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Using a TVP-VAR connectedness approach, we investigate dynamic connectedness of either market returns or conditional volatility, between COVID-19 news sentiment (captured by the Google Trends Index), capital (SP however, they do not seem to have a prominent role as net transmitters of return spillovers.

The conditional volatility barely drops at macro-announcements. This is at odds with virtually all models that justify high macro-announcement returns through a high announcement premium. We propose an alternative explanation: macro-announcement days are, on average, with good news in existing sample periods. Our novel estimation approach reveals that high macro-announcement returns are not a manifestation of high conditional equity premiums but positive return innovations that are not averaged out in-sample. We find that macro-announcement days do not seem to operate with a separate mechanism. The patterns of macro-announcement days are not only well replicated by random samples from non-announcement days but also are fully rationalized by traditional equilibrium models that do not feature an announcement premium.

This paper studies the impact of investor sentiment on the Bitcoin returns and conditional volatility taking into account the Covid-19 outbreak by using different investor sentiment proxies and by ...

This study investigates the effect of crude oil price fluctuations on wheat, sugar, corn, and fertilizers. Results of Markov switching dynamic regression support evidence of two states. State 1, pertains to the low volatility of crude oil prices, and state 2, refers to higher volatility of crude oil prices. At state 1 higher levels of oil prices lead to a decline in food commodity prices, whereas in state 2, higher oil prices cause an increase in food commodity prices. Results of Dynamic Conditional Correlation (DCC) GARCH estimates indicate the coefficients of oil price levels are significant and positively associated with the conditional volatility of the four commodity prices, implying that fluctuations in global food commodity prices are not due to oil price volatility but due to the oil price levels attained at the extreme points.

This study revisits the topic of predicting aggregate equity returns out-of-sample by conditioning on economic variables through Bayesian model averaging (BMA). Besides simultaneously addressing pa...

Applied research on central banking has attempted to assess the effects of adopting full-fledged inflation targeting on several macroeconomic variables, while the impact of financial variables has been under-researched so far. This paper fills this gap by assessing and quantifying the effect of full-fledged inflation targeting on stock returns and their conditional volatility. For this purpose, it implements the Propensity Score Matching (PSM), which is immune against reverse-causality, self-selection and omitted-variable biases, on a sample of advanced countries over 1990:Q1–2015:Q1 period. The results suggest that full-fledged inflation targeting is effective in boosting stock market returns and reducing volatility.

This article aims to understand the transmission of volatility from the main market indicators of the European financial system, towards market interest rates, focusing on the prices of the swap with maturity of one year and payments of three months as endogen variable and the three main indexes of the European market as CAD, DAX3, and IBEX35, as an exogenous variable. The exogenous will absorb all the necessary information from the market agents as companies, banks, investments funds, or from externals disturbances as European Central Banks and will affect the levels and the slope of the swap prices. Introduction. SWAP is the financial instrument that will be employed to analyze the changes of the volatility in the market because it is the bigger derivative inside of the group of Fixed Income Assets. It is with the greatest depth and liquidity being one of the best instruments for developing market strategies of investment. Aim. Analyst the transmission of volatility from the systematic risk, represented by indices of the market, through the swap prices. Results. DAX30 and CAD transference of volatility are positive, in the particular case of the CAD the effect of transference is significantly positive and extended because the coefficient is greater than 1. IBEX35 provides an extended negative correction. Meaning for every one percentage point change in the IBEX35, It can be expected on average that the volatility of the swap will move in -4.19 percentage point. Conclusion: The slope of the curve o the endogen variables will be determined by the transmission of the volatility from the exogenous variables and the correlation level of the endogenous will adopt with each index

Despite increasing research on firm-specific news, it is unclear how the media affects the assimilation of macroeconomic news by futures markets. To fill this gap, we employ scheduled and non-scheduled macroeconomic announcements, as well as 60,030 instances of macroeconomic news reporting in China, to investigate the media’s role in transmitting macroeconomic news to the gold futures market. We find that macroeconomic announcements, while necessary, may not be sufficient to drive the gold futures market. Meanwhile, the media—as a vital intermediary—is essential for news absorption; the effect is stronger for national and political media. Further, the gold futures market asymmetrically reacts to good and bad news; only reporting on good news can invoke significant market responses. Our findings demonstrate the importance of the information environment cultivated by powerful media in improving the efficiency of financial markets, and help to explain why bad news moves slowly from a media perspective.

