Literature Review on Volatility

Literature Review on Volatility Literature Review What is Volatility? Volatility is defined as the spread of all likely outcomes of an uncertain variable (Poon, 2005). Statistically, it is often measured as the sample standard deviation (as seen below), but can also be measured by variance. Where rt = return on day t, and ÃŽÂ ¼ = average return over the T-day period. The common misconception is to equate volatility to risk. However, whilst volatility is related to risk, it is not the same. Risk represents an undesirable outcome, whilst volatility is a measure for uncertainty that could arise from a positive outcome. Furthermore, volatility as a measure for the spread of a distribution contains no information on the shape, this represents another reason for volatility being an imperfect measure for risk. The sole exception to this being a normal distribution or lognormal distribution where mean and standard deviation are appropriate statistics for the whole distribution (Poon, 2005). In dealing with volatility as a subject matter in financial markets, the focus is on the spread of asset returns. High volatility is generally undesirable as it indicates security values are unreliable and capital markets arent functioning efficiently (Poon, 2005; Figlewski, 1997). Financial market volatility has been the subject of much research and the number of studies continues to rise since Poon and Granger (2003)s original survey first identified 93 papers in the field. A whole host of drivers for volatility have been explored (including political events, macroeconomic factors and investors behavior) in an attempt to better capture volatility and decrease risk (Poon, 2005). This study will add to that list, hoping to contribute something novel to the field by scrutinizing the appropriateness of different volatility models for different country indexes. The Importance of Volatility Forecasting Investment strategies, Portfolio Optimization and Asset Valuation Volatility when taken as uncertainty transforms into an important component in a wide range of financial applications including Investment strategies for trading or hedging, Portfolio optimization and Asset price valuation. The Markowitz mean-variance portfolio theory (Markowitz, 1952), Capital Asset Pricing Model (Sharpe, 1964) and Sharpe ratio (Sharpe 1966) signify three cornerstones for optimal decision-making and measurement of performance, advocating a focus on the risk-return interrelationship with volatility taken as a risk proxy. With Investors and portfolio managers having limits as to the risk they can bear, accurate forecasts of the volatility of asset prices for long-term horizons is necessary to reliably assess investment risk. Such forecasts allow investors to be better informed and hold stocks for longer rather than constantly reallocating their portfolio in reaction to movements in prices; an often expensive exercise in general (Poon and Granger, 2003). In terms of st ock price valuation French, et al. (1987) analyse NYSE common stocks for the period of 1928-1984 and find expected market risk premium to be positively related to the predictable volatility of stock returns, which is further strengthened by the indirect relationship between stock market returns and the unexpected change in the volatility of stock returns. Derivatives pricing Volatility is a key element in Modern option pricing theory that enables estimation of the fair value of options and other derivative instruments. According to Poon and Granger (2003) the trading volume of derivative securities had quadrupled in the recent years leading up to their research and since then this growth has accelerated with the global derivatives market now estimated to be around $544 Trillion excluding credit default swaps and commodity contracts (BIS, 2017). As one of five input variables (including Stock Price, Strike price, time to maturity and risk-free interest rate), expected volatility over the options life in the Black-Scholes model theorized by Black and Scholes (1973) is crucially also the only variable that is not directly observable and must be forecast (Figlewski, 1997). Implied volatility and realized volatility can be computed by referencing observed market prices for options and historical data. Whilst the former is attractive for requiring little input data and delivering excellent results when analysed in some empirical studies compared to time series models utilizing just historical information, it is deficient by not having a firm statistical basis and different strike prices yielding different implied volatilities creating confusion over which implied volatility to use (Tse, 1991; Poon, 2005). Lengthening maturities of derivative instruments also weakens the assumption that volatility realized in the recent past can be used as a fairly reliable proxy for volatility in the near future. (Figlewski, 1997). With recent developments, derivatives written on volatility can now also be purchased whereby volatility represents the underlying asset, thus further necessitating volatility forecasting practices (Poon and Granger, 2003). Financial Risk Management Volatility forecasting plays a significant role in Financial Risk Management of the finance and banking industries. The practice aids in estimation of value-at-risk (VaR), a measure introduced by the Basel Committee in 1996 through an amendment to the Basel Accords (an international standard for minimum capital requirement among international