Periods Of Low Volatility Research Articles

From Shanghai to Sydney: Chinese stock market influences on Australia

We examine whether China's growing importance to Australia as both a trade partner and engine of growth has been accompanied by financial market interdependence. We consider effects on the overall Australian market as well as the iron ore sector, which has been accounting for over half of Australia's exports to China in the years since the global financial crisis. Markov-switching analysis yields evidence of the Shanghai Composite being connected not only with the Australian iron ore sector but also the broad market Australian All Ordinaries index. These ties are found to be significant only during low volatility periods, however.

Term Structure of CDS Spreads and Risk-Based Capital of the Protection Seller: An Extension of the Dynamic Nelson–Siegel Model with the Business Cycle

This article proposes an extended Diebold-Li dynamic Nelson-Siegel model with factors following AR-GARCH processes to fit the term structure of CDS spreads. The proposed model is used to estimate the risk-based capital of a protection seller of CDS contracts. Using CDX North American Investment Grade Index and CDX North American High Yield Index data, we find the AR-GARCH process with the business cycle to outperform all the other models. The risk-based capital for a protection seller increases with the duration of the holding period. Moreover, the protection seller of CDS contracts on high-yield reference entity needs capital-at-risk at least twice the amount that is needed for similar CDS on the investment-grade reference entity. The observed high level of capital-at-risk is driven mainly by the high volatility period identified in our sample, because the low volatility period is characterized by low realized defaults and persistent decline in CDS spreads. TOPICS:Factor-based models, credit default swaps Key Findings • This paper proposes an extended version of the Diebold-Li dynamic Nelson-Siegel (DNS) model to fit the term structure of CDS spreads; with a family of AR-GARCH process with business cycle to capture the dynamics of the conditional mean and the conditional volatility of CDS spreads. • Using data on the CDX North American Investment Grade index (CDXIG) and the CDX North American High Yield index (CDXHY), the proposed model is used to determine the risk-based capital of a protection seller of CDS contracts. • Overall, the proposed AR-GARCH process with the business cycle outperforms all the other processes (AR, AR-GARCH, AR-EGARCH, and AR-GJR), which highlights the importance of the business cycle to better forecast CDS spreads.

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.

Examining the asymmetric monetary policy response to foreign exchange market conditions in emerging and developing economies

In many emerging market economies that have adopted inflation targeting, policymakers are concerned with the effects that strong appreciation/depreciation pressures and excessive volatility in exchange rates may have on economic stability and their ability to reach their inflation targets. The research in this paper addresses whether central banks have an asymmetric response to exchange rate conditions with respect to meeting their monetary policy objectives. Using a panel of 30 emerging market and developing economies, we employ a panel threshold regression analysis to determine whether policymakers respond differently during appreciation versus depreciation periods, during periods of high versus low exchange rate volatility, or when the country has a relatively high exchange rate pass-through effect. We find that policymakers react more strongly and significantly to depreciations with increases in domestic interest rates, while remaining inactive during periods of appreciation. Yet, during appreciationary periods, they react more strongly to deviations in output from its potential, suggesting a concern over competitiveness. Furthermore, volatility in exchange rates is a significant concern to policymakers, as they regularly claim, and when volatility is sufficiently high, policymakers react with higher interest rates to maintain stability and mitigate capital outflows. Lastly, with a stronger exchange rate pass-through effect, policymakers are more responsive to depreciationary pressure as it affects their ability to reach inflation targets.

Is there a risk and return relation?

ABSTRACTTraditional finance theory posits that the relation between the risk and return of stocks is positive. Equally, investment practice is often based on the contention that high (low) beta stocks earn higher (lower) returns. However, this fundamental relation is questioned by several researchers, who present mixed evidence. The purpose of this paper is to shed further light on this question by examining both market- and firm-level price data; employing a battery of tests, including individual market, panel and quantile regressions; analysing the nature of the relation during periods of high and low volatility and in bull and bear markets. The results indicate that there is no single robust relation between risk and return. Notably, the results suggest a positive relation when returns are high and during bear markets. Further, the finding of a positive relation is stronger at the market-level than the firm-level and over long time periods. However, a negative relation exists at low return levels, during bull markets and, even more so, at the individual firm level. Overall, the results suggest that the risk-return relation is switching in nature and is primarily driven by changing risk preferences. A positive relation exists when macroeconomic risk plays a larger role.

