Top PDF Sovereign rating actions and the implied volatility of stock index options

Sovereign rating actions and the implied volatility of stock index options

Sovereign rating actions and the implied volatility of stock index options

This paper investigates the interaction between sovereign rating news and the equity index option market. This market is typically inhabited by institutional informed traders (see Chakravarty et al., 2004; Chen et al., 2005; Jin et al., 2012). Much literature identifies that the derivative markets play a leading role in the price discovery process (e.g. Blanco et al., 2005; Acharya and Johnson, 2007; Avino et al., 2013). Therefore, the dynamics of derivative markets can provide important information regarding the credit quality of underlying entities. In 2011, the turnover of equity index options traded on organised exchanges over the world was US$ 166 trillion (Bank for International Settlements, 2012). The equity index option market is the second largest segment of exchange-traded financial derivative markets, after interest rate derivatives. Given the prominence of both derivative markets and CRAs, interesting questions about the interaction between the index option market and credit rating actions can be raised. Such investigations must also consider CRAs’ ‘through the cycle’ rating philosophy, which implies that credit ratings are stable and possibly lag behind option market indicators. 3
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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

This paper investigates the interaction between sovereign rating news and the equity index option market. This market is typically inhabited by institutional informed traders (see Chakravarty et al., 2004; Chen et al., 2005; Jin et al., 2012). Much literature identifies that the derivative markets play a leading role in the price discovery process (e.g. Blanco et al., 2005; Acharya and Johnson, 2007; Avino et al., 2013). Therefore, the dynamics of derivative markets can provide important information regarding the credit quality of underlying entities. In 2011, the turnover of equity index options traded on organised exchanges over the world was US$ 166 trillion (Bank for International Settlements, 2012). The equity index option market is the second largest segment of exchange-traded financial derivative markets, after interest rate derivatives. Given the prominence of both derivative markets and CRAs, interesting questions about the interaction between the index option market and credit rating actions can be raised. Such investigations must also consider CRAs’ ‘through the cycle’ rating philosophy, which implies that credit ratings are stable and possibly lag behind option market indicators. 3
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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

This paper examines the relationship between the volatility implied in option prices and the subsequently realized volatility by using the S&P/ASX 200 index options (XJO) traded on the Australian stock exchange (ASX) during a period of five years. Unlike the stock index options such as the S&P 100 index options in the US market, the S&P/ASX 200 index options are traded infrequently, in low volumes, and with long maturity cycle. This implies that the error-in-variables problem for measurement of implied volatility is more likely to exist. After accounting for this problem by instrumental variable method, it is found that both call and put options implied volatilities are superior to historical volatility in forecasting future realized volatility. Moreover, implied call volatility is nearly an unbiased forecast of future volatility.
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A macro financial analysis of the euro area sovereign bond market  National Bank of Belgium Working Paper No  259, June 2014

A macro financial analysis of the euro area sovereign bond market National Bank of Belgium Working Paper No 259, June 2014

analyzed. It includes (i ) the Chicago Board Options Exchange (CBOE) Market Volatility Index (VIX ), obtained from Datastream, which expresses the implied volatility of the Standard & Poor’s (S&P) 500 stock market index options, as a measure of global …nancial volatility or uncertainty in …nancial markets; (ii) the European Commission’s Economic Sentiment Indicator (ESI ), a forward-looking variable which re‡ects expectations regarding the euro area economic outlook; (iii) the Overnight Indexed Swap (OIS) rates for maturities of 1, 2, 3, 4, and 5 years, from Bloomberg, which re‡ects the evolution of the risk-free interest rate for all euro area countries, and is also used as a reference rate to calculate the spreads of sovereign bonds at the respective maturities; (iv) the spread between the yield on the German government-guaranteed KfW (‘Kreditanstalt fur Wiederaufbau’, a government-owned development bank) bond and the German sovereign bond (from Bloomberg), averaged across maturities, which measures the liquidity premium, and can be interpreted as a common liquidity or ‡ight to safety (F2S ) factor across the euro area bond market 6 (see De Santis (2013)); (v ) the European Economic Policy
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Impact of crude oil volatility on stock returns: Evidence from Southeast Asian markets

Impact of crude oil volatility on stock returns: Evidence from Southeast Asian markets