This paper aims to study the impacts of long memory in conditional volatility and conditional non-normality on market risks in Bitcoin and some other cryptocurrencies using an Autoregressive Fracti...

This paper explores the effects of global economic policy uncertainty (GEPU) on conditional volatility in the gold futures market using Markov regime‐switching GARCH‐MIDAS models. The in‐sample empirical results suggest that GEPU indeed contains predictive information for the gold futures market, and higher GEPU leads to higher volatility within the gold futures market. Moreover, the novel model, which adds Markov regime switching with time‐varying transition probabilities and the GEPU index, achieves relatively better performance than those of the other competing models from a statistical point of view. Furthermore, we discuss the asymmetric effects of different changes in GEPU on the gold futures market and the models' performances with different horizons, and we find that our new model has better predictive performance under negative changes in GEPU than under positive changes in GEPU. Further discussion also confirms that our previous findings are robust during two special cases, the global financial crisis and European debt crisis, during which the market suffered from fierce fluctuations and was fraught with considerable uncertainty.

This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using t...

This paper proposes the use of a spliced distribution with generalized Pareto tail for financial risk management. The proposed distribution is tailored to flexibly capture the heavy tail in asset return distribution. The parameters of the distribution can be estimated jointly with a conditional heteroskedasticity model. The estimated parameters can then be used to produce tail risk forecasts for risk management purposes. The use of the proposed distribution is illustrated by evaluating tail risk forecasts for a number of major stock indices.

This paper is a comparative study of volatility spillover effects in India and European indices. The analysis used various GARCH models, in order to measure conditional volatility (GARCH), asymmetric effect in the conditional volatility (T-GARCH), volatility persistence in conditional volatility (E-GARCH), impact of conditional volatility on conditional returns (M-GARCH) and volatility spillover (GARCH (1, 1), with exogenous variable, for the period 2005 to 2018. The major results, regarding volatility spillover, revealed that Indian stock market had exercised strong impact on selected European indices. Volatility spillover was found to be from Indian stock market to European indices and vice-versa. According to the T-GARCH model, there was significant asymmetric effect on the conditional volatility. The results of E-GARCH model established volatility persistence in conditional volatility.

This paper implements the analysis of volatility behaviour of the eight major cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Dash and Tether) for the period starting from October 13th 2015 to November 18th 2019. The GARCH-type models with heavy-tailed distributions are fitted to filter the conditional volatility exhibited by cryptocurrencies. Extreme value analysis based on the peak over threshold approach is then used to model the extreme tail behaviour of the cryptocurrencies. The predictive performance of the GARCH-EVT model in forecasting Value-at-Risk is evaluated at both 5% and 1% levels of significance. The backtesting results demonstrate the superiority of the GARCH-EVT model in both out-of-sample forecasts and goodness-of-fit properties to cryptocurrency returns and forecasting Value-at-Risk. Overall, the empirical results of this study recommend the heavy-tailed GARCH-EVT based model for modelling and forecasting the volatility of cryptocurrencies.

There are limitations in the understandings of investors’ overreaction to the volatility in less transparent industrial sectors. Investors investing in a less transparent sector are likely to over-interpret available market information. This article compares investors’ reaction to market shocks across different industrial sectors, through analyzing the information content in implied volatility using financial derivatives of individual companies in Singapore. Investors in the less transparent property and financial service sector are found to overreact on market shocks, further destabilizing the market. The findings imply that regulatory measures that increase the level of transparency could aid the stabilization of markets. JEL Classification: G13, G14, G18