Using Markov-switching models with Markov chain Monte Carlo inference methods in agricultural commodities trading

In this work, the use of Markov-switching GARCH (MS-GARCH) models is tested in an active trading algorithm for corn and soybean future markets. By assuming that a given investor lives in a two-regime world (with low- and high-volatility time periods), a trading algorithm was simulated (from January 2000 to March 2019), which helped the investor to forecast the probability of being in the high-volatility regime at t + 1. Once this probability was known, the investor could decide to invest either in commodities, during low-volatility periods or in the 3-month US Treasury bills, during high-volatility periods. Our results suggest that the Gaussian MS-GARCH model is the most appropriate to generate alpha or extra returns (from a passive investment strategy) in the corn market and the t-Student MS-GARCH is the best one for soybean trading.

Impact of food price volatility on child health and education attainment

PurposeThe purpose of this paper is to find the impact of food price volatility on child health and education attainment in urban areas of Pakistan. This research also compares the two variables among the two time periods: the period of low volatile food prices (2014‒2015) and the period of high volatile food prices (2013‒2014). The rate of child immunization and the rate of child school attendance are used as proxies for child health and child education, respectively.Design/methodology/approachThis study employs propensity score matching (PSM) technique introduced by Rosenbaum and Rubin (1983), to overcome the selection bias problem in the observational studies.FindingsThe closing part of the paper concludes that both the rate of child immunization and the rate of child school attendance are significantly poorer for the households of Pakistan in the control period (of high food price volatility) as compared to the treated period (of low food price volatility). After controlling the problem of selection bias through PSM technique, it is found that there is a further increase in the rate of child immunization and the rate of child school attendance. It proves that the data were biased before applying the matching technique.Originality/valueThis study lengthens the literature by identifying the impact of food price volatility on child health and education of urban households of Pakistan, using high frequency data of PSLM/HIES, with the help of semi-parametric technique of matching. This type of micro-level research has not been conducted (nationally or internationally) so far; therefore, it would possibly open a sphere for policy makers to implement the suitable policies.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2019-0275.

Abnormal returns and systemic risk: evidence from a non-parametric bootstrap framework during the European sovereign debt crisis

We investigate the impact of European Central Bank (ECB) interventions on major European and Turkish stock and credit default swap (CDS) markets highlighting the importance of abnormal to excess abnormal returns in the systemic risk. In particular, we examine the impact of ECB announcements (news) on major European and Turkish financial markets (stocks and CDSs indices) for a high and low-volatility period, i.e., from November 6th, 2008 to December 31st, 2015. We also examine the market efficiency by using both an event study methodology and the Capital Asset Pricing Model. Moreover, the impact of the ECB events is measured by an event study and a systemic risk analysis. The results show that investors exposed to Finland, Sweden, Austria and Spain tend to be more vulnerable to risk and volatility, when ECB announcements are published.

Abnormal returns and systemic risk: evidence from a non-parametric bootstrap framework during the European sovereign debt crisis

We investigate the impact of European Central Bank (ECB) interventions on major European and Turkish stock and credit default swap (CDS) markets highlighting the importance of abnormal to excess abnormal returns in the systemic risk. In particular, we examine the impact of ECB announcements (news) on major European and Turkish financial markets (stocks and CDSs indices) for a high and low-volatility period, i.e., from November 6th, 2008 to December 31st, 2015. We also examine the market efficiency by using both an event study methodology and the Capital Asset Pricing Model. Moreover, the impact of the ECB events is measured by an event study and a systemic risk analysis. The results show that investors exposed to Finland, Sweden, Austria and Spain tend to be more vulnerable to risk and volatility, when ECB announcements are published.

How do volatility regimes affect the pricing of quality and liquidity in the stock market?

