Podcasts about garch

La brasilera Fernanda nos revela una anécdota que vivió con su novio. Míralo en YouTube y seguime en redes: https://linktr.ee/elopodcast

Devilina cuenta el lugar más raro donde tuvo relaciones, además ¿tragará o escupirá? Mirá más en YouTube y seguime en redes: https://linktr.ee/elopodcast

La peruana Vania Puch se declara insaciable a la hora del sexo. Míralo en YouTube y seguime en redes: https://linktr.ee/elopodcast

Jazpincita nos cuenta como fue el día en el cuál estuvo con 5 pibes. Míralo en YouTube y seguime en redes: https://linktr.ee/elopodcast

Gold celebrates 30 years on air today, so Christian and the team take a trip back to 1991See omnystudio.com/listener for privacy information.

Gold celebrates 30 years on air today, so Christian and the team take a trip back to 1991

¡Aire viernes! ¿De qué hablamos si no hablamos del montaje de la pandemia? Los que producen y crean oportunidades vs los oportunistas parasitarios estatales. "Cuando calienta el GARCH" la versión de Andy Perrone y Lorena Bello en su columna "La Bello y la Bestia".

In a previous post, we presented an example of volatility analysis using Close-to-Close historical volatility. In this post, we are going to use the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to forecast volatility. http://tech.harbourfronts.com/trading/forecasting-volatility-garch-model-volatility-analysis-python/

Osvaldo se chocó con un cantante de protesta que protesta en serio y un programa que pone colorado hasta a Liberman.

Garch and Aidan ramble about camping, stupids birds, and more! Patreon: https://www.patreon.com/GMMDT

Enjoy! Join our patreon for exclusive content! https://www.patreon.com/GMMDT

Wie es ist, wenn man strunzbekifft im edelsten Lokal des Landes diniert oder wenn man sich drei Wochen nur von rohem Gemüse ernährt oder wenn man in Zürich eine Wohnung sucht, geschweige denn einen Musikraum, erfahrt ihr in diesem fiftyfifty Pottcast von Knack und Luuk. In der ersten Hälfte wird vor allem bekifft herumgeprustet und 1 Ochsenschwanz gebraten, dafür gibts in der zweiten eine knallharte Abrechnung mit der Regierung, mit den Medien und überhaupt mit der ganzen Gesellschaft. Bambule!

To close out Varch/Garch/Smarch, Kyle, Matthew, Natalie and Laura bring you a special bonus episode which takes you back to their roots. That's right, they're finally releasing two episodes they recorded way back in the very beginning of the podcast (circa 2014) but had to hold back due to Burnside's work NDAs. In the first episode, they talk to comedian/podcaster Jordan Morris (from "Jordan, Jesse, Go!") about fighting games. Streetfighter is discussed. Water is spilled. Then they talk to their friend Aldrin Cornejo in their original Legend of Zelda episode, which serves as a good primer and overview of the series and why it's rad. Also, they make (now outdated) E3 predictions and talk Nintendo, and Natalie plays the ocarina. Two episodes for the price of one? It's a Garch miracle!

本期由Yixue主持，Alfred同学分享了期权定价公式研究的背景以及历史过程，主要介绍了Paul Samuelson先生和Robert Merton先生在权证定价方面开创性的研究，以及Fischer Black先生与Myron Scholes先生关于期权定价公式的研究过程。 CORE TOPICS: 期权定价、费谢尔·布莱克、CAPM、无风险套利 SHOW NOTE： CAPM Paul Samuelson Robert C. Merton Robert K. Merton Black–Scholes model Myron Scholes 期权 权证 The History of Options Trading Put–call parity 期权平价公式 场外交易 无风险套利 证券交易所 芝加哥期权交易所 GARCH 做市商制度 现金流折现 定价未来 费希尔·布莱克与革命性金融思想 注释： 节目中将期权归为一类或近似，但是期权和权证其实是有区别的，见权证与其他金融衍生品的对比 更多内容请访问:www.quant.fm