Past literatures have not studied the impact of real-world events or information on the return and volatility of virtual currencies, particularly on the COVID-19 event, day-of-the-week effect, daily high-low price spreads and information flow rate. The study uses the ARMAGARCH model to capture Bitcoin’s return and conditional volatility, and explores the impact of information flow rate on conditional volatility in the Bitcoin market based on the Mixture Distribution Hypothesis (Clark, 1973). There were 3,064 samples collected during the period from 1st of January 2012 to 20th April, 2020. Empirical results show that in the Bitcoin market, a daily high-low price spread has a significant inverse relationship for daily return, and information flow rate has a significant positive relationship for condition volatility. The study supports a significant negative relationship between information asymmetry and daily return, and there is a significant positive relationship between daily trading volume and condition volatility. When Bitcoin trades on Saturday & Sunday, there is a significant reverse relationship for conditional volatility and there exists a day-of-the-week volatility effect. Under the impact of COVID-19 event, Bitcoin’s condition volatility has increased significantly, indicating the risk of price changes. Finally, the Bitcoin’s return has no impact on COVID-19 events and holidays (Saturday & Sunday).

This article aims to study the impact of peg structure on volatility behaviour and crisis vulnerability, considering the COVID-19 economic context. We adopt a comparative analysis of volatility beh...

We propose and evaluate mixed-frequency multivariate GARCH models for forecasting low-frequency (weekly) volatility based on high-frequency intraday returns (at 5-minute intervals) and on the overnight returns. The low-frequency conditional volatility matrix is modeled as a weighted sum of an intraday and an overnight component. The components are specified as multivariate GARCH processes of the BEKK type, adapted to the mixed-frequency data setting, and may enter the model as two separate components or as a single one. The models may further be extended by a nonparametrically estimated slowly-varying long-run volatility matrix. We evaluate the models in and out of sample using the 5-minute and overnight returns on four DJIA stocks (AXP, GE, HD, and IBM) from January 1988 to November 2014 and find that they systematically dominate a variety of models that only use lower-frequency data (weekly, daily, or close-to-open and open-to-close returns).

In this study, we examine the role of the Euro in currency co‐movements and contagion considering the USD exchange rates of six major currencies (i.e., EUR[DM], JPY, GBP, CHF, AUD, as well as, CAD). We identify five distinct intervals, each one corresponding to a different exchange rate regime or reflecting diverse economic developments. First, we model conditional volatility by introducing a novel DCC‐GARCH‐Copula model (based on GARCH selection criteria). Then, we investigate conditional volatility connectedness employing a Bayesian TVP‐(Pseudo)FAVAR model. This approach effectively refines existing measures of dynamic connectedness. Findings suggest strong co‐movements that differ across regimes. In addition, the EUR becomes weaker following both the collapse of Lehman Brothers and the decision for the first Economic Adjustment Programme for Greece. Following the announcement of the result of the EU referendum the GBP eventually receives influence from the EUR. Results remain robust to a series of tests.

We provide evidence suggesting that the assumption on the probability distribution for return in- novations is more influential for Value at Risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard alternatives for VaR fore- casting, and they should be preferred when estimating tail risk. The flexibility of the free power parameter in conditional volatility in the APARCH and FGARCH models explains their better performance. Indeed, our estimates suggest that for a number of financial assets, the dynamics of volatility should be specified in terms of the conditional standard deviation. We draw our results on VaR forecasting performance from i) a variety of backtesting approaches, ii) the Model Confi- dence Set approach, as well as iii) establishing a ranking among alternative VaR models using a precedence criterion that we introduce in this paper.

We introduce three dominance criteria to compare the performance of alternative value at risk (VaR) forecasting models. The three criteria use the information provided by a battery of VaR validation tests based on the frequency and size of exceedances, offering the possibility of efficiently summarizing a large amount of statistical information. They do not require the use of any loss function defined on the difference between VaR forecasts and observed returns, and two of the criteria are not conditioned by the choice of a particular significance level for the VaR tests. We use them to explore the potential for 1-day ahead VaR forecasting of some recently proposed asymmetric probability distributions for return innovations, as well as to compare the asymmetric power autoregressive conditional heteroskedasticity (APARCH) and the family of generalized autoregressive conditional heteroskedasticity (FGARCH) volatility specifications with more standard alternatives. Using 19 assets of different nature, the three criteria lead to similar conclusions, suggesting that the unbounded Johnson SU, the skewed Student-t and the skewed Generalized-t distributions seem to produce the best VaR forecasts. The unbounded Johnson SU distribution performs remarkably well, while symmetric distributions seem clearly inappropriate for VaR forecasting. The added flexibility of a free power parameter in the conditional volatility in the APARCH and FGARCH models leads to a better fit to return data, but it does not improve upon the VaR forecasts provided by GARCH and GJR-GARCH volatilities.