Abstract This paper shows how stock market volatility regimes affect the cross-section of stock returns along quality and liquidity dimensions. We find that, during crisis periods, low quality and low liquidity stocks experience relatively higher losses than predicted in normal times, while high quality and high liquidity stocks experience rather relatively lower losses. These findings lend strong support to the presence of cross-market and within-market flight-to-quality and to-liquidity episodes during crisis periods. During low volatility periods, however, low quality and low liquidity stocks earn relatively larger returns, while high quality and high liquidity stocks yield lower returns; suggesting that low volatility conditions benefit junk and illiquid stocks but not quality and liquid stocks. Finally, our results reveal that liquidity level dominates liquidity beta in explaining stock returns across the different market volatility regimes.

Structural Breaks in GARCH Processes Do Matter

Structural breaks in GARCH processes do matter for applications and model-based inference. To show this, I first propose a new approach to estimate change-point GARCH models with maximum likelihood, for which no method exists so far. Practical implementation of the proposed algorithm is discussed and its effectiveness and properties are demonstrated in simulation studies. Building on the new method, the paper shows empirically that the inclusion of structural breaks changes the results of typical model applications both qualitatively and quantitatively. First, I document that standard GARCH models systematically overpredict volatility in low volatility periods, and underpredict in high volatility times, while the change-point GARCH model is able to overcome this bias. Similarly, a GARCH option pricing model systemically overprices options in calm periods, and underprices in turbulent times. Finally, adding structural breaks to GARCH models with jumps drastically changes the inference on risk-premia as well as the intensity and occurrence of jumps.

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.

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.

Jump and Volatility Risk Premia:‎ Empirical Estimations Following Tail Events

We estimate post-jump volatility-decay risk premia as the predictable ‎difference between periods of high and low diffusive volatility. By ‎constructing straddle portfolios after positive and negative jumps occur, we ‎show that the gains that these hedged options' portfolios yield compensate ‎investors for the uncertain magnitude and duration of volatility decay, as well ‎as for vega exposure. This paper adds to the literature by distinguishing ‎between the premia after positive versus negative jumps, and by exploring ‎premia patterns over time. In particular, we find that GARCH(1,1) is an ‎inefficient identifier of jumps, and show that Hampel [1971] is a superior ‎procedure. ‎

Market risk and Bitcoin returns

Bitcoin is emerging as a distinct asset class among investors given its seemingly detached price behavior relative to market and economic fundamentals. Its incomparably high returns in recent years has further fuelled intense interest and investment into Bitcoin and cryptocurrencies at large. This paper cautions that Bitcoin prices, despite their seemingly attractive independent behavior relative to economic variables, may still be exposed to the same types of market risks which afflict the performance of conventional financial assets. Using a Markov regime-switching model to distinguish between regimes of high and low Bitcoin price volatility, this paper shows that while returns on the aggregate market portfolio cannot explain Bitcoin returns, other asset pricing risk factors, such as interest rates and implied stock market and foreign exchange market volatilities, are important determinants of Bitcoin returns. Distinguishing between periods of high and low Bitcoin price volatility reveals heterogeneity in the explanatory power of market risk factors; in particular, Bitcoin returns are more difficult to explain during periods of high volatility relative to periods with low volatility. This finding can partially explain why extant studies, which neglect to distinguish between exchange rate regimes in Bitcoin, have difficulty linking Bitcoin prices to economic fundamentals.

The role of the volatility index in asset pricing: The case of the Indian stock market

This study examines whether the volatility index, a proxy for aggregate volatility risk, can be used as an additional factor in the standard asset pricing model for the Indian stock market after controlling for well-documented risk factors. This study first examines the empirical performance of the capital asset pricing model, the Fama-French three-factor model and the Carhart four-factor model. On the basis of a GRS test, the Fama-French three-factor model is used as the baseline model to explore the role of the volatility index as an additional factor considered in the asset pricing model. The factor mimicking portfolio returns for the volatility index innovations is constructed and is included as an additional factor in the Fama-French three-factor model. The study provides strong evidence of size effects but relatively weak value-growth and momentum effect. Further, stock’s sensitivity to volatility index innovation is a priced risk factor during high volatility period, but not during low volatility period. Nevertheless, the addition of volatility risk factor leads to marginal improvements in the capacity for the model to explain the variation in stock returns. The results of this study will help financial analysts and corporate financial managers to evaluate the performance of professionally managed portfolios.