Many economic and financial time series exhibit time-varying volatility. GARCH models are tools for forecasting and analyzing the dynamics of this volatility. The co-movements in financial markets and financial assets around the globe have recently become the main area of interest of financial econometricians; hence, multivariate GARCH models have been introduced in order to capture these co-movements. A large variety of multivariate GARCH models exists in the financial world, and each of these models has its advantages and limitations. An important goal in constructing multivariate GARCH models is to make them parsimonious enough without compromising their adequacy in real-world applications. Another aspect is to ensure that the conditional covariance matrix is a positive-definite one. Motivated by the idea that volatility in financial markets is driven by a few latent variables, a new parameterization in multivariate context is proposed in this thesis. The factors in our proposed model are obtained through a recursive use of the singular value decomposition (SVD). This recursion enables us to sequentially extract the volatility clustering from the data set; accordingly, our model is called Sequential Volatility Extraction (SVX model in short). Logarithmically transformed singular values and the components of their corresponding singular vectors were modeled using the ARMA approach. We can say that in terms of basic idea and modeling approach our model resembles a stochastic volatility model. Empirical analysis and the comparison with the already existing multivariate GARCH models show that our proposed model is parsimonious because it requires lower number of parameters to estimate when compared to the two alternative models (i.e., DCC and GOGARCH). At the same time, the resulting covariance matrices from our model are positive-(semi)-definite. Hence, we can argue that our model fulfills the basic requirements of a multivariate GARCH model. Based on the findings, it can be concluded that SVX model can be applied to financial data of dimensions ranging from low to high.

This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk measures. Furthermore, the different dependence models are used to analyze the integration of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model. The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no single parametric model dominates all other models for all portfolios and risk measures. The investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less integrated into the world economy than proprietary. Thus, secondary emerging markets are more suitable to diversify a portfolio consisting of developed equity or bond indices than proprietary

We introduce the ``reflexivity'' index that quantifies the relative importance of short-term endogeneity for financial markets (financial indices, future commodity markets) from mid-2000s to October 2012. Our reflexivity index is defined as the average ratio of the number of price moves that are due to endogenous interactions to the total number of all price changes, which also include exogenous events. It is obtained by calibrating the Hawkes self-excited conditional Poisson model on time series of price changes. The Hawkes model accounts simultaneously for the co-existence and interplay between the exogenous impact of news and the endogenous mechanism by which past price changes may influence future price changes. Our robustness tests show that our index provides a 'pure' measure of endogeneity that is independent of the rate of activity, order size, volume or volatility. We find an overall increase of the reflexivity index since the mid-2000s to October 2012, which implies that at least 60-70 percent of financial price changes are now due to self-generated activities rather than novel information, compared to 20-30 percent earlier. While our reflexivity index is defined on short-time windows (10-30 minutes) and thus does not capture long-term memory, we discover striking coincidence between its dynamics and that of the price hikes and abrupt falls that developed during financial bubble regimes. We also show that the ``reflexivity'' index allows one to disentangle the internal dynamics from exogenous factors within the Autoregressive Conditional Duration (ACD) model. We provide a direct comparison of the Hawkes and ACD models based on numerical simulations and show that our effective measure of endogeneity for the ACD can be mapped onto the ``branching ratio'' of the Hawkes model. This opens the road to quantify the degree of endogeneity in the large class of auto-regressive models such as GARCH and extensions. The talk will provide an opportunity to present results of the Financial Crisis Observatory (www.er.ethz.ch/fco) at ETH Zurich, which aims at testing and quantifying rigorously, in a systematic way and on a large scale the hypothesis that financial bubbles can diagnosed with a rigorous scientific methodology before they burst.

Spannungen im Alltäglichen Themen: Cocktailbars Teilnehmer: Herb Frankfurter Franz Blum

Spannungen im Alltäglichen Themen: Cocktailbars Teilnehmer: Herb Frankfurter Franz Blum

