Changes In Volatility Research Articles

Time-varying autoregressive distributed lag model with changing volatility for stress test

We present a novel time-varying autoregressive distributed lag (TV-ADL) model that allows for changes in both transmission mechanisms and innovation volatilities. The forecasting performance of the TV-ADL model has been substantially improved by removing the unrealistic traditional assumptions of constant volatility and constant inter-variable relationship. Our model is further adapted to stress tests mandated by the US Federal Reserve to generate conditional forecasts of the pre-provision net revenue of financial holding companies with large assets. The improvement of forecasting performance is demonstrated by the significant reduction of out-of-sample forecast errors at different horizons.

Timing Does Matter: Institutional Flaws and the European Debt Crisis

ABSTRACT Financial crises are complex phenomena. Yet, at the cost of some simplifications, the literature has identified two main elements causing the crises: macroeconomic imbalances and institutional design flaws. Economists recognise that both play some role, but disagree on their strength. Using Bai and Perron’s technique and an EGARCH model we contribute to the debate on the European debt crisis by identifying break dates and changes in volatility in daily values of 10-year public bond interest rates for Greece, Italy and Spain. The results are then related to key political and institutional events. The results of our econometric exercise uncover the following facts: the crisis began in May 2010; worsened after summer 2011, as the European authorities hastened to restructure the Greek debt; improved during summer 2012, when the ECB approved the OMTs, a new programme for the purchase of bonds. On the whole, the results are compatible with an interpretation of the crisis that considers the institutional flaws as the main cause.

Investor attention and oil market volatility: Does economic policy uncertainty matter?

This paper aims to investigate the impact of investor attention on the oil market volatility by using the Google search volume index as the measurement of investor attention. In particular, we decompose aggregate volatility into good volatility and bad volatility to gain a deeper insight into this issue. Additionally, we discuss whether the relationship between investor attention and oil market volatility changes with the adjustment of economic policy uncertainty in G7 and BRIC. The empirical results show that changes in investor attention mainly affect bad volatility rather than good volatility, and this impact is positive, symmetric, and transient. Interestingly, this impact can also lead to changes in future oil prices through the volatility feedback effect channel. Moreover, we find that the economic uncertainty policy can enhance the positive relationship between investor attention and bad volatility, but this relationship seems to be only sensitive to economic policy uncertainty in the US and Canada

A double mixture autoregressive model of commodity prices

Many commodity prices exhibit boom-bust type behavior: sustained periods of price increases, followed by sudden sharp collapses. Since around the year 2000, booms have become longer while busts have tended to be short but steep, suggesting a structural change in growth and persistence. We model these features of the data using a novel double mixture autoregression with two independent hidden Markov chains. One chain tracks shifts in mean growth rates that account for rising and falling prices, while a second chain tracks changes in volatility and lag-structure. While the two chains are independent, the persistence of price growth depends on the volatility state, which allows the lag-structure to vary across variance regimes. Estimation requires a two-stage Fisherian approach. Initially, location-related parameters are estimated while suppressing the underlying autoregressive structure. These parameters are then held fixed while the optimal lag-structure across variance regimes is determined. We apply the model to three industrial commodities price time series: Crude Oil, Aluminum, and Rubber. We find that in each case, the model captures boom and bust cycles, with data from more recent periods exhibiting higher volatility, longer price rallies, and steeper collapses.

Do central banks respond to exchange rate movements? A Markov-switching structural investigation of commodity exporters and importers

We analyse whether central banks in small open commodity exporting and importing countries respond to exchange rate movements, taking into consideration that there may be structural changes in parameters and volatility. Using a Markov Switching Rational Expectations framework, we estimate the model for Australia, Canada, New Zealand, Norway, Sweden and the UK. We find that the size of policy responses, and the volatility of structural shocks, have not stayed constant over the estimation sample. Furthermore, monetary policy has responded strongly to the exchange rate for many commodity exporters, most notablyNorway. This has had a stabilizing effect on the exchange rate. In particular, although the terms of trade are highly volatile among commodity exporters, the exchange rate has about the same volatility across all importers and exporters in the recent period. • Analyse if central banks in commodity exporters and importers respond to the exchange rate, allowing for time-varying policy rule and shocks. • Find strong evidence that neither policy responses nor the volatility of structural shocks have remained constant. • Central banks in three commodity exporting countries, Canada, New Zealand and Norway, respond strongly to the exchange rate in periods. • For Norway, the central bank also responds strongly to the exchange rate after adopting inflation targeting. • Policy rules with a high response to the exchange rate exacerbate the effects of external shocks on the domestic variables in advanced economies.

