, 2016) and the rough Heston model of El Euch et al. (Risk 84–89, 2019). We now show practically how to expand these two rough volatility models at larger time scales, we analyze their implications for the pricing of long-term life insurance contracts and we explain why they provide a more accurate fair value of such long-term contacts. In particular, we highlight and study the long-term properties of these two rough volatility models and compare them with standard stochastic volatility models such as the Heston and Bates models. For the rough Heston, we manage to build a highly consistent calibration and pricing methodology based on a stable regime for the volatility at large maturity. This ensures a reasonable behavior of the model in the long run. Concerning the rBergomi, we show that this model does not exhibit a realistic long-term volatility with extremely large swings at large time scales. We also show that this rBergomi is not fast enough for calibration purposes, unlike the rough Heston which is highly tractable. Compared to standard stochastic volatility models, the rough Heston hence provides efficiently a more accurate fair value of long-term life insurance contracts embedding path-dependent options while being highly consistent with historical and risk-neutral data.

The impact of oil price change and its volatility on major global stock markets

This paper investigates the impact of oil price change, and its volatility on the stock market returns of the US, Japan, and China.Moreover it highlights the association between the returns of each market with respect to the returns of the other markets. More specifically, the study uses daily data of three indices to represent the markets which are Standard & Poor 500, Nikkei 225, and Shanghai Composite (SSE), beside daily data of crude oil price. The GARCH model employed to measure the volatility. The findings indicate that, the S&P500 returns are influenced significantly by oil price change, the returns of both N225 and SSE, as well as its own past volatility. Regarding Nikkei 225 returns it influenced by the returns of the S&P500 and SSE, and by its own previous day volatility. In contrast, Shanghai Composite returns are not influenced neither by oil price change, nor by the returns of the two other indices, rather it’s only affected by its own previous volatility, and S&P500 and Nikkei 255 returns are influencing each other. The impact of oil price volatility on the three stock indices returns is insignificant during the sample period.

An impact of FPI inflows, Nifty returns, and S&P returns on India VIX volatility

This paper analyses the relationship between FPI and the implied volatility. The daily data have been used for analysing the impact of FPI, Nifty returns, S&P 500 returns and CBOE VIX (the proxy of volatility expectation in the US) on India VIX, the proxy for volatility expectations. The time period for this study starts from March 2008-December 2017. The overall objective of the study was to analyse the impact of FPI, Nifty returns, S&P returns, volatility of US market and interaction effect among them on the implied volatility index (India VIX). ARIMA GARCH has been used to test the hypothesis of the study. It was concluded that implied volatility is impacted by the FPI investment in India, Nifty returns, S&P returns and CBOE VIX. It was also found that the volatility has not increased by the emergence of FPIs in India, rather the expected volatility decreased with the increased investment by FPIs. Another observation from the study was that the past volatility affect was more on the future volatility as the GARCH effect was more as compared to the ARCH effect.

Impact of sectoral bank credit on economic growth in India - an empirical analysis

The aim of the present study is on empirically testing the impact of sectoral credit amongst Indian sectors on economic growth. This was achieved by studying the relation between sectoral credit, liquidity and economic growth. The results of the study prove that the sectoral credit was seem to be working on the growth of Indian markets in the long run. On the contrary, it was found that in the short run not only credit was an important factor which impacted the growth of the economy but increase in broad money liquidity, i.e., M3 also impacted sectoral credit which was in favour of our research hypothesis. Similarly, it was also found that past volatility change in terms of different sectors growth was impacting current economic growth again showing the applicability of the concept of increase in sectoral credit and liquidity impacts the economic growth in the Indian markets. However, the external shocks in the variables affect the stable equilibrium in the short run while these stability in the markets were maintained in the long period of time.

Dynamic Interdependence of Stock Returns Based on Information Transmission: Evidence from China and Latin America

This paper analyzes the degree of dynamic interdependence of stock returns between China and six Latin American countries from 2003 to 2018 using the DCC-MGARCH (1,1) model that reflects the information transmission process and the properties of time-varying correlation, such as sensitivity to new information, dependence of past volatility, and the persistence of volatility. It also reports the long-term trends of dynamic interdependence by using rolling window analysis corresponding to a window width of 175 days. The research presents the trends with the United States as a benchmark. First, this paper determined that the Chinese stock market’s influence on the stock markets of six Latin American countries was not decisive. Second, the Chilean and Peruvian stock markets were the most vulnerable to the fluctuations in the Chinese stock returns, while Brazil and Mexico were relatively less affected. Third, the long-term patterns of dynamic conditional correlation between China and the six Latin American countries except for Peru were generally placed between 0.1 and 0.3 Last, when considering the dynamic conditional correlation with the United States as a benchmark, the degree of dynamic interdependence between China and the six Latin American countries was not relatively high.