This study measures the volatility of cryptocurrency by utilizing the symmetric (GARCH 1,1) and asymmetric (EGARCH, TGARCH, PGARCH) model of GARCH family using a daily database designated in different digital monetary standards. The results for an explicit set of currencies for entire period provide evidence of volatile nature of cryptocurrency and in most of the cases, the PGARCH is a better-fitted model with student’s t distribution. The findings show positive shocks heavily affected conditional volatility as a contrast with negative stuns. Those additional analyses can be provided further support their findings and worthwhile information for economic thespians who are engrossed in adding cryptocurrency to their equity portfolios or are snooping about the capabilities of cryptocurrency as a financial asset.

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.

Bu çalışmada BIST Banka (XBANK) endeksinin volatilitesi koşullu varyans modelleri GARCH, TGARCH ve EGARCH kullanılarak modellenmeye çalışılmıştır. Çalışmada kullanılmak üzere 2010-2016 arası XBANK Endeksi günlük kapanış değerleri Thompson Reuters-Eikon veri tabanı üzerinden elde edilmiştir. 2016 yılı itibariyle bazı göstergelerdeki önemli değişikliklerin etkisi öncesi durumun tespit edilmesi amacıyla ilk aşamada bu aralık tercih edilmiştir. Elde edilen veriler yardımı ile ele alınan dönemde Bankacılık Endeksi logaritmik getiri serisi elde edilmiş ve endeks getiri volatilitesini hesaplama amacıyla GARCH(1,1), TGARCH(1,1) ve EGARCH(1,1) modelleri kurulmuştur. Kurulan modeller incelenerek uygun model belirlenmiş ve uygun model GARCH(1,1)’den elde edilen koşullu varyans yardımı ile volatilite hesaplaması yapılmıştır.

The stock market prices are volatile due to influence by many factors such as global trends, local trends, and economic conditions. Identification of Generalized autoregressive conditional heteroscedasticity(GARCH) order for stock data is a challenging task due to more fluctuation in stock prices and high variance in data. GARCH is considered to model the conditional volatility of a stock time series. Stock markets data often exhibit volatility clustering. Though many models which belong to autoregressive conditional heteroscedasticity (ARCH) family has proposed, but all the previous studies gave their affirmative consent on the performance of GARCH (1,1), which is considered the standard model, maybe because of the belief held by many researchers that the first lag of conditional variance can capture all the volatility clustering. This can be highly misguiding, especially when the stock market data has high order variance. The focus of this work is to make use of existing, well-known Information Criteria (IC) to identify the stock indices data-generating-process whenever the GARCH effect is present. Akaike Informations Criteria (AIC), Bayesian Information Criteria(BIC), and Hannan-Quinn information(HQ) criteria have used for this experiment. We studied different models with different parameter values and observed the abilities of information criterion in choosing the correct model from a given pool of models. For higher-order GARCH models and high sample sizes, AIC was able to correctly predict the model with high probability, while BIC and HQ performed well for smaller order models.

Traditional empirical models analyze impact of sentiments on financial market volatility using macroeconomic fundamentals or financial indicators. In this paper, recent methods of text based sentiment analysis of market from relevant news articles regarding economy and financial market are used. Two distinct market sentiments namely, positive and negative sentiments are constructed using different emotions which are identified through standard natural language processing methods. Further, the paper aims at proposing an augmented version of asymmetric GARCH model of conditional volatility for Indian stock exchange, Sensex, during the time period of April 19, 2007 to January 10, 2020 by incorporating aforementioned market sentiments. Empirical findings suggest dominant impact of negative market sentiment over positive one and it also provides evidence of noise trading in financially immature Indian stock market.