banks to safeguard against various risks). Whilst many risks are examined within, volatility forecasting is most relevant for Market risk and VaR. However, calculating VaR is necessary only if banks choose to adopt its own internal proprietary model for calculating market risk related capital requirement. By choosing to do so, there is greater flexibility for banks in specifying model parameters but with an attached condition of regular backtesting of the internal model. Apart from banks, other financial institutions may also use VaR voluntarily for internal risk management purposes. (Poon and Granger 2003; Poon 2005) Christoffersen and Diebold (2000) do however contend the limits of relevance of Volatility Forecasting for Financial Risk Management, arguing that for reliable forecastablity much depends on whether the horizon of interest is of a short term or long-term nature (taken to be more than 10 or 20 days) with the practice deemed more relevant for the former than the latter due to the limitations in forecastability. Policymaking Financial market volatility can have wide-reaching consequences on economies. As an example, large recessions create ambiguity and hinder public confidence. To counter such negative impacts and disruptions, policy makers utilize market estimates of volatility as a means for identifying the vulnerability of financial markets, equipping them with more reliable and complete information with which to respond with appropriate policies. (Poon and Granger, 2003) The Federal Reserve of the United States is one such entity that incorporates volatility of various financial instruments into its monetary policy decision-making (Nasar, 1991). Bernanke and Gertler (2000) explore the degree to which implications of asset price volatility impact monetary policy decision-making. A side-by-side comparison of U.S. and Japanese monetary policy is the basis of the study. The researchers find that inflation-targeting is desirable, however, monetary policy decisions based on changes in asset prices should only be made to the extent that such changes help to forecast inflationary or deflationary pressures. Meanwhile, Bomfim (2003) investigates the relationship between monetary policy and stock market volatility from the other perspective. Interest rate policy decisions that carry an element of surprise appear to increase short run, stock market volatility significantly with positive surprises also having a greater effect than negative surprises. Empirical stylized facts of asset returns and volatility Any attempt to model volatility appropriately must be done with an understanding of the common, recurring set of properties identified from numerous empirical studies carried out across financial instruments, markets and time periods. Contrary to the event-based theory in which it is hypothesized different assets respond differently to different economic and political events, empirical studies show that different assets do in fact share some generalizable, qualitative statistical properties. Volatility models should thus seek to capture these features of asset returns and volatility so as to enhance the forecasting process; herein lays the challenge. (Cont, 2001; Bollerslev et al 1994) Presented are some of these stylized facts, along with their corresponding empirical studies that have contributed to the evolving literature aimed at improving volatility-forecasting practices and which this study will also look to capture. Return Distributions Stock Market returns are not normally distributed and it is therefore an unsuitable distribution for modeling returns according to Mandelbrot (1963) and Fama (1965). Returns are approximately symmetrical but can display negative skewness and significantly have leptokurtic features (excess kurtosis with heavier tails and taller, narrower peaks than found in a normal distribution) that see large moves occur with greater frequency than under normal distributions (Sinclair, 2013). Cont (2001) asserts that these large moves in the form of gains and losses are asymmetric by nature with the scale of downward movements in stock index values dwarfing upward movements. He further argues that the introduction of GARCH-type models to counter the effects of volatility clustering can reduce the heaviness of tails in the residual time series to some small extent. However, as GARCH models can at times struggle to fully incorporate heavy-tail features of returns, this has necessitated the use of alte rnative distributions such as the students t-distribution employed in Bollerslev (1987). Alberg et al (2008) employ a skewed version of this distribution to various models with the EGARCH model delivering the best performance in forecasting the volatility of Tel Aviv stock indices. Cont (2001) does however also highlight an important consideration with the notion of aggregational gaussianity that as one increases time scale (t) for calculation of returns, the distribution of returns seems more normally distributed in appearance. Leverage effect/Asymmetric volatility In most markets, volatility and returns are negatively correlated (Cont, 2001). First elucidated by Black (1976) and particularly prevalent for stock indices, Volatility will tend to increase when stock price declines. The justification for this is because a decline in equity stock price will increase a companys debt-to-equity ratio and consequently its risk and volatility (Figlewski and Wang, 2000; Engle and Patton, 2001). Importantly, this relationship