Boosting is an iterative algorithm for functional approximation and numerical optimization which can be applied to solve statistical regression-type problems. By design, boosting can mimic the solutions of many conventional statistical models, such as the linear model, the generalized linear model, and the generalized additive model, but its strength is to enhance these models or even go beyond. It enjoys increasing attention since a) it is a generic algorithm, easily extensible to exciting new problems, and b) it can cope with``difficult'' data where conventional statistical models fail. In this dissertation, we design autoregressive time series models based on boosting which capture nonlinearity in the mean and in the variance, and propose new models for multi-step forecasting of both. We use a special version of boosting, called componentwise gradient boosting, which is innovative in the estimation of the conditional variance of asset returns by sorting out irrelevant (lagged) predictors. We propose a model which enables us not only to identify the factors which drive market volatility, but also to assess the specific nature of their impact. Therefore, we gain a deeper insight into the nature of the volatility processes. We analyze four broad asset classes, namely, stocks, commodities, bonds, and foreign exchange, and use a wide range of potential macro and financial drivers. The proposed model for volatility forecasting performs very favorably for stocks and commodities relative to the common GARCH(1,1) benchmark model. The advantages are particularly convincing for longer forecasting horizons. To our knowledge, the application of boosting to multi-step forecasting of either the mean or the variance has not been done before. In a separate study, we focus on the conditional mean of German industrial production. With boosting, we improve the forecasting accuracy when compared to several competing models including the benchmark in this field, the linear autoregressive model. In an exhaustive simulation study we show that boosting of high-order nonlinear autoregressive time series can be very competitive in terms of goodness-of-fit when compared to alternative nonparametric models. Finally, we apply boosting in a spatio-temporal context to data coming from outside the econometric field. We estimate the browsing pressure on young beech trees caused by the game species within the borders of the Bavarian Forest National Park ``Bayerischer Wald,'' Germany. We found that using the geographic coordinates of the browsing cases contributes considerably to the fit. Furthermore, this bivariate geographic predictor is better suited for prediction if it allows for abrupt changes in the browsing pressure.

Best of Volatility Views: Volatility Discussion with Nobel Laureate Robert Engle Mark and Don have the honor of speaking with Professor Engle on various aspects of volatility, including: Professor Engle's move from physics to economics ARCH and GARCH models, and how the ARCH model was born The Volatility Institute Vlab SoFiE Professor Engle's work with the SEC's Flash Crash Advisory Committee The Professor's viewpoint on Dodd-Frank, particularly as it applies to derivatives & counterparty risk.

Volatility Views 49: How Best to Price Variance and Volatility Swaps Volatility Review: It's funny how it didn't take long for our prediction of a range-bound VIX to be proven wrong! The VVIX has been high. Is a tradable VIX of VIX in the works? What have the NASDAQ and SPX vol of vol six-month averages been? One-year? Don gives some remarkable numbers. Apple on the move downwards; some are eager to buy Apple at 600. NASDAQ vol cones, like most volatility readings, are at historical lows. Volatility Viewpoint: Today's guests, Thomas Thorsen and Emil Stamp, are the authors of the master's thesis, "Pricing of Variance and Volatility Swaps in a Stochastic Volatility and Jump Framework." The gang discusses pricing models of OTC variance swaps, the different models the authors created, and the accuracy of the models for both forecasting realized volatility, as well as pricing of variance swaps and GARCH models in real-world applications. Crystal Ball: Are you still shorting the VIX? Are higher levels forecasted for the future? Perception versus reality.

Volatility Views 6: Volatility Discussion with Nobel Laureate Robert EngleMark and Don have the honor of speaking with Professor Engle on various aspects of volatility, including: Professor Engle's move from physics to economics ARCH and GARCH models, and how the ARCH model was born The Volatility Institute Vlab SoFiE Professor Engle's work with the SEC's Flash Crash Advisory Committee The Professor's viewpoint on Dodd-Frank, particularly as it applies to derivatives & counterparty risk.

Volatility Views 8: Live From OIC 2011Live from Savannah, Mark is joined by Robert Krause, CEO and Chairman of the Volatility Exchange. Volatility Review:  Euro vol contract update: stable, but at a high level, hanging out at the 12.50 range.  Bob gives a detailed overview of how the product works.Crystal Ball:  The news makes it reasonable to think that volatility will stay high.  What is coming up next for VolX?  New volatility indices: three different time periods for three new indices, a historical lookback, the volatility of volatility, and a GARCH-based forecast.