This paper studies how managers plan under uncertainty. In a new survey panel on German manufacturing firms, we show that uncertainty reflects change: Planning incorporates higher subjective uncertainty about future sales growth when the firm has just experienced unusual growth, and more so if the experience was negative. At the quarterly frequency, subjective uncertainty closely tracks conditional volatility of shocks: Both exhibit an asymmetric V-shaped relationship with past growth. In the cross section of firms, however, subjective uncertainty differs from conditional volatility: planning in successful firms—either large or fast-growing—reflects lower subjective uncertainty than in unsuccessful firms even when the size of the shocks is the same.

This paper analyzes the role of COVID-19 pandemic crisis in determining and forecasting conditional volatility returns for a set of eight cryptocurrencies through an asymmetric GARCH modeling approach. The findings report that the COVID-19 pandemic exerts a positive effect on the conditional volatility of those returns, while explicitly considering the pandemic event improves volatility predictions.

Purpose The purpose of this paper is to investigate whether Islamic stock indexes outperform conventional stock indexes, in terms of informational efficiency and risk, during the recent financial instability period. Design/methodology/approach The paper uses a state space model combined with a standard GARCH(1,1) specification while taking into account structural breakpoints. The authors allow for efficiency and volatility spillovers to be time-varying and consider break dates to locate periods of financial instability. Findings Empirical results show that Islamic stock indexes are more volatile than their conventional counterparts and are not totally immune to the global financial crisis. As regards of the informational efficiency, the results show that the Islamic stock indexes are more efficient than the conventional stock indexes. Practical implications Resulting evidence of this paper has several implications for international investors who wish to invest in Islamic and/or conventional stock markets. Policy makers and even academics and Sharias researchers should as well take preventive measures in order to ensure the stability of Islamic stock markets during turmoil periods. Overall, prudent risk management and precocious financial practices are relevant and crucial for both Islamic and conventional financial markets. Originality/value The originality of this study is performed by the use of time-varying models for volatility spillovers and informational efficiency. It considers structural break dates that think about the dynamic effect of informational flows on stock markets. The study was developed in a global framework using international data. The global analysis allows avoiding country specific effects.

We compute optimal hedge ratios between bitcoin and other financial assets by using conditional volatility estimates of different GARCH models for a period over January 03, 2011 to February 19, 2018. Gold is found to provide a better hedge against bitcoin. Following generalized orthogonal GARCH, which offers maximum hedging effectiveness, U.S.$1 long of bitcoin could to be hedged with 70 cents short of gold. Our findings are fairly robust to alternate specifications.

Through the application of Diagonal BEKK and Asymmetric Diagonal BEKK methodologies to intra-day data for eight cryptocurrencies, this paper investigates not only conditional volatility dynamics of major cryptocurrencies, but also their volatility co-movements. We first provide evidence that all conditional variances are significantly affected by both previous squared errors and past conditional volatility. It is also shown that both methodologies indicate that cryptocurrency investors pay the most attention to news relating to Neo and the least attention to news relating to Dash, while shocks in OmiseGo persist the least and shocks in Bitcoin persist the most, although all of the considered cryptocurrencies possess high levels of persistence of volatility over time. We also demonstrate that the conditional covariances are significantly affected by both cross-products of past error terms and past conditional covariances, suggesting strong interdependencies between cryptocurrencies. It is also demonstrated that the Asymmetric Diagonal BEKK model is a superior choice of methodology, with our results suggesting significant asymmetric effects of positive and negative shocks in the conditional volatility of the price returns of all of our investigated cryptocurrencies, while the conditional covariances capture asymmetric effects of good and bad news accordingly. Finally, it is shown that time-varying conditional correlations exist, with our selected cryptocurrencies being strongly positively correlated, further highlighting interdependencies within cryptocurrency markets.