is asymmetric, with negative returns having a more marked effect on volatility than positive returns as documented by Christie (1982) and Schwert, (1989). However they also argue that the leverage effect is not enough on its own to explain all of the change in volatility with Christie (1982) incorporating interest rate as another element that has a partial effect. Hence, whilst, ARCH (Engle, 1982) and GARCH (Bollerslev, 1986) models do well to account for volatility clustering and leptokurtosis, their symmetric distribution fails to account for the leverage effect. In response to this, various asymmetric modifications of GARCH have been developed, the most significant of these being Exponential GARCH (EGARCH; Nelson, 1991) and GJR (Glosten et al, 1993). Other models like GARCH-in-Mean have also endeavored to capture the leverage effect along with the risk premium effect, another concept that has been theorized to contribute to volatility asymmetry by studies such as Schwert (1989) (Engle and Patton, 2001). Volatility Distribution The distribution of volatility is taken to be approximately log-normal. Various studies such as Andersen et al (2001) have postulated this. More significantly than the actual distribution is the high positive skewness indicating volatility spends longer in lower states than higher states. (Sinclair, 2013) Volatility-Volume correlation All measures of volatility and trading volume are highly positively correlated (Cont, 2001). Lee and Rui (2002) show this relationship to be foundationally robust, however what is more complex is determining the causality between the two. Strong arguments can be made either way. As an example, Brooks (1998) utilizes linear and non-linear Granger causality tests and finds the relationship to be stronger from volatility to volume than the converse. He concludes by highlighting that for forecasting accuracy, predicting volume using volatility is more productive than forecasting stock index volume and using such forecasts in trading. According to Gallant et al (1992) this relationship is also closely linked with the leverage effect and incorporating lagged volume weakens the effect considerably. Non-Constant Volatility Volatility is not constant. The changing nature of volatility occurs in a particular manner; Merton (1980) was critical of researchers who failed to incorporate this feature in their models. Firstly volatility is mean reverting. Indeed LeBaron (1992) found a strong negative relationship between volatility and autocorrelation for stock indices in the United States. Secondly, Volatility clusters. This is a phenomenon first noted by Mandelbrot (1963) that allows a good estimation of future volatility based on current volatility. Other studies such as Chou (1988) have also empirically shown the existence of clustering. Mandelbrot (1963) wrote, large changes tend to be followed by large changes of either sign, and small changes tend to be followed by small changes. In other words, a turbulent day of trading usually comes after another turbulent trading day, whilst a calm period will usually be followed by another calm period. Importantly, the phenomenon is not exclusive to the underlying product and can be seen in stock indices, commodities and currencies. It also tends to be more pronounced in developed than emerging markets. (Taylor, 2008; Sinclair, 2013) Engle and Patton (2001) argue that volatility clustering indicates volatility goes through phases whereby periods of high volatility eventually give way to more normal volatility with the contrary also holding. Engles (1982) landmark paper incorporated these features of volatility persistence using his ARCH model, whereby time varying, non-constant volatility that persists in high or low states is taken account of.

Emerging Markets; Risks and Challenges

Trade among these countries has also grown by a staggering amount in recent years and their multinational companies are now competing with those from the developed economies. There remain, however, significant risks and challenges to investing in these countries. They are discussed here under these broad headings; Political, Economic, Legal and Socio cultural. They affect the different countries in deferent ways and sometimes Interact in deferent ways to produce deferent results. For example, political processes more often than not drive economic, legal and social policies of governments.China and India, two of the largest emerging markets operate very different political processes and therefore have two very different sets of political institutions. Chinese communism and Indian democracy vary significantly, and their political systems ultimately affect the choice of economic, legal and social policies. The first step to emerging market status for most of these countries can be trace d to political reforms and/or movements, examples being the transition from authoritarian to democratic governments and economic liberation's.It can also be argued that social reforms and/or popular movements brought about the downfall of the authoritarian governments in the first place, allowing for reforms in the political and economic systems In place (the political economy), thus paving the way for economic gains witnessed today. Despite the often complex interactions between these factors, wave attempted to simplify them by grouping them In broad categories. Emerging markets also face challenges as they come to grips with economic prosperity and their new status in the global community.