The oil-stock linkage in new markets could be the guideline for risk management activities when the Southeast Asian stock markets have gained much considerable attention from investors recently. Due to the openness of global trade, the international characteristic of portfolio diversification has been increasing to improve the performance of investments (Steinberg, 2018). The support for international diversification is also discussed by Elton, Gruber, Brown, & Goetzmann (2011), arguing that the investors could obtain the advantage of diversification even if the expected returns of foreign equities are lower than those of domestic stocks. However, the benefit of international diversification is questioned by the research of Hanna (1999) due to the greater integration of financial markets among developed countries examined. Bhargava, Konku, & Malhotra (2004), on the other hand, agree on the strength of diversification but this benefit is declining since the correlation between markets is increasing. Therefore, the new markets, especially emerging and frontier economies, have become the attractive investment opportunities for diversification. A recent study on 21 markets of Yarovaya, Brzeszczyński, & Lau (2016) demonstrates that the Asian markets generally could provide better possibilities for internationally diversifying the portfolio. Thus, it is vital to further explore the movements of Southeast Asian stock markets and their interactions to the volatility on other global indices.
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Similarities and Differences of the Volatility Smiles of Euro- Bund and 10-year T-Note Futures Options

Similarities and Differences of the Volatility Smiles of Euro- Bund and 10-year T-Note Futures Options

The futures option valuation model with futures-style margining is basically a variation of Black’s model. The main difference between the standard Black model and the futures options model with futures-style margining is that there is no discount factor in the later. Lieu’s results are subject to the criticism that expected cash flows are discounted by fixed interest rate, whereas security futures prices are assumed to be stochastic. However, Chen and Scott (1993) show that the results in Lieu hold in a general equilibrium model with stochastic interest rates. They argue that futures options with futures-style margining should not be exercised early because their prices are always greater than the intrinsic value. Thus, American style options on futures with futures style margining will have the same prices as comparable European style options on futures. As a result, we can simply price American style options on futures with futures style margining with a European pricing model. American Euro-Bund futures options have a futures-style option margining, so it is appropriate to use the interest rate futures option valuation model with futures-style margining to derive implied volatilities.
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CURRICULUM VITAE. Model-free implied volatility (estimating model-free implied volatility in the options market, etc.);

CURRICULUM VITAE. Model-free implied volatility (estimating model-free implied volatility in the options market, etc.);

2007 – Conference on Financial Econometrics, Montreal, CA. 2007 – American Economics Association Meetings, Chicago 2006 – Bank of Canada Fixed Income Conference, Ottawa, CA. 2006 – Conference on Realized Volatility, Montreal, CA. 2005 – World Econometrics Congress, London, UK. 2005 – Federal Reserve Board, Washington D.C.

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Inflation volatility, financial institutions and sovereign debt rating

Inflation volatility, financial institutions and sovereign debt rating

Since the determinants of sovereign debt rating tend to be similar to those of the spreads, being that both are measures of risk, the literature on the spreads is also relevant. For instance Min (1998) analyzes the determinants of yield spread of US dollar-denominated fixed income securities using panel least squares methodology on 11 countries over the period 1991–1995. The results emphasize the importance of macroeconomic fundamentals, including inflation − if a country were to gain access to the international bond market. Similarly, Eichengreen and Mody (1998) and Kamin and Kleist (1999) stress the importance of “market sentiment,” in addition to country-specific fundamentals and external factors, to explain variations in sovereign spreads in emerging markets.
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Sovereign country rating, growth volatility and financial crisis

Sovereign country rating, growth volatility and financial crisis

The variables in equation (4.1) are same as before and some of the variables in equation (4.2) are new and they have been as determinants of sovereign credit ratings. MONEY is monetary policy stand, INF is rate of inflations, COMPRISK is composite risk as measured in ICRG, LGDP stands for log of GDP, GROWTH is for per capita GDP growth, and VINF is volatility of inflation. Equations (4.1) and (4.2) are jointly estimated as a system using the three stage least square estimator and the results are presented in Table 4. Column (1) presents the estimated coefficients of the volatility equation. It can be seen that the signs of the estimated coefficients and their significance are very similar to those in Table 3. In particular, the estimated coefficient of RATING and DRATIING are negative and significant as they have been before conforming the notion that credit rating contributes to lower growth volatility. What is also seen from column is that the estimated coefficient on the interaction term (GFC*DRATING) is negative and significant. The joint test on the coefficients of DRATING clearly rejects the null. This leads to the conclusion that whilst the direct effect of GFC on growth volatility has been insignificant, the indirect of GFC has been its contribution towards it by weakening the volatility reducing effect of credit rating. Among the control variable, oil prices have consistently contributed towards increased volatility of output by having a positive and significant coefficients in all specifications.
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The Information Content of Indian Implied Volatility Index