SWITCHING TO GOOD POLICY? THE CASE OF CENTRAL AND EASTERN EUROPEAN INFLATION TARGETERS

The paper analyzes how actual monetary policy changed following the official adoption of inflation targeting in the Czech Republic, Hungary, and Poland and how it affected the volatilities of important macroeconomic variables in the years thereafter. To disentangle the effects of the policy shift from exogenous changes in the volatilities of these variables, a Markov-switching dynamic stochastic general equilibrium model is estimated that allows for regime switches in the policy parameters and the volatilities of shocks hitting the economies. Whereas estimation results reveal periods of high and low volatility for all three economies, the presence of different policy regimes is supported by the underlying data for the Czech Republic and Poland, only. In both economies, monetary policy switched from weak and unsystematic to strong and systematic responses to inflation dynamics. Simulation results suggest that the policy shifts of both central banks successfully reduced inflation volatility in the following years. The observed reduction in output volatility, on the other hand, is attributed more to a reduction in the size of external shocks.

Nonparametric implied Lévy densities

This paper develops a nonparametric estimator for the Lévy density of an asset price, following an Itô semimartingale, implied by short-maturity options. The asymptotic setup is one in which the time to maturity of the available options decreases, the mesh of the available strike grid shrinks and the strike range expands. The estimation is based on aggregating the observed option data into nonparametric estimates of the conditional characteristic function of the return distribution, the derivatives of which allow to infer the Fourier transform of a known transform of the Lévy density in a way which is robust to the level of the unknown diffusive volatility of the asset price. The Lévy density estimate is then constructed via Fourier inversion. We derive an asymptotic bound for the integrated squared error of the estimator in the general case as well as its probability limit in the special Lévy case. We further show rate optimality of our Lévy density estimator in a minimax sense. An empirical application to market index options reveals relative stability of the left tail decay during high and low volatility periods.

Behavioral-Attention Phillips Curve: Theory and Evidences From Inflation Surveys

Abstract. I derive a theory of endogenous uncertainty and attention choice that can jointly account for two recent phenomena: (i) the flattening Phillips Curve and (ii) “well-anchored” inflation. In particular, I derive a Behavioral Attention Phillips Curve (BAPC) whose slopes on the output gap and inflation expectations decline when inflation is less uncertain. When inflation uncertainty is low, firms find it less costly to misperceive aggregate demand and inflation expectations, thus pay little attention to monetary shocks and change prices less. The dampened price response flattens the Phillips Curve. Inflation becomes more anchored with low uncertainty because costly attention motivates firms to rely more on “rules-of-thumb” such as the 2% inflation target. Using novel measures of inflation uncertainty constructed from surveys of inflation expectation, I show that the new Phillips Curve performs better both in-sample and out-of-sample than the traditional Phillips Curve with constant slope. Particularly, the BAPC does not generate the counterfactual prediction of large disinflation after the 2008-2009 Financial Crisis as does traditional Phillips Curves, resolving the Missing Disinflation Puzzle. I show that multiple equilibria arise with medium volatility due to the complementarity between pricing and attention choices. This gives rise to a novel policy paradox for the Central Bank and makes it hard to raise inflation in a quiet, low-volatility period.

Impact of Economic Policy Uncertainty on Chinese Carbon Price

There are direct as well as indirect linkages between economic policy uncertainty and carbon market through the channels of market fundamentals. This paper theoretically analyzes the linkages between economic policy uncertainty and carbon price and empirically examines the impact of Chinese economic policy uncertainty on Hubei carbon prices. A two-regime Markov-Switching process is introduced into the VAR model to examine the impact of economic policy uncertainty during different regimes of the carbon market. The empirical results show that the two-regime Markov-Switching model applies well in modelling the return series from Hubei carbon market during April 2014 to December 2017. Under the two different regimes, although the impacts from economic policy uncertainty are both significantly positive, the magnitude of the impacts differs. The impact of Chinese economic policy uncertainty on Hubei carbon price is larger during the low volatility period on carbon market than that during the high volatility period on carbon market.