Salah: Spiritual Nourishment of the Soul

Salat: Salah Is ordained on Muslims five times a day and Is essentially the spiritual nourishment of the soul of the believer as well as the divine connection between Allah and the believer. There are 5 prayers throughout the day: Fajr (predawn), Dhuhr (afternoon), Asr (post-afternoon), Maghrib (evening), Isha (night). Salat In Qur'an: 1 . Establishing Salah Develops Taqwa (Fear and Awareness of Allah) : â€Å"This Is the Book In which there is no doubt, a guidance for those who have taqwa; who believe In the unseen, and who establish Salah, and spend out of what we have provided for them† (2: 2-3) 2.Salah Is the Sign ofa Believer : â€Å"The believers, men and women, are protecting friends of one another; they enJoln good and forbid evil, and they establish Salah, and give Zakah, and obey Allah and His Messenger. Allah will have His Mercy on them, and surely, Allah is All-Mighty, All- Wise. † (9: 71) 3. Establishing Salah Leads to Allah's Eternal Blessings : â€Å"So whatever you have been given is but (a passing) enjoyment for this worldly life, but that which Is with Allah is better and more lasting for those who believe and put their trust In their Lord. And those who avoid the great sins and lewdness, and when they are angry. hey forgive.And those who answer the Call of their Lord, and perform the Salah, and who conduct their affairs by mutual consultation, and who spend of what We have bestowed on them. † (42:36-38) 4. Those Who Pray Shall Have Nothing to Fear on the Day of Judgment : â€Å"Truly, those who believe and do righteous deeds, and perform Salah, and give Zakah, they will have their reward with their Lord. on them shall be no fear, nor shall they grieve. † (2:277) 5. Remain in Allah's remembrance after prayer : â€Å"When have finished performing the Salah, remember Allah standing, sitting, and eclining, but when you are free from danger, perform the Salah.Surely, Salah Is en]olned on the believers at fixed times. à ¢â‚¬  (4:103) 6. Command to Pray with Congregation : â€Å"And establish Salah and give Zakah, and bow down (in worship) along with those who bow down (in worship)† (2:43) 7. Special Command Regarding Punctuality of Prayer : â€Å"Guara strlctly tne salan, especlally obedience. † (2:238) 8. Allah's Help Comes Through Salah : e Saa . Ana stand DeTore Allan w â€Å"Seek help through patience and Salah; truly it is extremely difficult except for the humble true believers. † (2:45) â€Å"Oh you who believe! Seek help through patience and Salah. Truly, Allah is with those who are patient. (2:153) 9. Special Emphasis on Friday Prayer . â€Å"Oh you who believe! When the call is made for the Salah on Friday, come to the remembrance of Allah, and leave off business. That is better for you, if you only knew! And when the Salah has ended, you may disperse through the land, and seek the Bounty of Allah, and remember Allah much so that you may be successful. † (62: 09-10) 10. Prayer Protects Against Evils : â€Å"Recite that which has been revealed to you of the Book, and perform Salah. Verily, Salah prevents from lewdness and evils. And indeed, the remembrance of Allah (by you) is greatest.And Allah knows what you do. † (29:45) Necessary WaJib Acts of Salat : 1 . Starting the prayer with the Takbir, Allah-u Akbar; 2. Reciting the Fatiha completely; 3. Reciting a Surah (chapter) of the Qur'an after the Fatiha in the first two rakats of any obligatory prayer and in all the rakats of the Odd-Numbered Prayer (Salat al-Witr) and again in all the rakats of any optional (nafllah) prayer; 4. Reciting the Fatiha before the additional Surah (chapter); 5. When prostrating, placing the forehead and the nose on the ground together; 6. Performing the two prostrations (saJdas) successively; 7.Paying attention to â€Å"tadil arkan†(i. e. to perform all pillars of the prayer with ease, not shortening them; 8. In the three-rakat or four-rakat pra yers, sitting at the completion of the second rakat; 9. Reciting at-Tahiyyatu at the end of the second rakat and when one sits before one has made salaam (salutation); 10. When performing any of the obligatory three-rakat and four-rakat prayers, the Odd- Numbered Prayer (Salat al-Witr) and the first sunnah part of the Noon Prayer (Salat az Zuhr), standing up for the third rakat as soon as one has recited the at-Tahiyyatu t the and of the second rakat; 11.When performed in congregation, the prayer leader (imam) reciting aloud the Fatiha and the additional surahs of the Qur'an in the two obligatory rakats of the Morning Prayer (Salat al-FaJr), in the first two rakats of the obligatory rakats of the Sunset Prayer (Salat al-Maghrib) and the