Extreme temperatures compromise male and female fertility in a large desert bird

Temperature has a crucial influence on the places where species can survive and reproduce. Past research has primarily focused on survival, making it unclear if temperature fluctuations constrain reproductive success, and if so whether populations harbour the potential to respond to climatic shifts. Here, using two decades of data from a large experimental breeding programme of the iconic ostrich (Struthio camelus) in South Africa, we show that the number of eggs females laid and the number of sperm males produced were highly sensitive to natural temperature extremes (ranging from −5 °C to 45 °C). This resulted in reductions in reproductive success of up to 44% with 5 °C deviations from their thermal optimum. In contrast, gamete quality was largely unaffected by temperature. Extreme temperatures also did not expose trade-offs between gametic traits. Instead, some females appeared to invest more in reproducing at high temperatures, which may facilitate responses to climate change. These results show that the robustness of fertility to temperature fluctuations, and not just temperature increases, is a critical aspect of species persistence in regions predicted to undergo the greatest change in climate volatility.

Asset Volatility and Capital Structure: Evidence from Corporate Mergers

We exploit cross-sectional variation in the predictable changes in asset volatility following corporate acquisitions to identify the effect of business risk on capital structure. We find that postmerger changes in leverage and cash holdings are strongly predicted by expected asset volatility changes estimated using premerger information. These capital structure adjustments are partly achieved through the choice of payment method. Our findings provide direct evidence for the coinsurance effect of mergers on debt capacity. More broadly, they suggest that firm risk is a first-order determinant of leverage, consistent with the tradeoff theory of capital structure. Our coefficient estimates imply that a one-standard deviation decline in a firm’s asset volatility corresponds to a 7.5-percentage point increase in leverage. This paper was accepted by Renee Adams, finance.

Association of enriched metabolites profile with the corresponding volatile characteristics induced by rice yellowing process

Change in metabolites and volatiles during yellowing process in six rice cultivars was analyzed. Based on the yellowness, the study indicated Japonica was more prone to yellowing than Indica rice. Metabonomics analysis showed most differential metabolites were up-regulated, in which pathways of flavone and flavonol biosynthesis were significantly enriched following the yellowing process. Meanwhile, 54 differential metabolites were overlapped in six comparative groups, which is characterized by commonly-shared metabolic regulation pathway in each rice. Phenylalanine content was increased, followed by the enhanced phenylpropanoids formation, showing transformation between primary and secondary metabolites during yellowing process. Furthermore, 43 volatile compounds were identified, and the yellowed rice had more volatiles, including ketones, alcohols, esters and hydrocarbons, suggesting a positive correlation with the yellowing. Compounds 6-methyl-5-hepten-2-one and 6,10,14-trimethyl-2-pentadecanone were increased steadily during yellowing process, which may be applied for monitoring rice yellowing progress. This investigation provides further insight for revealing rice yellowing mechanism.

Effectiveness of Augmented Dollar-Cost Averaging

Dollar-Cost Averaging investment strategy has been widely criticized by academics for decades. This article proposes an Augmented Dollar-Cost Averaging (ADCA) strategy that conditions investment strategy to the prevailing market environment. The ADCA investment strategy is more aggressive if the economy is expanding and more conservative if the economy is contracting. Changes in market volatility, unemployment rate and capacity utilization were used to determine whether the economy was expanding or contracting. Using the Sharpe ratio, coupled with first-order and second-order stochastic dominance criteria, this study shows the risk-reducing benefits of ADCA in the U.S. stock market between 1967 and 2018.

