The calculated approximation of a result which is usable even if input data may be incomplete or uncertain.

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1
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18 views

Relationship between in-sample and out-sample periods length

I have two general questions regarding "in-sample fitting vs. out-of-sample backtesting" kind of analyses. Is there any relationship between the length of the data collected for in-sample fitting ($a$)...
0
votes
1answer
70 views

Package for multivariate Garch Vech model for R?

I`m new to programming and searching a package for R which inherents the estimation for a Vech Garch(1,1). This is a multivariate Garch model which forms the residuals and the covariance matrix from a ...
4
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1answer
98 views

How are Quandl monthly S&P500 earnings estimates derived?

Can someone explain how the monthly earnings estimates are derived for S&P500? Quandl sources multpl.com, who state: ...
0
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1answer
35 views

What is Estimation Risk - VAR Backtest

Simple Question. Can someone explain please: What is Estimation Risk in Value at Risk Backtesting
0
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0answers
13 views

Do smaller horizons better estimate volatility for longer horizons than the longer horizons?

Suppose you want an estimate of the 20 day return variance. You could grab historical lagging 20 day windows to build an estimate, or you could build 10 day lagging windows (twice as many data points) ...
0
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0answers
20 views

Modeling the distrubution of future swap rates

I'm interested in better understanding the unwind cost/value of a swap at various points in the future. Suppose that we have entered a 7Y swap (paying fixed) and want to understand the unwind cost/...
0
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0answers
27 views

is mean and variance sufficient to determine the following question?

consider this question: I want to buy AAPL, and I could buy it from 3 brokers. I have their historical quote. Is there any statistic other than mean and variance I could use to determine if one broker ...
0
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0answers
19 views

Asset weights in asset allocation

I am trying to solve an asset allocation problem by approximating the expected utility of a portfolio. I am using the following formula: $E_t [U(1 + w_t' r_{t+1} + w_{ft} r_{f, t + 1})] = m_{1,t+1} - ...
2
votes
1answer
101 views

GJR-GARCH with $\alpha = 0$ as parameter estimate

I am estimating a GJR-GARCH(1,1) model with variance targeting in R. As data I am using returns on some stock indices. While calculating the GARCH models I obtain $\alpha=0$ for some indices. From ...
4
votes
2answers
106 views

Degrees of freedom in calculating significance of GARCH coefficients

I am trying to determine the significance of coefficients of a GARCH model by calculate the p-values using the following Matlab formula: pvalues = 2*(1-tcdf(abs(t),n-v)), where $t$ is the t-stat,...
6
votes
0answers
166 views

Estimating Parameters - Vasicek

The Vasicek model for the short rate $r_t$ is given by the SDE $$ dr_t = \alpha(\beta - r_t)dt + \sigma dW_t, $$ where $W_t$ is a Brownian motion under the physical measure. I'd like to compute bond ...
21
votes
3answers
1k views

How are distributions for tail risk measures estimated in practice?

Let's say you want to calculate a VaR for a portfolio of 1000 stocks. You're really only interested in the left tail, so do you use the whole set of returns to estimate mean, variance, skew, and shape ...
0
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1answer
39 views

Imposing MLE restrictions by logistic mapping

I am doing some Maximum Likelihood Estimation with a density that has time-varying parameters. I am using the fmincon function in Matlab, but I do not know how to ...
1
vote
0answers
66 views

How to fit exogenous + GARCH Model In Python?

I am studying a textbook of statistics / econometrics, using Python for my computational needs. I have encountered GARCH models and my understanding is that this is a commonly used model. In an ...
0
votes
0answers
90 views

Forecasting conditional variance using fGARCH

I am forecasting the conditional standard deviation using ARMA(1,0)-GJRGARCH(1,1) in R using the fGarch package. Here is a sample code: ...
1
vote
0answers
138 views

Skewed Generalized Error Distribution's (SGED) pdf

I want to use the SGED distribution of Theodossiou for GARCH estimation, however, I am struggling to understand which is the correct pdf function of the distribution. Let me just say that the ...
0
votes
1answer
101 views

How to estimate today's closing price?