HOW DID COVID-19 AFFECT THE CO-MOVEMENT OF THE WORLD’S LARGEST ECONOMIES?

ABSTRACT In this paper, we examine how the COVID-19 outbreak influenced the co-movement of the three largest economies (based on purchasing power parity) in the world; US, China, and India. It is important to understand the relationship among the financial markets of these economies, as collectively they represent Int$56,269 billion of economic power and hence create valuable investment opportunities for international investors. We focus on the time period around the start of the pandemic and compare the pre-pandemic co-movement with that during the pandemic. Our results show that the pandemic increased the volatility in the world stock markets, particularly in the emerging markets of India and China. Past volatility of these markets also continues to be a significant determinant of future volatility during the pandemic. Although our correlation analysis show that these markets continue to be connected during the pandemic, further analysis reveals that the long-run association among the stock markets that existed before the pandemic disappeared. The causality among the markets also weakened during the pandemic. Overall, these results show that COVID-19 disrupted the interrelationships of world markets, resulting in more diversification opportunities for international investors. Keywords Stock market co-movement, GARCH models, COVID-19 impact, volatility, Indian stock market, Chinese stock market, US stock market.

Volatility Modelling of Stock Returns in the Petroleum Marketing Sector of the Nigerian Stock Exchange

Introduction: Financial markets play key role in the growth and sustainability of the economy. However, high levels of volatility in the markets may adversely affect the financial system and weaken the economy.
 Purpose: This paper examined the presence of volatility in the stock returns of the petroleum marketing sector of the Nigerian Stock Exchange using ten petroleum marketing firms quoted on the Nigerian Stock Exchange for a period of twenty-four months that is from January 2017 to December 2018.
 Methodology: The study adopted empirical research design using time series data where ordinary least squares was employed to run the analysis through the use of ARCH/GARCH models.
 Findings: Among other results, it was seen that a unit increase in volatility (VLT) will lead to 0.006916 decrease in stock returns (STR). Also, the result of R-squared implies that about eight per cent (8%) of the changes in stock returns (STR) is captured by volatility (VLT) while the remaining ninety-two per cent (92%) of the variation in the model is captured by the error term. The ARCH effect observed is statistically significant. The coefficient of the GARCH effect which is significantly positive at 5% shows that past volatility of stock market return is significant and has effect on current volatility.
 Unique Contribution to theory, Practice and Policy: The implication of this is that an increase in volatility is linked to a significant increase of returns, which is an expected result and thus conforms to economic theory. The results of static and dynamic forecasting of GARCH volatility showed that the volatility is stable. As a result, investors can hold the stock. Among other things, the author recommends that Government should make sufficient regulatory effort that will improve efficiency of stocks performance and reduce volatility aimed at boosting investors’ confidence in the petroleum marketing sector and since the various ARCH and GARCH models showed volatility movement in stock returns, Nigerian government should look for new ways to diversify the economy from dependence on oil and explore other sectors like manufacturing sector and agricultural sector to reduce volatility in the economy and the overall effect on it.

DO ECONOMIC FORECASTERS BELIEVE THE STOCK MARKET IS EFFICIENT? EVIDENCE FROM GERMANY

The perception of market efficiency is quite different from the reality of market efficiency. We show using a large survey of German market forecasters that few respondents consistently believe that the stock market is currently efficient and will remain so. Past volatility tends to erode the view that the market is efficient and strengthen the belief that the market is inefficient.

Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility

The cryptocurrency market is highly volatile; this can be attributed to several factors such as being an emerging market that is purely digital and still evolving with many speculations taking place aligning with behavioural finance factors such as media and investors profile. This study aims to investigate the Autoregressive Conditional Heteroskedasticity (ARCH) and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) in forecasting selected 9 cryptocurrencies that represent over 80% of the total market capitalization. This study carries a time-series of daily data ranges from 2010 to 2020 base on each cryptocurrency starting date. The results show that the ARCH and GARCH have a significant effect in forecasting cryptocurrency market volatility which means that the past volatility of cryptocurrencies affects the current volatility of it. It also shows that bad and good news can significantly affect the conditional volatility of all cryptocurrencies returns. This study contributes to the investors’ understanding of the dynamics of the cryptocurrency market which enhances the ability to make informed decisions based on a scientific approach.