The Information Content of Indian Implied Volatility Index

between changes in implied volatility index and market returns has been documented in the literature for the various financial markets. These results give foundation to the interpretation of implied volatility index as a measure of capturing market sentiments and risks. The arrival of news to the market, a sudden increase in the trading volume and the number of orders crossed may produce a negative relationship between volatility changes and index returns. In particular, an increased level of uncertainty in markets, due to the release of some economic data figures or some political announcements or policy changes that increase risk, may cause an upward surge in financial market volatility. At the same time, these changes induce pressure on the selling decisions of the investors; that may lead to generate negative returns. Whaley (2000), Simon (2003) and Giot (2005) found a negative contemporaneous relationship between volatility changes and index returns in American markets. They found that arrival of bad news may induce a larger volatility increase than the arrival of good news of same relevance. Therefore, if this asymmetric negative relationship between volatility changes and index returns is confirmed, the information in volatility index can become an important element in portfolio management.
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Implied volatility of foreign exchange options: is it worth tracking?

Implied volatility of foreign exchange options: is it worth tracking?

As both market and central bank analyses usually refer to the Black-Scholes implied volatility directly, it is the relevant indicator to study, rather than a more complicated, more realistic but less widely used one. Furthermore, the use of more complex models assuming stochastic volatility requires the estimation of additional parameters, which may introduce fur- ther uncertainties and measurement errors. Moreover, the literature suggests that the model choice does not affect the results regarding the predictive power of implied volatility. In the case of foreign exchange options, Neely (2002) com- pares implied volatilities calculated by three option pricing models – Heston (1993), Barone-Adesi and Whaley (1987) and Black (1976) – and reveals that they produce highly similar descriptive statistics.
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The Volatility Structure Implied by Options on the SPI Futures Contract

The Volatility Structure Implied by Options on the SPI Futures Contract

Following Heynen, Kemna and Vorst (1994), explaining the implied volatility structure may lead to the conclusion that option prices are better described by an alternative underlying asset price process. Taylor and Xu (1993) study the smile effect of implied volatilities and show that the existence of stochastic volatility is a sufficient reason for smiles to exist. They show that an approximation to the theoretical implied volatility is a quadratic function of ln(F/X) where F is the forward price and X is the strike price, and that this approximate function has a minimum when X = F. This theoretical result requires that asset price and volatility differentials are uncorrelated and that volatility risk is not priced. Using currency option data obtained from the Philadelphia Stock Exchange, over the period from 1984 to 1992, and regressing a function of theoretical and observed implied volatilities on moneyness, they find little evidence of asymmetry in implied volatilities. However, the empirical smile pattern is about twice the size predicted by the theory.
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Implied Volatility of Leveraged ETF Options: Consistency and Scaling

Implied Volatility of Leveraged ETF Options: Consistency and Scaling

◮ IV skew is downward sloping for long ETFs (e.g. SPY, SSO, UPRO), ◮ IV is skew is upward sloping for short ETFs (e.g. SDS, SPXU).. Intuitively,.[r]

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The Greek Implied Volatility Index: Construction and Properties

The Greek Implied Volatility Index: Construction and Properties

The study of the construction and of the properties of implied volatility indices has been primarily motivated by the increasing need to create derivatives on volatility (volatility derivatives, see Brenner and Galai, 1989, 1993). These are instruments whose payoffs depend explicitly on some measure of volatility. Hence, they are the natural candidates for speculating and hedging against changes in volatility (volatility risk). Volatility risk has played a major role in several financial disasters in the past 25 years (e.g., Barings Bank, Long-Term-Capital Management). Many traders also profit from the fluctuations in volatility (see Carr and Madan, 1998, for a review on the volatility trading techniques); Guo (2000), and Poon and Pope (2000) find that profitable volatility trades can be developed in the currency and index option markets, respectively. In March 2004, the Chicago Board Options Exchange (CBOE) introduced volatility futures, and it announced the imminent introduction of volatility options.
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Eurozone sovereign bonds and rating assessments: impact on volatility

Eurozone sovereign bonds and rating assessments: impact on volatility

More recently, a working paper published by the ECB analyse the impact of sovereign bonds assessments performed by rating agencies in the context of the eurozone sovereign crisis. De Santis (2012) deals with the impact of rating events on the eurozone countries that presented more deteriorated public finances (so-called PIIGS 5 ) in the period 2008-2011. He found that country-specific credit rating is one of the factors that can explain the developments in sovereign spreads mainly for those countries presenting more deteriorated economic fundamentals, although they are also influenced by the existence of spillovers from other eurozone countries (i.e. Greece). He also finds evidence that spreads for Austria, Finland and the Netherlands depend on the higher demand on German bonds during the crisis (flight to quality) and not on rating events or lack of liquidity.
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Option implied volatility measures and stock return predictability