This article investigates conditional growth volatility for industrial production in the U.S. during 1828–1915, taking as a reference sectoral and aggregate indexes constructed in connection to Davis (Quart J Econ 119:1177–1215, 2004). The period includes the major shock represented by the Civil War with the associated resource allocation distortions. The evidence mostly suggests high persistence in conditional volatility as would be found in later studies for the U.S. on GDP growth volatility. However, the evidence of asymmetric volatility appears to be more localized and salient examples of a stronger role of negative shocks on volatility can be identified in the cases of the textile, machinery and metals sectors that might have been more vulnerable to the Civil War. As for interindustry linkages, a complementary factor analysis suggests that the communality changes between the antebellum and postbellum eras. The relative importance of the aggregate shocks increased considerably after the Civil War. This indicates that the Civil War had significant effects in raising the cross-correlation between most sectors, suggesting substantial changes in the basic productive relationships in U.S. 19th century economy.

Through the application of three pair-wise bivariate BEKK models, this paper examines the conditional volatility dynamics along with interlinkages and conditional correlations between three pairs of cryptocurrencies, namely Bitcoin-Ether, Bitcoin-Litecoin, and Ether-Litecoin. While cryptocurrency price volatility is found to be dependent on its own past shocks and past volatility, we find evidence of bi-directional shock transmission effects between Bitcoin and both Ether and Litecoin, and uni-directional shock spillovers from Ether to Litecoin. Finally, we identify bi-directional volatility spillover effects between all the three pairs and provide evidence that time-varying conditional correlations exist and are mostly positive.

•This paper examines uncertainties and risks on excess stock returns G7 markets using monthly observations.•The estimated results find evidence supporting positive risk-return relation not only for risk as measured by conditional volatility but also for downside risk.•The stock returns are negatively correlated with economic policy uncertainty innovation (ΔEPU) in both local and global sources.•In addition to the negative effect of ΔEPU on excess stock returns, evidence also demonstrates that lagged ΔEPU positively contributes to a rise in stock return volatility.•The conventional test of risk-return relation without controlling ΔEPU is subject to a specification bias.

This paper examines the long memory feature in the conditional volatility and Value at Risk calculations of precious metals returns at different time frequencies. In doing so, we employ high frequency data while using FIGARCH models. Furthermore, significant long memory characteristics have been detected in absolute returns by using semiparametric Local Whittle (LW) approximation. The empirical evidence of the local memory parameter across different time intervals gives consistent approximates of the long memory parameter which indicates that the series show some degree of self-similarity. Further, empirical results show that the long memory characteristics remains fully consistent across different time intervals of data for conditional and unconditional volatility measures. This study is useful for investors and traders (with different trading horizons) with regard to forecasting volatility and calculating or implementing trading strategies at different time frequencies.

This paper investigates the volatility persistence in sovereign bond yields of India and China during study period of 2010–2018. For that purpose, the researcher has applied the Engle and Lee (in: Engle and Lee (eds) Cointegration, causality, and forecasting: a Festschrift in honour of Clive WJ Granger, Oxford University Press, Oxford, pp 475–497, 1999) C-GARCH model to decompose the volatility of 10-year sovereign bond yields of India and China into permanent and transitory components. The results reveal that permanent conditional volatility shows long memory with long-run component’s half-life decay ranges from 91 to 97 days for India and China, respectively. However, the temporary component of volatility much smaller with short-run component’s half-life decay ranges from .70 to .75 for India and China, respectively.

The leverage effect refers to the well-known relationship between returns and volatility for an equity. When returns fall, volatility increases. We evaluate the role of the leverage effect with regards to generating density forecasts of equity returns using well-known observation and parameter-driven conditional volatility models. These models differ in their assumptions regarding: The parametric specification, the evolution of the conditional volatility process and how the leverage effect is specified. The ability of a model to generate accurate density forecasts when the leverage effect is incorporated or not as well as a comparison between different model-types is analyzed using a large number of financial time series. For each model type, the specification with the leverage effect tends to generate more accurate density forecasts than its no-leverage counterpart. Among the specifications considered, the Beta-t-EGARCH model is the top performer, regardless of whether we attach the same weight to each region of the conditional distribution or emphasize the left tail.