Nonlinear Interactions and Volatility Spillovers between Stock and Foreign Exchange Markets: The STVEC-STGARCH-DCC Approach

This study aims to investigate the interactions, volatility spillovers and smooth transition effects between stock and foreign exchange markets in emerging versus developed countries by the Smooth Transition Vector Error Correction-Smooth Transition GARCH with Dynamic Conditional Correlation model (STVE-STGARCH-DCC). The empirical results yield several findings. Firstly, boom stock markets in emerging countries will trigger their domestic currency appreciation, while prosperous stock markets in developed countries result in currency depreciation. Secondly, the conditional variances for stock markets mainly result from unexpected shocks, past volatility, and short-term impact effects, thus leading to a persistence of volatility in both emerging and developed markets. The conditional variances for foreign exchange markets display similar patterns but show weaker short-term impact effects and slower transition speeds. Thirdly, unexpected shocks in a stock market broadly affect its own stock volatility, while those only affect India’s volatility in the rupee market. In contrast, unexpected shocks in foreign exchange markets mainly affect foreign exchange volatility, except for India; however, those influence their stock volatility only for emerging countries, such as India and South Africa. Lastly, developed markets are more efficient than emerging markets are. JEL classification numbers: C32, C51, C52, G11, G15 Keywords: Asymmetric Effects, Bivariate STVEC-STGARCH-DCC, Market Efficiency, Nonlinear Model, Smooth Transition Auto-regression, ICSS Algorithm.

What Triggers Stock Market Jumps?

We examine next-day newspaper accounts of large daily jumps in 16 national stock markets to assess their proximate cause, clarity as to cause, and the geographic source of the market-moving news. Our sample of 6,200 market jumps yields several findings. First, policy news – mainly associated with monetary policy and government spending – triggers a greater share of upward than downward jumps in all countries. Second, the policy share of upward jumps is inversely related to stock market performance in the preceding three months. This pattern strengthens in the postwar period. Third, market volatility is much lower after jumps triggered by monetary policy news than after other jumps, unconditionally and conditional on past volatility and other controls. Fourth, greater clarity as to jump reason also foreshadows lower volatility. Clarity in this sense has trended upwards over the past century. Finally, and excluding U.S. jumps, leading newspapers attribute one-third of jumps in their own national stock markets to developments that originate in or relate to the United States. The U.S. role in this regard dwarfs that of Europe and China.

What Triggers Stock Market Jumps?

We examine next-day newspaper accounts of large daily jumps in 16 national stock markets to assess their proximate cause, clarity as to cause, and the geographic source of the market-moving news. Our sample of 6,200 market jumps yields several findings. First, policy news – mainly associated with monetary policy and government spending – triggers a greater share of upward than downward jumps in all countries. Second, the policy share of upward jumps is inversely related to stock market performance in the preceding three months. This pattern strengthens in the postwar period. Third, market volatility is much lower after jumps triggered by monetary policy news than after other jumps, unconditionally and conditional on past volatility and other controls. Fourth, greater clarity as to jump reason also foreshadows lower volatility. Clarity in this sense has trended upwards over the past century. Finally, and excluding U.S. jumps, leading newspapers attribute one-third of jumps in their own national stock markets to developments that originate in or relate to the United States. The U.S. role in this regard dwarfs that of Europe and China.

Co-movement of prices between biofuel co-products in Canada: ethanol, electricity, and pellets

The potential emergence of a second-generation ethanol industry in Canada will depend on future production technologies and prices. Financial viability might be improved by producing cellulosic ethanol with co-products such as lignin pellets or electricity. Financial returns to ethanol production will depend on price variability and possible price spillovers among ethanol and its co-products. We use a multivariate BEKK–GARCH approach to investigate past mean price interactions and volatility spillovers between ethanol and electricity prices. Wood pellets are investigated in a univariate framework because of data constraints. Results show substantial price interactions and volatility spillovers among these products. If a second-generation ethanol industry emerges, co-production of products from common feedstocks may strengthen already established relationships between the prices of these energy products. These conditions could create increased risk and the clustering of high–low price fluctuations among co-products. For investors, results suggest that risk reduction strategies should protect against correlated volatility. For policy makers, results suggest that policies that target one commodity may lead to unintended impacts on co-product(s). In sum, understanding links between markets is important for designing future policies and for insights into how a second-generation ethanol industry may emerge.