Option implied volatility measures and stock return predictability

In order to see whether option-implied volatility measures can predict stock returns after controlling for known firm-specific effects, we also include several firm-level control variables. To control for the size effect documented by Banz (1981), we use the natural logarithm of a company’s market capitalization (in thousands of USD) on the last trading day of each month. Following Fama and French (1992), we use the book-to-market ratio as another firm-level control variable. Jegadeesh and Titman (1993) document the existence of a momentum effect (i.e., past winners, on average, outperform past losers in short future periods). We use past one-month returns to capture the momentum effect. Stock trading volumes are included as another variable (measured in hundred millions of shares traded in the previous month). The market beta reflects the historical systematic risk and is calculated by using daily returns available in the previous month using the standard CAPM
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Forecasting global stock market implied volatility indices

Forecasting global stock market implied volatility indices

We use daily data from the 1 st of February, 2001 up to the 9 th of July, 2013 (i.e. 3132 trading days) from eight implied volatility indices. The implied volatilities are the following: VIX (S&P500 Volatility Index – US), VXN (Nasdaq-100 Volatility Index – US), VXD (Dow Jones Volatility Index – US), VSTOXX (Euro Stoxx 50 Volatility Index – Europe), VFTSE (FTSE 100 Volatility Index – UK), VDAX (DAX 30 Volatility Index – Germany), VCAC (CAC 40 Volatility Index – France) and VXJ (Japanese Volatility Index - Japan). The stock markets under consideration represent six out of the ten most important stock markets internationally, in terms of capitalization. In addition, these markets are among the most liquid markets of the world. Thus, we maintain that their implied volatility indices are representative of the world’s stock m arket uncertainty. The data were extracted from Datastream ® . As we aim for a common sample of the aforementioned implied volatility indices, the starting data of the sample period were dictated by the availability of the data of the VXN index.
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Active Trading in the Stock Market Using Implied Volatility

Active Trading in the Stock Market Using Implied Volatility

As Christine Lagarde perfectly coined it, “Markets love volatility.” Until automation is perfected and capable of making sound investment decisions, the free market is dominated by humans and their intuition for a financial advantage. Emotions and common sentiments across a population play a critical role in the market movement. Analyzing the common thinking or sentiment of a population towards a certain trend or idea would be the logical concept to look out for in a market where buying and selling, determines the outcome. In this paper, we will be largely focusing on the role of human emotions namely fear, which is the primary unit for emotion in the market. A lack of fear indicates a strong confidence in a position, on the contrary, an abundance of fear results in instability in a position. We can use levels of fear to gauge how investors think, make decisions, and react to events in the economy. In our study, we will be using the Chicago Board Options Exchange Volatility Index (VIX) which is termed the “investor fear gauge,” to determine and gauge future market, sector, stock, and equity performance. And how these common practices can be applied to predict trends, automate trends, and hopefully educate the public on the use of volatility as a trading strategy. In the next couple of pages, we will be introducing financial and business terms which are necessary to understand further technical details and strategies.
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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 *

This study develops a new KOSPI200 implied volatility index and examines its infor- mational content and nonlinear dynamics. The construction of this new benchmark for volatility expectations follows the methodology for calculating the new VIX index from S&P500 options. The empirical evidence suggests that the expected level of volatility in the Korean stock market has been steadily falling since the inception of option trading and the onset of the Asian financial crisis. Implied volatility is found to reflect useful in- formation on future volatility that is not contained in the history of returns, even after allowing for leverage effects. Markov regime-switching models suggest that nonlinearities in volatility expectations are not likely to be driven solely by the asymmetric impact of news but also by regime-dependencies in the realignment mechanism adjusting for fore- cast errors. The adjustment process is likely to be significant during regimes of lower volatility expectations but financial crises seem to elevate the level of anticipated volatil- ity and impair its adaptive dynamics.
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Determinants of the implied volatility function on the Italian Stock Market

Determinants of the implied volatility function on the Italian Stock Market

Financial literature handled this empirical evidence of not constant implied volatility with two broad classes of methods. The first could be labelled “deterministic volatility methods”; in general it refers to the use of a pricing model in which the parameter of constant volatility is replaced by a deterministic volatility function: different examples of this type of models are the approach of Shimko (1993), the implied binomial tree or lattice approach developed by Derman and Kani (1994) and Rubinstein (1994), the non-parametric kernel regression approach of Ait- Sahalia and Lo (1998). The second class of methods could be labelled “two factors models”; besides the risk of the market price of underlying asset, the valuation models price additional non-traded sources of risk, such as the volatility of volatility or market price jumps or even both. One of the first examples belonging to this general class was the stochastic volatility model of Hull and White (1987); more recent advances are, among others, the stochastic volatility model of Heston (1993), the random jump model of Bates (1996) and the multifactor model of Bates (2000).
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