Top PDF The relationship between implied and realised volatility: Evidence from the Australian stock index option market

The relationship between implied and realised volatility: Evidence from the Australian stock index option market

The relationship between implied and realised volatility: Evidence from the Australian stock index option market

good news enters into the market between the trade of option and the time when closing index level and futures price are recorded, then the recorded index level or futures price will be higher than the index level or futures price simultaneously corresponding to the option price, indicating that implied call (put) volatility underestimates (overestimates) the true implied volatility. A similar situation can happen with bad news which may lead to deviations in the opposite direction. In reality, good news and bad news come randomly, and hence the two effects can offset each other and the computed implied volatility will not deviate consistently from the true volatility. However, this non-synchronous measurement does cause an errors-in- variables problem (EIV), which leads to the correlation between the explanatory variable and the error term in our subsequent regressions.
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Relationship of the change in implied volatility with the underlying equity index return in Thailand

Relationship of the change in implied volatility with the underlying equity index return in Thailand

The leverage effect posits that stock return shocks lead to asymmetric changes of expected volatilities in stock markets (see details in Black, 1976, and Christie, 1982). For the implied volatility literature, the evidence on the asymmetric impacts of the underlying index returns on implied volatility indices has been recently well- documented. 1 The implied volatility index can measure investors’ sentiment or fear. Investors’ fear is defined in the sense that a decline in the equity index or negative index return and if negative return is associated with an asymmetrically larger rise in the implied volatility index, investors will take this phenomenon into account when they make decision. The asymmetric relationship between index return and the change in implied volatility index is well documented (see for example, Flemming et al., 1995, Whaley, 2000, and Giot, 2005). Specifically, Giot (2005) finds that the S&P100 index exhibits the statistically negative relationship with its implied volatility. The relationship exhibits asymmetry and thus indicates that negative stock return yields bigger change in the corresponding implied volatility than positive return does. Bollerslev and Zhou (2006) find that the leverage effect is always stronger for implied volatility than realized volatility in the US stock market. Dennis et al. (2006) examine the dynamic relation between daily stock return and daily innovations in option- derived implied volatility. They find that the asymmetric relation between return and implied volatility primarily stems from systematic market-wide risk factors rather than aggregate firm-level effects. In other words, the return-implied volatility relation should be a market phenomenon. Hibbert et al. (2008) examine the short-term dynamic relation between the S&P500 and Nasdaq 100
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COMPARISON BETWEEN IMPLIED AND HISTORICAL VOLATILITY FORECASTS: EVIDENCE FROM THE RUSSIAN STOCK MARKET. Denys Percheklii. MA in Economic Analysis.

COMPARISON BETWEEN IMPLIED AND HISTORICAL VOLATILITY FORECASTS: EVIDENCE FROM THE RUSSIAN STOCK MARKET. Denys Percheklii. MA in Economic Analysis.

Another article, which also examined liquid market (UK FTSE), was written by Gwilym and Buckle (1999). The main contribution of this study is that the authors examine relative accuracy of several different time horizons. The conclusion is that the best forecasting method (either implied or historical volatility) depends on the time horizon and data frequency. The data consists of the American-style FTSE index options at LIFFE that expire on a monthly basis. With the exception of June and December, all contracts trade for four calendar months. They examined five forecast horizons: 5–20 trading days, 21–40 trading days, 41–60 trading days, 61–80 trading days and more than 80 trading days. They ignored horizons of less than 5 trading days because of possible distortions in option markets due to approaching maturity.
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Dynamics of the Relationship between Implied Volatility Indices and Stock Prices  Indices: The Case of European Stock Markets

Dynamics of the Relationship between Implied Volatility Indices and Stock Prices Indices: The Case of European Stock Markets