Momentum, Reversal, and Seasonality in Option Returns

Option returns display substantial momentum using formation periods ranging from 6 to 36 months long, with long/short portfolios obtaining annualized Sharpe ratios above 1.5. In the short term, option returns exhibit reversal. Options also show marked seasonality at multiples of three and 12 monthly lags. All of these results are highly significant and stable in the cross section and over time. They remain strong after controlling for other characteristics, and momentum and seasonality survive factor risk-adjustment. Momentum is mainly explained by an underreaction to past volatility and other shocks, while seasonality reflects unpriced seasonal variation in stock return volatility.

Should Mutual Fund Investors Time Volatility?

Increasing (decreasing) investment in an actively managed mutual fund when fund volatility has recently been low (high) leads to a significant improvement in investment performance. Specifically, volatility-scaled fund returns exhibit significantly higher alphas and Sharpe ratios than the original (unscaled) fund returns. Scaling by past downside volatility leads to even greater performance improvement than scaling by total volatility. The superior performance of volatility-managed mutual fund trading strategies is attributable to both volatility timing and return timing. Fund flows are negatively related to past fund volatility, suggesting that fund investors are aware of the benefit of volatility management.

INVESTIGATING ABNORMAL VOLATILITY TRANSMISSION PATTERNS BETWEEN EMERGING AND DEVELOPED STOCK MARKETS: A CASE STUDY

The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.

Does volatility traverse between emerging and frontier stock markets of Asia?

Given Asian market recognition at the forefront of the investment domain, the research examines volatility spillover and asymmetric transmission between emerging and frontier stock markets of Asia. Stock returns of two frontier and nine emerging markets, during the data period spanning from August 2000 to March 2020, were analyzed using multivariate asymmetric GARCH-BEKK model around the global financial crisis (GFC). The study results suggest that the structure of cross-markets shocks and volatility spillover between emerging markets are higher during post-GFC. Therefore, this diminishes the possibility of portfolio diversification and investment opportunities to the investors in most of the Asian emerging markets. In the case of Asian frontier markets, most of the volatility generates due to its past shocks and volatility traverse from Asian emerging markets are considerably less. Hence, asset allocations prospects exist in the Asian frontier stock markets. Nevertheless, safe investment strategies need to design to reap diversification benefits from these markets, particularly during financial turmoil and market distress in the future.

The Volatility of Stock Investor Returns

The volatility of investor returns depends not only on the volatility of the stocks investors hold but also on their time-varying capital exposure to these holdings. We provide comprehensive evidence on the volatility of investor returns using individual stocks, portfolios of stocks, and market indexes from the U.S. and major international stock markets. Our main finding is that the volatility of investor returns is higher than the corresponding volatility of stock returns in nearly all specifications. The relative magnitude of the volatility differential varies from as little as 10% and up to 75%, where this differential tends to increase with investment horizon. Probing into the drivers of this volatility differential reveals that firms issue more equity after low past volatility but before high future volatility, implying that managers cater to investor propensity to chase stability. Overall, taken together with existing evidence that investor returns tend to be lower than corresponding stock returns, this study suggests that the risk-return trade-off for stock investors is worse than previously thought.

Asymmetric volatility spillovers between world oil prices and stock markets of the G7 countries in the presence of structural breaks

AbstractThe purpose of this paper is to examine the volatility transmission between oil prices and the seven major stock markets (G7) using symmetric and asymmetric bivariate GARCH models incorporating structural breaks. Since these countries play a crucial role in the international economy, it is important for financial market participants to understand the volatility transmission process for designing optimal portfolio allocations. We examine weekly data over the period 1998–2017 and endogenously detect structural breaks in variance using an iterated algorithm in order to estimate the volatility dynamics with greater accuracy. The models were estimated using the maximum likelihood method, and the BFGS algorithm was employed to obtain the variance–covariance estimation and the corresponding standard errors. Overall, we find volatility spillovers between markets together with the fact that the oil market has a leading role. More importantly, we show that neglecting structural changes in variance and/or asymmetries may lead to biased volatility spillover effects. Furthermore, the results highlight that models ignoring structural breaks are significantly overestimating the effect of their own past volatility on the current volatility. In addition, we find that disregarding asymmetries could also lead to erroneous estimates of these effects. Finally, we compute dynamic risk‐minimizing hedge ratios and optimal portfolio weights, however, no significant discrepancies are found among the models considered.