I'm working on interday trading algorithm and I have a basic question: How can I estimate today's closing price? I need it to predict tomorrow closing price. Should I use the price few moments before ...
0
votes
0answers
193 views

Forecasting conditional mean in ARMA-GARCH model (R/Matlab)

I am trying to forecast the conditional mean from a ARMA(1,0)-GARCH(1,1) model. The mean equation in my model is: $x_t = \mu + \delta x_{t-1} + h_t \epsilon_t$ where x is the variable (a return ...
0
votes
0answers
90 views

Skewed Generalized Error Distribution in GARCH modelling

I am trying to estimate GARCH models with the use of Theodossiou's (2000) Skewed Generalized Error Distribution. I am modifying matlab's ARMAX-GARCH-K toolbox to calculate this model. I am calculating ...
3
votes
1answer
128 views

Log-likelihood of skew-t distribution

I am trying to estimate GARCH models with the use of Hansen's (1994) skew-t distribution. I am using matlab's ARMAX-GARCH-K toolbox, where the log-likelihood is calculated as: ...
6
votes
2answers
220 views

What is the preferred GARCH method in practice?

My advance apologies, if this question is too naive or basic. Please be patient with my first experiences with SE; ask for clarification, if needed. I recognize there are many (often-criticized) ...
2
votes
1answer
332 views

2-step estimation of DCC GARCH model in Python

Embedded in this thread are multiple questions. I'm currently im the process of implementing a DCC GARCH forecast model on quantopian (a python-powered trading platform). The two step consists of ...
3
votes
1answer
322 views

Stochastic Volatility CIR estimation

Would anyone have a code (pref. Matlab or R) for any type of estimation (QML, GMM) not using option prices of a stochastic volatility model driven by a CIR process described below? \begin{equation} ...
1
vote
2answers
152 views

How to estimate probable seeling pricegiven OHLC data for backtesting?

I'm relative new to this, so I might be asking something that doesn't make sense. Here is my scenario: I have intraday day at 1 minute intervals. This data has ohlc data and I want to compute for any ...
21
votes
3answers
2k views

Tools in R for estimating time-varying copulas?

Are there libraries in R for estimating time-varying joint distributions via copulas? Hedibert Lopes has an excellent paper on the topic here. I know there is an existing packaged called copula but ...
2
votes
0answers
38 views

How to calculate the estimation error of portfolio variance using propagation results?

I am trying to find a conservative approximation for the propagated estimation error of a investment portfolio's variance (comprising two assets), given we know the estimation error for the variance ...
0
votes
0answers
130 views

Maximum Likelihood Estimation Heston Model using Matlab

My question is based on the MLE of the Heston model discussed in this paper URL: http://www.princeton.edu/~yacine/stochvol.pdf with Matlab code: http://www.princeton.edu/~yacine/closedformmle.htm ...
3
votes
4answers
481 views

Unsmoothing of returns

The following problem arises in the context of private equity, which typically report "smoothed" returns (think of it as a moving average). As you can imagine, "smoothed" returns would have a much ...
2
votes
1answer
109 views

Estimating correlation using EWMA

I am using an EWMA model to evaluate the correlation between yearly time series. I know Riskmetrics uses $\lambda=0.94$ for daily data and $\lambda=0.97$ for monthly data. Is there a value ...
2
votes
2answers
180 views

Extracting Signal from Noisy Data

Consider a scenario in which Y_t represents the % change in price and we want to use X_t to predict Y_t. We assume that X_t is information we get before Y_t is revealed. Suppose that in reality Y_t =...
5
votes
2answers
121 views

Is there a relation between these two forecasting/estimation approaches?