However, several empirical studies have shown that implied volatility indices are less informative with regard to future stock market volatility compared to other alternative ones. In the same vein, a study conducted in the US market by Dowling and Muthuswamy (2005) calculated implied volatility index AVIX using the same methodology that VIX-New. They found that the AVIX index represents a poor estimator of future volatility relative to those from autoregressive models. In addition, Koopman et al. (2005) on the basis of a study conducted in the US market during the period from January 6, 1997 to November 15, 2003, they found that implied volatility index VIX contains additional information in relation to a stochastic volatility model. However, this result is not confirmed using GARCH specification. Therefore volatility index is not the best measure for forecasting future volatility of the US stock market. Similarly, Becker et al. (2007) through a study conducted during the period from 2 January 1990 to 17 October 2003 found that implied volatility index VIX doesn’t contain any additional information compared to different volatility forecasting models. This result indicates that option market (on the S&P 500) is unable to anticipate the movements of the stock market future volatility. Therefore, they considered these markets as inefficient. More recently, the study of Padhi and Shaikh (2014) using a time period from 4 June 2001 to 31 May 2011, showed that implied volatility of call and put options on the currency from S & P CNX Nifty contains relevant information about future realized volatility based on underlying stock index returns. In contrast, the historical volatility contains additional information with respect to the latter. Referring to the first part of the literature review, we propose to validate the following assumptions:
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The KOSPI200 Implied Volatility Index: Evidence of Regime Switches in Volatility Expectations *

The KOSPI200 Implied Volatility Index: Evidence of Regime Switches in Volatility Expectations *

As indicated by the distributional statistics reported in Table 1, volatility expecta- tions in the Korean market are typically higher than those in the US market. How- ever, with the total sample is divided into two equal sub-periods, it appears that the differences in volatility are more salient during the more volatile crisis period of early KOSPI200 option trading. Implied volatility in the Korean market is not only found to be higher than in the post-crisis 2001~2006 period, but it also exhibits more vola- tility of its own, judging from the sample estimates of standard deviations. The Asian financial crisis seems to exert a significant increase in the perceived level of uncer- tainty as well as the degree of fluctuations in volatility expectations. The estimates of implied volatility are on average, found to be close to the annualized standard devia- tions of returns. Indeed, the average expected volatility is reflective of the changing level of uncertainty from the early trading to post-crisis periods, which are associated with annualized standard deviations of 55% and 30%, respectively. The higher mo- ments suggest that the distributions of returns and implied volatility are leptokurtic and the Jarque-Bera statistics strongly reject the hypothesis of normal distribution. The results of unit-root tests indicate that the time-series of stock market returns and implied volatility are stationary for both markets.
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Sovereign rating actions and the implied volatility of stock index options

Sovereign rating actions and the implied volatility of stock index options

IV on average reduces by 2.4 percentage points. This is similar to findings in prior literature on the impact of rating signals in other markets in the sense that upgrades generally do not trigger significant reactions from financial markets (Kaminsky and Schmukler, 2002; Gande and Parsley, 2005; Ferreira and Gama, 2007). However, the reduction in IV in response to downgrades is unexpected. Why does the option market consider an equity market in a recently downgraded country (i.e. a lower creditworthiness), to be less uncertain? One possible justification is that the market anticipates credit problems in advance and rating downgrades might serve as means of confirming the market anticipation (see Beber and Brandt, 2006). However, there is no evidence that upgrades and downgrades are anticipated by the market within the prior week. Rating upgrades (downgrades) might be anticipated further in advance. Increasing the length of time windows could answer the question. However, this approach encounters a rating clustering problem and reduces the number of (clean event) observations, hence, the power of the tests. The later methodology relaxes this constraint.
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Sovereign rating actions and the implied volatility of stock index options

Sovereign rating actions and the implied volatility of stock index options

IV on average reduces by 2.4 percentage points. This is similar to findings in prior literature on the impact of rating signals in other markets in the sense that upgrades generally do not trigger significant reactions from financial markets (Kaminsky and Schmukler, 2002; Gande and Parsley, 2005; Ferreira and Gama, 2007). However, the reduction in IV in response to downgrades is unexpected. Why does the option market consider an equity market in a recently downgraded country (i.e. a lower creditworthiness), to be less uncertain? One possible justification is that the market anticipates credit problems in advance and rating downgrades might serve as means of confirming the market anticipation (see Beber and Brandt, 2006). However, there is no evidence that upgrades and downgrades are anticipated by the market within the prior week. Rating upgrades (downgrades) might be anticipated further in advance. Increasing the length of time windows could answer the question. However, this approach encounters a rating clustering problem and reduces the number of (clean event) observations, hence, the power of the tests. The later methodology relaxes this constraint.
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Does the Implied Volatility Index Have  Signaling Power? Evidence from Mexico