CEO Turnover and Equity Volatility

Purpose of the paper: This study aims to investigate changes in equity volatility around CEO turnovers. It proposes hypotheses regarding the impact of CEO performance and types of turnover on the changes in equity volatility. It extends the current understanding of the existing theories by providing new empirical evidence. Design/methodology/approach: This paper uses both event study and regression analysis to examine and test the hypotheses proposed empirically. Data are obtained from multiple databases. Findings: This study finds evidence that the relationship between changes in equity volatility and the likelihood of CEO turnovers does not monotonically increase, but is a function of the various types of turnovers and successions. Compared to the departure of outperforming CEOs, the change in equity volatility following the departure of underperforming CEOs is much greater. The positive relationship between the change in volatility and past underperformance is stronger for forced turnovers than for voluntary turnovers. Equity volatility is substantially lower when the CEO relinquishing the post remains as an executive chairman, especially for those successions involving outside appointments. Originality/value: This paper extends the strategy and ability hypothesis by investigating the behaviour of equity volatility around CEO departures in case of prior under or outperformance along with the forced or voluntary nature of the turnover and provide new empirical evidence on the theories.

The Price-Leverage Covariation as a Measure of the Response of the Leverage Effect To Price and Volatility Changes: Empirical Evidence

We study the sensitivity of the leverage effect to changes in the volatility and the price, showing the existence of an analytical link between the latter and the price-leverage covariation in settings with, respectively, stochastic and level-dependent volatility. From the financial standpoint, the results we obtain allow for the interpretation of the price-leverage covariation as a gauge of the responsiveness of the leverage effect to price and volatility changes. The empirical study of S&P500 high-frequency prices over the period March, 2018-April, 2018, carried out by means of non-parametric Fourier estimators, supports this interpretation of the role of the price-leverage covariation.

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that outlier-augmented SV schemes do at least as well as a conventional SV model. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the period since the pandemic’s outbreak, as well as for earlier subsamples of relatively high volatility.

Компаративний аналіз видів організаційної структури управління закладом вищої освіти

The modern stage of modernization of both the national and the world higher education system necessitates the transformation of higher education institutions into entrepreneurial, innovatively active organizations that are able to quickly adapt to volatile changes in the external environment. In this regard, many issues require a new solution regarding the management of higher education institutions (HEIs), i.e.: ensuring the quality of higher education, training the personnel, appropriate to labor market, providing various educational services, ensuring the continuity of education, and improving the organizational and economic mechanism of management of HEI. From the point of view of the system approach, a HEI is seen as an open system, a mechanism for converting input information or resources into final products, where effective organization of internal processes becomes one of the most important ways to obtain its competitive advantages. Based on the analysis of the main properties of HEI as an open dynamic system, it is defined that the most important systemic characteristic of university is its organizational structure, which includes four main aspects: description of the structure of subdivisions and departments; connections between them and the external environment; information circulating on these connections; functions performed by structural units and departments. The article is aimed at studying various types of organizational structure of management of higher education institution, determining the specifics of their application in the activities of the university, which operates in an open informational and educational space. The main types of organizational structure of management of HEI, built on the classical principle, are distinguished; weaknesses and strengths of their application for the management of HEI are determined. It is proved that the organizational management structures, built according to divisional and matrix types, are promising, since only they are able to adequately and promptly respond to changes both in the internal and external environment of HEI. A list of requirements to meet the contemporary, effective organizational structure of a higher education institution is formed, the highlighted requirements are: optimality, operational efficiency, reliability, economic efficiency, flexibility and stability of the management system. The carried out study allowed to conclude that a higher education institution in the process of its life cycle should timely adjust its strategic goals of activity and make adequate changes to the organizational structure in accordance with the new conditions in which it functions.

The COVID-19 Effects on Agricultural Commodity Markets

This study examines the effect of the COVID-19 pandemic on major agricultural commodity prices (cattle, cocoa, coffee, corn, cotton, hog, rice, soya oil, soybeans, soybean meal, sugar and wheat) using daily data from February 28, 2020 to November 20, 2020. We measured COVID-19 effect using a news-based sentiment index. A novel nonparametric causality-in-quantiles test was used to test the effect of the COVID-19 sentiment on agricultural commodity prices and price volatility. We found significant causality from the news-based COVID-19 sentiment to mean of the agricultural commodity prices in the lower and upper ranges of the quantiles, but not in the quantiles around the median, implying that the COVID-19 sentiment largely causes extreme changes in the agricultural commodity prices. Moreover, findings show that the COVID-19 sentiment is also causal for variance of agricultural commodity prices, but only above the quantile ranges above the first quarter. Thus, COVID-19 is causal for large volatility changes in agricultural commodity prices. Accordingly, the extremely negative sentiment associated with COVID-19 has not only caused a price crash in agricultural markets, but also significantly increased market risk.

