Volatility Models Essay

2074 words - 9 pages

Why is important the volatility?
Volatility is indispensable, and is central for pricing any asset within the financial market, from a single stock to the most complicated derivative. It is quite important when managing portfolios or in computing the risk or the corresponding hedging strategy, but the issue is that is not observable and it is heteroskedastic, it fluctuates through time, you should not assume a homoscedastic pattern, constant over time, because it will be a huge mistake when estimating. For that, enormous literature has put huge effort in trying to predict future volatility as accurately as possible, thus large number of sophisticated models has been created since the ...view middle of the document...

Therefore, since the seminal paper of Bollerslev, many authors have tried to refine the base GARCH, accommodating for leverage effect among other improvements.
On the other hand, the Stochastic Volatility literature is one of the most well known statistical approaches for estimating future volatility.
Supplement approaches are those than connect specific information of the underlying, but the final estimation is only valid under that specifics assumptions. We could think about the Black & Scholes method for getting the price of an option, of the VaR model that requires the estimation of the volatility.
The papers that are presented below suggest that, in general, the GARCH model performs reasonable well in forecasting. Although there are some studies that find that the GARCH predictions are not so well, many authors have found later on that this bad performance was due to the bad specification of the ‘real’ volatility and not to the GARCH estimation per se, because the conditional variance of a financial series is an unobservable process, so there are some possible interpretations of what are you going to substitute in order to do the forecasting exercise. The literature presented in this paper also notices the differences when using different proxies for the conditional volatility.
This project is organized as follows; sections 2 to 5 are a simple summary of the key references that I have received. Section 6 includes some personal comments about the overall literature studied and finally, Section 7 is a briefly conclusion of the topic.

2. A Forecast Comparison Of Volatility Models: Does Anything Beat A GARCH (1,1)?

In this paper, they wanted to know if some sophisticated volatility models work much better than the naive GARCH (1,1) when trying to capture the conditional variance, therefore they propose 330 GARCH-type models in order make a ranking of which ones perform better.
The study was done with two different financial series; an exchange rate, the Deutsche Mark against the US dollar, and the IBM stock, both of them in daily frequency.
As they wanted to know the ability for each model in predicting the future conditional variance, the overall sample is divided into estimation or in-sample period and an evaluation or out-of-sample period. For the exchange rate, the estimation stage covers from October 1987 to September 1992, and the evaluation stage from October 1992 to September 1993. Then, for the IBM analysis, the in-sample phase is from January 1990 to May 1999 and the out-of-sample phase, from June 1999 to May 2000. They assess each model in terms on loss function, as they do not know surely which one is more proper, they suggest six different functions, just in order to observe the power of the models in forecasting future volatility.
Hansen and Lunde, in line with Andersen and Bollerslev, assess the conditional variance by the realized variance and not by the squared return. They do so, because the realized variance or...

Find Another Essay On Volatility models

Real Estate Volatility Tests Essay

986 words - 4 pages Real estate volatility tests There has been research carried out on the risk and returns of real estate investments trusts (REIT). This study was done by Najand and Fitzgerald (2006, p.174) who studied the volatility and return for REITs between 1995 and 2003 with daily data where they found that they had an average beta of 0.24 and an abnormal return 2.25%. A study done by Chaudhry, Myer and Webb (1999, p.342) on real estate stocks, in the

A Unique Expert System for Optimum Oil Price Estimation by Integration of Fuzzy Cognitive Map, Neural Networks and GA

1145 words - 5 pages several different univariate and multivariate statistical models such as TGARCH and GARCH to forecast daily volatility in petroleum future price returns. Kulkarni and Haidar (2009) used a model based on multilayer feed forward neural network to forecast crude oil spot price direction in the short-term. Several data preprocessing methods were tested by them. Manera et al. (2007) compared the performance of several static and dynamic forecasting

managerial risk and incentive

840 words - 4 pages 1. Problem statement The authors’ main focus is the sensitivity of CEO wealth to stock return volatility (vega). They find that the higher vega leads to riskier policies made by managers. Examples of risky policies are investment in research and development and high leverage. It indicates that vega is an incentive for executives to invest in riskier asset and more aggressive debt policy. Therefore, firms with high growth opportunities may want

Freight Rate Management and Shareholder Value

2292 words - 9 pages associated with certain goods and services influence the rate in which companies charge for the transportation of goods from one region to another. To avoid the risks associated with fluctuating freight rates and volatility as witnessed over the last couple of years, companies must ensure that they consider hedging. Hedging helps in controlling the cash inflow and outflow as dictated by both internal and external parameters (Kavussanos & Visvikis 2006

Case study: Language Competition

1752 words - 8 pages . The two languages will coexisted if there is no significant change in random possibilities due to the equivalent in prestige. Figure 2 : the results of the AS-model (left) and MW-model (right) simulation with parameters S = 0.5, a = 3. When volatility is low, agents are lazier and more hesitant to change their current state. Both of the models in Figure. 2 display a clearer and smoother domain comparing to the previous test. The process of

Short-Term Spot Electricity Price Modeling in Competitive Electricity Markets

1650 words - 7 pages bills and notes have a volatility of less than 0.5%, Stock indices have moderate volatility of about 1-1.5%, Commodities such as crude oil and natural gas have volatilities of 1.5-4%, Highly volatile Stocks have volatilities not exceeding 4% and Electricity prices exhibits extreme volatility of up to 50%. In a power market, for both spot markets and long-term contracts, price models and forecasts are necessary input so that power market

Why Is It so Difficult to Forecast Exchange Rate Movements?