Does the Implied Volatility Index Have Signaling Power? Evidence from Mexico

In the way of Whaley [6], the relation between the VIX and SPX is asymmetric, so the VIX is an investors’ fear gauge in a market fall rather than an investors’ excitement gauge in a market rally. As a contrarian indicator, VIX is more relevant at market bottom 5 . Ralf Becker, Adam E. Clements and Andrew McClelland [7] consi- dered two issues relating to the information content of the VIX 6 . Silmai [8] investigated the information spillov- er between VIX changes and SPX returns. N. Bada and Y. Sakurai [9] investigated whether macroeconomic va- riables can predict the regime switches in the VIX index 7 . Jianhua Gang and Xiang Li [10] used the bivariate semi-nonparametric (SNP) model by Gallant and Tauchen [11] to study the contemporaneous relationship be- tween the innovation of VIX and the expected SPX returns 8 . Ghulam Sarwar [12] have examined whether the relation between stock market returns and VIX has changed over time 9 . Kozyra and Lento [13] provided an in- sight into the relation between the VIX and technical analysis 10 .
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On the Economic Premium Principle

On the Economic Premium Principle

Another approach used to determine the pricing kernel is that of Bühlmann [12]. Bühlmann [12] explicitly models a market in which the positions for the asset are considered, and derives a pricing kernel from investors’ optimized be- havior and the market equilibrium. Therefore, the pricing kernel is determined endogenously. Bühlmann’s approach has been applied in several studies (Iwaki et al. [13], Iwaki [14], Kijima et al. [15], Takino [16] [17]). Our study is based on that of Takino [5]. That is, we construct a model where the market participants determine the optimal position for a claim to maximize their utility, and provide the pricing kernel to clear those positions. Therefore, our model demonstrates how to obtain a pricing kernel from the market equilibrium of the option mar- ket. Our approach also intuitively explains how to derive the risk-neutral density given in previous studies (Bakshi et al. [8]. This is the first contribution of this study.
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The Greek Implied Volatility Index: Construction and Properties

The Greek Implied Volatility Index: Construction and Properties

Table 3 shows the results from the Granger causality test using two lags (K=2). We can see that R Granger-causes ∆GVIX (i.e., rejection of the null) while the reverse does not hold. This result is robust to the choice of K. Moreover, it is in contrast to Malz (2000). He also ran similar Granger causality tests, and he found that several measures of volatility (constant maturity implied, historical, exponentially weighted moving average) could predict the future (squared) returns of various assets 10 . Our findings are of particular importance to an investor who has a position in FTSE/ASE-20 options. They suggest that he can use the returns of the underlying asset in order to forecast the future movement of the implied volatility, and hence of the option price. On the other hand, the Greek implied volatility index does not contain information regarding the direction of future returns.
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A Macroeconomic Perspective on Stock Market Return Volatility: Forecasting, Causes and Consequences

A Macroeconomic Perspective on Stock Market Return Volatility: Forecasting, Causes and Consequences

Political instability can affect stock market return volatility through various channels. For one thing, instability is likely to result in sudden changes in government economic policy, to which investors respond by altering their market strategies, thus causing sharp fluctuations in stock prices. For another, instability generates uncertainty about the future socioeconomic environment. This in turn hinders investment prospects and makes the economy generally more vulnerable to other idiosyncratic or aggregate shocks. Building on these considerations is a large body of research that investigates the impact of political instability on stock market performance. Most of this literature focuses on stock returns, concluding that greater political instability reduces stock market returns (see, for example, Lehkonen & Heimonen, 2015; Dimic et al., 2015), instead of working empirically on the linkage between political instability and stock market return volatility. Boutchkova et al. (2011) use industry level data, while Chau et al. (2014) focus on the MENA subregion. Asteriou and Sarantidis (2016) use a GARCH approach on a sample of OECD countries.
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On the pricing of illiquid options with Black-Scholes formula

