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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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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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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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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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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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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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.

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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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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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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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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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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◮ 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 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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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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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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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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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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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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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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