Volatility Views 15: Know Your VolatilityVolatility Review:  Vol gets demolished. Volatility modeling is not accurate. Range-bound volatility.  Euro vol at expiration looking calmer than previous expirations.Volatility Viewpoint: Featuring Mike Cavanaugh from Know Your Options, Inc.  The gang discusses the role of volatility in their collar strategies, the great VIX disconnect, self-directed trading, VolX products, and appropriate hedging techniques for different audiences.Crystal Ball:  Earnings season.  European debt woes. Uptick in vol for currencies, and overall volatility.  Friday was the Euro VolContract expiration.  August contract coming up.  Euro vol abatement indicated by GARCH model.

In this paper we introduce a fractionally integrated exponential continuous time GARCH(p,d,q) process. It is defined in such a way that it is a continuous time extension of the discrete time FIEGARCH(p,d,q) process. We investigate stationarity and moment properties of the new model. It is also shown that the long memory effect introduced in the log-volatility propagates to the volatility process.

In this paper we introduce an exponential continuous time GARCH(p,q) process. It is defined in such a way that it is a continuous time extension of the discrete time EGARCH(p,q) process. We investigate stationarity and moment properties of the new model. An instantaneous leverage effect can be shown for the exponential continuous time GARCH(p,p) model.

We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on equally spaced observations. Using the fact that the increments of the COGARCH(1,1) process are ergodic, the resulting estimators are consistent. We investigate the quality of our estimators in a simulation study based on the compound Poisson driven COGARCH model. The estimated volatility with corresponding residual analysis is also presented.

A continuous time GARCH model of order (p,q) is introduced, which is driven by a single Lévy process. It extends many of the features of discrete time GARCH(p,q) processes to a continuous time setting. When p=q=1, the process thus defined reduces to the COGARCH(1,1) process of Klüppelberg, Lindner and Maller (2004). We give sufficient conditions for the existence of stationary solutions and show that the volatility process has the same autocorrelation structure as a continuous time ARMA process. The autocorrelation of the squared increments of the process is also investigated, and conditions ensuring a positive volatility are discussed.

Empirical volatility changes in time and exhibits tails, which are heavier than normal. Moreover, empirical volatility has - sometimes quite substantial - upwards jumps and clusters on high levels. We investigate classical and non-classical stochastic volatility models with respect to their extreme behavior. We show that classical stochastic volatility models driven by Brownian motion can model heavy tails, but obviously they are not able to model volatility jumps. Such phenomena can be modelled by Levy driven volatility processes as, for instance, by Levy driven Ornstein-Uhlenbeck models. They can capture heavy tails and volatility jumps. Also volatility clusters can be found in such models, provided the driving Levy process has regularly varying tails. This results then in a volatility model with similarly heavy tails. As the last class of stochastic volatility models, we investigate a continuous time GARCH(1,1) model. Driven by an arbitrary Levy process it exhibits regularly varying tails, volatility upwards jumps and clusters on high levels.

We use a discrete time analysis, giving necessary and sufficient conditions for the almost sure convergence of ARCH(1) and GARCH(1,1) discrete time models, tosuggest an extension of the (G)ARCH concept to continuous time processes. Our "COGARCH" (continuous time GARCH) model, based on a single background driving Levy process, is different from, though related to, other continuous time stochastic volatility models that have been proposed. The model generalises the essential features of discrete time GARCH processes, and is amenable to further analysis, possessing useful Markovian and stationarity properties.

We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic volatility model of Barndorff-Nielsen and Shephard (2001) with those of the COGARCH process. The latter is a continuous time GARCH process introduced by the authors (2004). Many features are shown to be shared by both processes, but differences are pointed out as well. Furthermore, it is shown that the COGARCH process has Pareto like tails under weak regularity conditions.

We use a discrete time analysis, giving necessary and sufficient conditions for the almost sure convergence of ARCH(1) and GARCH(1,1) discrete time models, to suggest an extension of the (G)ARCH concept to continuous time processes. The models, based on a single background driving Levy process, are different from, though related to, other continuous time stochastic volatility models that have been proposed. Our models generalise the essential features of discrete time GARCH processes, and are amenable to further analysis, possessing useful Markovian and stationarity properties.