On the pricing of illiquid options with Black-Scholes formula

However, an inexperienced user can be surprised by several consequences of such approach. First, since the illiquid options we wish to price have obviously at least some of the parameters different to the options used to get the implied volatility, we need to execute some kind of interpolation and subsequent smoothing to get nice resulting smile or smirk of the volatility curve or even surface. However, without respecting several important rules, the results might lead to arbitrage opportunity, ie. the option prices calculated with such volatilities might be mutually inconsistent, see eg. Benko et al. (2007). Second, the market mostly provides the maturity, moneyness, option price and implied volatility. But in the Black-Scholes formula there are two parameters more – the interest rate and dividend yield. Hence, the second issue the user is faced to is what are the market expectations about the interest rate and dividends over the option life?
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The Behaviour of India's Volatility Index

The Behaviour of India's Volatility Index

volatility index Ivix mirrors the empirical regularities normally exhibited by stock returns volatility. Hence, introducing trading products with Ivix as underlying may be contemplated. At present volatility trading is possible in India; as there is an active and liquid market in options trading, launching derivatives based on volatility index will pave way for trading pure volatility in an economical and a convenient way. Volatility trading strategies such as straddles need to be adjusted frequently as prices move else they become directional bets. The study shows a negative relationship between Ivix and Nifty returns which will be quite beneficial to investors, as including Ivix may lead to diversification benefits to investors. More importantly the significant negative relationship indicates volatility products will act as catastrophic hedging tools. In other words, inclusion of volatility index in a portfolio will provide the much needed insurance, particularly in market crashes. This is because volatility peaks during market falls and hence spot market losses could be offset by gains on the volatility front. Even though exchange traded derivatives are not currently available in India at least institutional investors can use the volatility index as the underlying and trade in OTC products such as volatility/variance swaps. To conclude, the study shows that India's volatility index reflects most of the stylised facts of volatility and hence it seems to be serving the purpose. Further studies may examine the predictive power of volatility index and examine the co-movements of Ivix with other global volatility indices.
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Herding evidence in Chinese stock market : a study of the relationship between stock price index and trading volume based on behavioral finance theory

Herding evidence in Chinese stock market : a study of the relationship between stock price index and trading volume based on behavioral finance theory

On one hand, one investor might profit from the market inefficiency if he or she is the only one person who understands the mechanism of the irrational patterns of the market. Just as Buffett said “Be fearful when others are greedy. Be greedy when others are fearful”. It can be a philosophy derived from behavioral study. On the other hand, the herding phenomenon is a big problem for the development of Chinese stock market. It makes the market very inefficient. Herding causes people to be extremely optimistic and thus market bubble. In 2007, The Shanghai Stock Exchange index rocketed from 2,700 points to 6,400 points, a 130 percent rise. However, the performance of those listed companies on which the growth of the domestic stock market depends has not made much noticeable progress although stock reforms have been carried out in majority of these firms.
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Mexican Stock Market Index Volatility

Mexican Stock Market Index Volatility

On this basis, the paper’s results suggest that banks’ responses will vary considerably from one European economy to another reflecting cross-country variations in the tightness of capital constraints, banks’ net cost of raising equity, and elasticities of loan demand with respect to changes in loan rates. The country-by- country estimations which include both large and small banks for which data is available in each country suggest that the net cost of raising equity by 1.3 percentage points ranges from 1 basis point in Sweden to 20 basis points in Ireland. Similarly the estimated elasticities of loan demand range from 1.0 percent in Ireland to 6.59 percent in Denmark. As a result the average impact of a 1.3 percentage point increase in the equity-asset ratio on loan growth for the crisis countries is 5.07 percent. This impact is significantly higher in the non-crisis countries such as Ireland and Denmark. The potential for a substantial impact of capital requirements makes it even more important for policy makers in these countries to identify exactly why the elasticity of loan demand or cost of equity is so high in these economies.
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Mexican Stock Market Index Volatility