Late Evening Prayer (Salat al'lsha), in the Friday Assembly Prayer (Salat al-Jum'a), and in both the Festival Prayers (Ela Prayers); 12 Agaln, In tne larawln Prayer ana tne oaa-NumDerea Prayer following it in the month of Ramadan, the imam's reciting aloud the Fatiha and the additional surahs; 13.In the obligatory sections of the Noon Prayer (Salat az-Zuhr) and the Late Afternoon Prayer (Salat al-Asr), the imam's reciting silently the Fatiha and the additional surahs of the Noble Qur'an; 14. While following the Imam, not reciting anything but remaining silent; 15. Reciting the Qunut Supplications in the Odd-Numbered Prayer (Salat al-Witr) 16. Performing the additional Takbirs in the Eid Prayers; 17. Giving salaam at the end of the prayer; 18. Making saJdah sahw (prostration for forgetfulness) at the end of a prayer if a mistake has been made in it; 19. Making a prostration after finishing an ayat where a aJdah is called for.Necessary Farz Acts of Salat : 1 . At-Tahrimah (the first Allahu Akbar 2. Qiyam (standing) 3. Qira'ah (recitation of Quran) ever if it be an Ayah. 4. Ruku 5. SJdah 6. The final sitting for the duration of Tashshahud Importance And Significance of Salat : Those who are steadfast in seeking the face of their L ord, and establish salat and give from the provision We have given them, secretly and openly, and stave off evil with good, it is they who will have the ultimate Abode. (Surat ar-Ra'd,22) The salat is a religious observance that believers are commanded to perform throughout the ourse of their lives, the times of which have been stipulated.

Plant Bugs, Family Miridae

As their name suggests, most plant bugs feed on plants. Spend a few minutes examining any plant in your garden, and theres a good chance youll find a plant bug on it. The family Miridae is the largest family in the entire order Hemiptera. Description In a group as large as the family Miridae, there is a lot of variation. Plant bugs range in size from a tiny 1.5 mm to a respectable 15 mm long, for example. Most measure within the 4-10 mm range. They vary quite a bit in color, too, with some sporting dull camouflage and others wearing bright aposematic shades. Still, as members of the same family, plant bugs share some common morphological traits: four-segmented antennae, four-segmented labium, three-segmented tarsi (in most species), and a lack of ocelli. The wings are a key defining characteristic of the Miridae. Not all plant bugs have fully formed wings as adults, but those that do have two pairs of wings that lie flat across the back and overlap at rest. Plant bugs have a wedge-shaped section (called the cuneus) at the end of the thick, leathery part of the forewings. Classification Kingdom – AnimaliaPhylum – ArthropodaClass – InsectaOrder – HemipteraFamily – Miridae Diet The majority of plant bugs feed on plants. Some  species specialize on eating a particular kind of plant, while others feed generally on a variety of host plants. Plant bugs tend to prefer eating the nitrogen-rich parts of the host plant – the seeds, pollen, buds, or emerging new leaves – rather than the vascular tissue. Some plant bugs prey on other plant-eating insects, and a few are scavengers. Predaceous plant bugs may specialize on a certain insect (a particular scale insect, for example). Life Cycle Like all true bugs, plant bugs undergo simple metamorphosis with just three life stages: egg, nymph, and adult. Mirid eggs are often white or cream-colored, and generally long and thin in shape. In most species, the female plant bug inserts the egg into the stem or leaf of the host plant (usually singly but sometimes in small clusters). The plant bug nymph looks similar to the adult, although it lacks functional wings and reproductive structures. Special Adaptations and Defenses Some plant bugs exhibit myrmecomorphy, a resemblance to ants that may help them avoid predation. In these groups, the Mirid has a notably rounded head, well distinguished from the narrow pronotum, and the forewings are constricted at the base to mimic an ants narrow waist. Range and Distribution The family Miridae already numbers well over 10,000 species worldwide, but thousands more may still be undescribed or undiscovered. Nearly 2,000 known species inhabit North America alone. Sources Borror and DeLongs Introduction to the Study of Insects,  7th edition, by Charles A. Triplehorn and Norman F. Johnson.Encyclopedia of Entomology,  2nd edition, edited by John L. Capinera.Biology of the Plant Bugs (Hemiptera: Miridae):  Pests, Predators, Opportunists, by Alfred G. Wheeler and Sir Richard E. Southwood.Family Miridae, Plant Bugs, Bugguide.net, accessed December 2, 2013.