We investigate whether depreciation of USD-CNY exchange rate causes direct or indirect effects on conditional variances in the international equity markets, especially of Japanese, ASEAN, Australian, and Indian markets. Employing APARCH and using MSCI indices we find a significant positive impact of Yuan’s depreciation on the conditional variances of Japanese, ASEAN and Australian equity markets. When USD-CNY exchange rate depreciates by 0.25 percent or more, volatility in the Chinese equity market causes a significant positive impact on the conditional volatility in the Japanese and Australian equity markets, though with some lag. USD-CNY exchange rate movements strongly influence the ASEAN equity markets across all time frames. The findings may enable investors to manage their portfolios of equity markets under consideration in the presence or absence of USD-CNY movements.

This study proposes that the overall state of the market, as captured by daily return and volatility, is an important determinant of volatility persistence. By utilizing the realized variance (RV) ...

Using the high-frequency data of Bitcoin, this paper investigates the long memory characteristics of the unconditional and conditional volatilities of Bitcoin at different time scales using the local Whittle (LW) estimator, the exact local Whittle (ELW) estimator and the ARMA–FIAPARCHmodel. The results show that the long memory parameter is significant and quite stable for both unconditional and conditional volatility measures across different time scales. This paper also examines the long memory characteristics of the unconditional and conditional “realized” volatilities of Bitcoin at different time scales using the local Whittle (LW) estimator, exact local Whittle (ELW) estimator and the ARFIMA model. Long memory is found to be significant and stable also in case of unconditional and conditional “realized” volatilities. The study also undertakes quarterly non-overlapping rolling window analysis to develop deeper insights into the evolution of long memory parameter, d, over the period. The results indicate high persistence in the Bitcoin market. This study has useful implications for different investors and market participants having varying exposures in the Bitcoin market depending on their trading horizons. The findings can help them in forecasting the expected volatility in the Bitcoin market and thereby in developing and implementing trading strategies.

The authors propose a novel approach to model investors' uncertainty using the conditional volatility of investors' sentiment. Working with weekly data on investor sentiment, 6 major U.S. stock ind...

This paper tests the risk-return relations for Asian stock markets by employing conditional volatility, local downside risk, regional downside risk, and world/U.S. downside risk. We find positive and significant intertemporal relations between excess stock returns and various risks. The evidence supports the risk-return tradeoff not only from local risk but also from external risk. The model is robust as it pertains to the risk of small variations as well as big shocks. The evidence supports positive risk-return relations across 10 Asian markets after controlling for the lagged dividend yield, higher moments of stock returns, and exchange rate variations.

Executive Summary. In this study, we investigate the time-varying response of conditional variance to endogenous and exogenous shocks. Using MSA-level monthly data spanning 31 years and two asymmet...

The present study demonstrates modelling of conditional volatility of NIFTY 50 using GARCH (1,1) model. The daily returns data of the Indian stock market index NIFTY 50 is used for the period ranging from April 2010- March 2020. The data is analysed using R software. The study estimates and interprets the results arrived in the summary output of the R environment and demonstrates how to forecast the volatility of the returns based on the estimated parameters. Extracting the time series of conditional volatilities is also demonstrated in the study.

The goal of this dissertation is to describe a methodology for modelling the volatility of high frequency financial data, considering its features and stylized facts. In order to account for the long-range dependence in conditional mean and conditional variance, ARFIMA and FI(E)GARCH models are used respectively, when observed. To account for the non-normality, skeweness and kurtosis, features observed in the the distribution of the log-returns in high frequency, the Skewed Student t and the Generalized Error Distribution (GED) are adopted for the innovation term of the aforementioned models. Wavelet shrinkage is used in a non-parametric identification and separation of the intraday jumps from the time series data. The application of this procedure is presented using real high frequency asset returns from the Brazilian Exchange and OTC, as well as exchange rates from cryptocurrencies traded in Crypto Exchanges. O objetivo desta dissertação é descrever uma metodologia para modelagem da volatilidade de dados financeiros de alta frequência, considerando suas particularidades e fatos estilizados. Os modelos ARFIMA e FI(E)GARCH são utilizados para modelar a longa persistência das séries na média e na variância condicional, respectivamente, quando isto for observado. A fim de contemplar não-normalidade, assimetria e curtose são utilizadas as distribuições t de Student Assimétrica e Distribuição Generalizada de Erros (GED) para o termo de inovações dos modelos supracitados. A limiarização de ondaletas é utilizada para identificação e separação dos \"jumps\" intradiários de forma não-paramétrica. A aplicação deste procedimento é apresentada utilizando séries financeiras reais de retornos de ações em alta frequência para ativos negociados no mercado à vista na bolsa de valores brasileira, além de séries de taxas de câmbio de criptomoedas, comparando o modelo semiparamétrico proposto a uma abordagem tradicional sem remover os \"jumps\".