Mexican Stock Market Index Volatility

In contrast, theory of random walks says that stock prices are determined in a random walk, its mean, cannot predict future prices from past prices (Fama, 1965). In statistical terms, theory says that the price changes are random variables, independent and identically distributed (Johnston & Dinardo, 1997). That is, past prices do not provide such information that can be used to predict future prices. Fame (1970) proposed three levels of market efficiency with respect to the information reflected in prices. The weak form holds that the history of stock prices does not contain information that can be used to obtain yields above which gives a random portfolio.
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Mexican Stock Market Index Volatility

Mexican Stock Market Index Volatility

The aim of the study was to evaluate the extent to which Earnings Per Share is influenced by the level of fixed assets maintained by brewery firms in the Nigeria brewery industry. It sought to determine the significance and nature of the interactions between firm size and financial performance in Nigeria brewery industry from 2000 to 2013. The Engle and Granger 2-step cointegration approach, in a simple regression framework, was adopted in the data analysis with a model to estimate the error correction period. The time series data were tested for stationarity to avoid spurious regression, applying the Augmented Dickey Fuller (ADF) procedure. The test revealed that the study variables were integrated of the same order I(2), indicating a possible cointegration. Firm Size has both short and long term positive effect on EPS; with a significant long run influence. There is no causality running from either EPS to Total Assets or otherwise at both periods. The implication is that firm size does not granger cause EPS and vice versa in Nigeria brewery industry. The study further reveals that the distortions affecting EPS, resulting from firm size, in the long run, could be corrected in approximately six (6) months. Consequently, to improve on financial performance, the firms within the industry should strive to fully automate their production lines, thereby increasing their asset base, in order to enhance product quality and packaging, meet the demands of customers at short notice, remain relevant amidst stern competition in the industry and avoid stock out costs.
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Relationship Between Stock Market Conditions and Investors Reactions: Evidence from Nigerian Stock Market

Relationship Between Stock Market Conditions and Investors Reactions: Evidence from Nigerian Stock Market

Anchoring was defined by Kahneman and Tversky as a phenomena used in the situation when people use some initial values to make estimation, which are biased toward the initial ones as different starting points yield different estimates [14]. Anchoring occurs in the stock market when the range for the price of a stock is fixed by current observations. When considering the sale of a stock, investors refer to the initial purchase price of the stock, therefore, today prices are often determined by past prices which could result to under-reaction to unexpected changes. Anchoring is related to representativeness as it also reflects that people often focus on recent experience and tend to be more optimistic when the market rises and more pessimistic when the market falls [23].
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Mexican Stock Market Index Volatility

Mexican Stock Market Index Volatility

Over the decades, the concept of corporate social responsibility (CSR) has continued to grow in Importance and significance. The idea is to make business enterprises have some responsibilities to society beyond that of making profits for the shareholders. it connotes conducting businesses on a reliable, sustainable, and desirable basis that respect ethical values, people, communities, and the environment. Although, corporate social responsibility practices apply to all firms, the social and environmental challenges however are to a large extent associated with manufacturing firms because of the significant impact of their activities on the environment. This paper examined how markets respond to the corporate social responsibility activities of listed manufacturing firms in Nigeria. It employed correlation research design using panel data from a sample of 19 firms for a period of 6 years (2008-2013). Ordinary Least Squares (OLS) regression technique was employed in the data analysis. The study found that corporate social responsibility of manufacturing firms in Nigeria is relevant and informative to investors. Especially, the study found that corporate social responsibility on society; environmental sustainability and owners’ wealth maximization have significantly impacted on the market values of listed manufacturing firms at 99% confidence level during the period covered by the study. The study however did not find evidence that corporate social responsibility on employees and regulatory compliances have any significant relationship with market values during the period under review. The paper recommends that manufacturing companies in Nigeria should double their efforts towards corporate social responsibility aimed at addressing the peculiarity of the social economic development challenges of the country (poverty alleviation, health care provision, infrastructural development, structure and education). This could send positive message to the market and enhance their value in return; it will also help create conducive atmosphere for conducting businesses.
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Granger-couse effect on trading volume and stock return volatility: evidence from Malaysian ACE market

Granger-couse effect on trading volume and stock return volatility: evidence from Malaysian ACE market

The Empirical Relationship between Stock Returns, Return Volatility and Trading Volume in the Brazilian Stock Market. Social Sciences Research Network[r]

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