1240 words - 5 pages models may not owe the ability of predicting and, inversely, it is the exchange rate that attains the predictability on these fundamentals. Works Cited Engel, Charles 2000, “Long-Run PPP May Not Hold After All”, Journal of International Economics, Vol. 51, no. 2, pp. 243-73. Frankel, J 1996, 'How Well Do Foreign Exchange Markets Work: Might a Tobin Tax Help?', The Tobin tax: Coping with financial volatility, pp. 41-81, n.p.: New York and Oxford

The Cost of Equity Capital and the Capital Asset Pricing Model

1785 words - 7 pages When discussing the cost of equity capital, or the rate of return required by investors for their share expenses, there are three main models widely used for analyzation. These models are the dividend growth model, which operates on the variable of growth and future trends, the capital asset pricing model (CAPM), which operates on the premise that higher returns are a result of higher risk, and the arbitrage pricing theory (APT), which has a

Relationship between CAPM, E/E raitio, and size

872 words - 3 pages market efficiency, Journal of Finance 48, 65-91.Lakonishok, Josef, Andrei Shleifer, and Robert W. Vishny, 1994, Contrarian investment, extrap- olation, and risk, Journal of Finance 49, 1541-1578.Lehmann, Bruce, and David Modest, 1988, Empirical foundations of the arbitrage pricing theory, Journal of Financial Economics 21, 213-254. MacKinlay, A. Craig, 1995, Multifactor models do notWest, Kenneth D., 1988, Bubbles, fads, and stock price volatility tests: A partial evaluation.Journal of Finance 43, 639-655.Wheatley, Simon, 1988a, Some tests of the consumption-based asset pricing model. Journal ofMonetary Economics 22, 193-215., 1988b,

Risk Management in Stock Valuation and Markets

2705 words - 11 pages share to income of the rivals is 20. Now the value of the company share as folloes: Per share valuation: EPS * X- PE ratio = $5 * 20 = $100 (Madura, 2011) Dividend discount model In 1931 John B. Williams developed the dividend discount model. It is one of the first models used for pricing and it is still used today for pricing of stocks. Williams declared that the present value of the stock’s future dividend should reflect in the

Operation Management

2557 words - 10 pages role in keeping production and distribution flowing smoothly.Better supply chain models don't just help manufacturers of physical goods, but also service businesses, including those that require great creativity, imagination, and specialized knowledge.For example, using a virtual reality system and ultrasound data sent through the Internet, a medical specialist in Dallas can give an opinion to a patient in Atlanta...or London...or Bombay. A virtual

Similar Essays

Volatility Trading Essay

1236 words - 5 pages Implied volatility refers to the market expectations of share price volatility implied by observed option prices. Implied volatility is ascertained by reversing option pricing models. Instead of calculating the theoretical option price using the stock price, strike price, expected volatility, time to expiry, interest rate and expected dividend yield, the observed option price becomes an input and the expected volatility the output. The method is

Comparing The Persistency Of Different Frequencies Of Stock Returns Volatility In An Emerging Market: A Case Study Of Pakistan

2718 words - 11 pages Abstract This study aims at comparing the variance structure of high (daily) and low (weekly, monthly) frequencies of data. By employing ARCH (1) and GARCH (1, 1) models, the study finds evidence that the intensity of the shocks are not equal for all the series. The study first finds that statistical properties of the three data series of returns are substantially different from one another and the persistence of conditional volatility is

Apply Garch Time Series Modelling In Risk Management Erm2 Research Paper

1621 words - 7 pages addition, GARCH model also models for ‘black swan’ events. However, one limitation of GARCH is that it cannot model asymmetric volatilities. That is, GARCH ignores the sign of past volatility and negative shocks will have the same effect of positive shocks. However, in reality, the negative shocks often have larger impact than positive ones. This is referred to as leverage effect (Engle and Siriwardane, 2014), and can be modelled by some extension

Scandinavian Essay

1015 words - 5 pages The Nordic Model describes the economic and social models of the 5 Nordic countries; Denmark, Iceland, Norway, Sweden and Finland. The model combines ‘growth and solidarity’ (TNMiNE, 2013) which results in the Nordic countries often presenting themselves at the top of the international ratings in the following areas: ‘equal distribution of income, competitiveness, innovation, employment, equality - gender equality and environmental stewardship