Short-term electricity price forecasting has received considerable attention in recent years. Despite this increased interest, the literature lacks a concrete consensus on the most suitable forecasting approach. We conduct an extensive empirical analysis to evaluate the short-term price forecasting dynamics of different regions in the Swedish electricity market (SEM). We utilized several forecasting approaches ranging from standard conditional volatility models to wavelet-based forecasting. In addition, we performed out-of-sample forecasting and back-testing, and we evaluated the performance of these models. Our empirical analysis indicates that an ARMA-GARCH framework with the student’s t-distribution significantly outperforms other frameworks. We only performed wavelet-based forecasting based on the MAPE. The results of the robust forecasting methods are capable of displaying the importance of proper forecasting process design, policy implications for market efficiency, and predictability in the SEM.

Investors pay a substantial premium to hedge against fluctuations in volatility—the variance risk premium (VRP). The asset-pricing literature has presented numerous models with jumps in economic fundamentals to reproduce the properties and the time variation of the VRP. This paper shows that these quantitative results are almost exclusively driven by an inaccurate measure of conditional volatility. Solved accurately, conditional volatility exhibits—counterfactually—a strong procyclical pattern and the models do not deliver a sizeable VRP in response to jumps in state variables. The notion that the VRP is purely a compensation for fluctuations in macroeconomic uncertainty does not hold.

We analyze the performance of volatility targeting strategies. Conventional volatility targeting fails to consistently improve performance in global equity markets and can lead to markedly greater draw-downs. Motivated by return patterns in different volatility states, we propose a conditional volatility targeting strategy that only adjusts risk exposures in the extremes during high and low volatility states. This strategy consistently enhances Sharpe ratios and reduces draw-downs and tail risks for major equity markets and momentum factors across regions, with low turnover and leverage. Conditional volatility management can also be applied to tactical allocation between multiple assets or risk factors.

French Abstract: L’objet de cet article est de mettre en evidence l’existence d’une finance comportementale comme alternative a l’efficience informationnelle a travers l’apport de la finance comportementale a l’analyse de la volatilite excessive des cours boursiers dans le cadre de la BRVM. Volatilite excessive qualifiee par Shiller (2000) d’exuberance irrationnelle. L’etude empirique porte sur deux echantillons collectes sur Bloomberg, et sont repartis comme suit : (1) un echantillon regroupant toutes les actions de la BRVM, observees durant la periode 2000-2018. (2) un echantillon qui totalise 21 actions observees entre janvier 2005 et decembre 2018. Les resultats auxquels nous sommes parvenus, montrent l’existence d’une volatilite excessive issue d’une relation synchrone entre le volume de transaction du a des echanges des investisseurs sur-confiants, et la volatilite conditionnelle des rendements du marche. Mots cles : Efficience, Volatilite excessive, Finance comportementale, Exces de confiance, Comportement gregaire, GARCH et BRVM. English Abstract: The purpose of this article is to highlight the existence of behavioural finance, as an alternative to informational efficiency, through the contribution of behavioural finance to the analysis of excessive stock price volatility in the framework of the BRVM. Excessive volatility qualified by Shiller (2000) as irrational exuberance. The empirical study is based on two samples collected on Bloomberg, and is distributed as follows: (1) a sample of all BRVM actions observed during the period 2000-2018. (2) a sample totalling 21 actions observed between January 2005 and December 2018. The results show the existence of excessive volatility resulting from a synchronous relationship between the volume of transactions due to overconfident investor trading and the conditional volatility of market returns.