Covenant Prices of US Corporate Bonds

This paper provides a novel approach to empirically determine prices of bond covenants based on transaction data for the US corporate bond market. Thereby, we are the first to measure price effects over the whole lifetime of bond contracts. We find that covenant prices vary significantly over time and are associated with changes in market-wide credit risk, volatility, and macroeconomic variables. During and after the financial crisis, this relationship is particularly pronounced leading to significantly higher covenant prices in the range of 5% of the overall bond yields. Apart from these time-series dynamics, there is also significant variation across bond and firm characteristics. In particular, covenant prices increase with interest rate, credit, and liquidity risk and are higher for firms that have more growth options, more tangible assets, and are smaller. Furthermore, we document a positive correlation between the prices of covenants and their subsequent inclusion rates.

The Impact of Renewable Energy Forecasts on Intraday Electricity Prices

In this paper we study the impact of errors in wind and solar power forecasts on intraday electricity prices. We develop a novel econometric model which is based on day-ahead wholesale auction curves data and errors in wind and solar power forecasts. The model shifts day-ahead supply curves to calculate intraday prices. We apply our model to the German EPEX SPOT SE data. Our model outperforms both linear and non-linear benchmarks. Our study allows us to conclude that errors in renewable energy forecasts exert a non-linear impact on intraday prices. We demonstrate that additional wind and solar power capacities induce non-linear changes in the intraday price volatility. Finally, we comment on economical and policy implications of our findings.

The Macroeconomic Cost of Climate Volatility

We study the impact of climate volatility on economic growth exploiting data on 133 countries between 1960 and 2005. We show that the conditional (ex -ante) volatility of annual temperatures increased steadily over time, rendering climate conditions less predictable across countries, with important implications for growth. Controlling for concomitant changes in temperatures, a +1C increase in temperature volatility causes on average a 0.9 per cent decline in GDP growth and a 1.3 per cent increase in the volatility of GDP. Unlike changes in average temperatures, changes in temperature volatility affect both rich and poor countries.

Predicting the Oil Market

We study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence of in-sample predictability. Our text measures, derived using energy news articles, hold their own against traditional variables. While we cannot identify ex-ante rules for selecting successful out-of-sample forecasters, an analysis of all possible two-variable models reveals out-of-sample performance above that expected under random variation. Our findings provide new directions for identifying robust forecasting models for oil markets, and beyond.

Four Decades of Canadian Earnings Inequality and Dynamics Across Workers and Firms

This paper studies the evolution of individual earnings inequality and dynamics in Canada from 1983 to 2016 using tax files and administrative records. Linking these individuals to their employers (and rich administrative records on firms) beginning in 2001, it also documents the relationship between the earnings dynamics of workers and the size and growth of their employers. It highlights three main patterns over this period: First, with a few exceptions (sharp increase in top 1% and declining gender gap), Canada has experienced relatively modest changes in overall earnings inequality, volatility, and mobility between 1983 and 2016. Second, there is considerable variability in earnings inequality and volatility over the business cycle. Third, the earnings dynamics of individuals are strongly related to the size and employment growth of their employers. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Incremental Volatility and Related Portfolio Analytics

In this note we prove a simple formula to compute the Incremental Volatility, i.e. the change in the portfolio volatility due to the removal of one asset from the portfolio. The common practice adopted in the literature and in the industry is to avoid the full recalculation of the portfolio volatility, but rather to use a first order approximation based on the gradient of the portfolio volatility. Here, we introduce a new exact formula that does not require to recompute the volatility of the new portfolio, but exploits quantities computed previously. Using the new formula, we can easily compute the effect on the portfolio volatility of any arbitrary change in the weigth of one asset as well as to determine the optimal trade, i.e. the weight change that allows the maximum reduction in the portfolio volatility. Due to their striking simplicity, the new formulas provide important portfolio analytics of immediate use in the asset management industry.