When learning econometrics I have usually seen stuff from the following perspective: Assume $Y_t = f(X_t) + e_t$, where f is some function of $X_t$ (typically linear). For example, assume $Y_t = X_t ...
1
vote
1answer
15 views

Measure difference between estimations and historic returns

For every day in a year, I have the return on an asset and the CAPM estimation for the return. I want to measure the average difference between the set of returns and set of estimations. So far, I ...
6
votes
0answers
69 views

What kind of errors arise when I fit ARMA(1,1) to data generated from ARMA(1,1)-GARCH(1,1) process?

As far as I know estimates of parameters of ARMA(1,1) are asymptotically optimal when fitted to data from ARMA(1,1)-GARCH(1,1) process, and only their variance increase, so when we assume large ...
0
votes
3answers
395 views

Parametric/Analytical VaR

Suppose I want to calculate VaR for a known distribution with mean $\mu$, variance $\sigma^2$ and $\alpha$-quantile as, $VaR_{\alpha}$ = $\mu + \sigma q_{\alpha}$. For a Gaussian distribution it is ...
9
votes
1answer
456 views

What good papers of short term (<30 seconds) volatility estimation [duplicate]

I am looking for good papers of short term (<30 sec) volatility estimation AND short term volatility forecasting. Do you have something in mind ?
0
votes
1answer
42 views

Where to get analysts' earnings estimates data? [duplicate]

I know thomsonreuters provide analysts' earnings estimates data. Is there any sources I could get it for free? For example, this website has analysts' earnings data. What's the source of this and how ...
10
votes
3answers
368 views

How to account for market movement when some exchanges are closed?

Daily data, such as open and close prices, is often available for much longer periods than high-frequency data. However, whenever backtesting any strategy that examines instruments traded in different ...
2
votes
1answer
241 views

Predicting stock returns - in a panel data specification or by using portfolio formation strategies?

I'm working on an empirical analysis where I try to predict stock returns using weekly data. Ideally, I would like to use a panel data model like the following: $$ Y_{it}=X_{it}'\beta+\varepsilon_{it}...
7
votes
2answers
471 views

Efficiency vs. Robustness - To use a constant or not in single factor time-series regression?

Arbitrage pricing theory states that expected returns for a security are linear combination of exposures to risk factors and the returns on these risk factors. Betas, or the exposures of the security ...
10
votes
2answers
451 views

How to estimate the following model?

Suppose I have the following model: $$r_t=\sigma_t * \epsilon_t$$ where $r_t$ is the return at time t, $\sigma_t$ is the volatility, the model used to model this volatility is an exponentially ...
0
votes
1answer
150 views

Where can I find historical data for volatility estimation?

I'm trying to estimate volatility following Shreve book, so I need observations of $f(t_j,t_j+\tau_k)$ and $f(t_j+\delta,t_j+\tau_k)$, where $t_J<t_{J-1}<\dots<0$ and $\tau_k$ are relative ...
3
votes
2answers
235 views

What nonparametric methods exist for estimating intraday seasonalities?

What nonparametric "Model Free" methods exist to measure intraday seasonality? I would like to estimate intraday seasonality in any of The volatility The traded volume The bid ask spread or ...
1
vote
0answers
55 views

What are the estimation methods for SV models?

I want to know about some methods like Methods-of-Moments, Quasi-Maximum Likelihood method, Baysian methods using Markov Chain Monte Carlo methods. Is there any reference to have an idea of these ...
1
vote
0answers
101 views

Derivation of a ML estimator

I have the following likelihood function: I'm given this information about the $\Omega$ matrix ($\boldsymbol{1}$ is a $T \times 1$ vector of ones): I would like to be able to show that the ...
5
votes
2answers
852 views

How to use a realized kernel?

I've read that realized kernels are the thing to use for calculating daily volatility from high-frequency data. So I've got minute data, how do I actually use such a kernel? Will it give me minute-ly ...
3
votes
0answers
103 views

Derivation of variance of Zhou (1996) volatility estimator

Does anyone know how to derive the Variance of Bin Zhou's volatility estimator (Theorem 1) in 'High-Frequency Data and Volatility in Foreign-Exchange Rates' (1996) Zhou 1996 Any help would be ...
7
votes
2answers
581 views

Estimation of Empirical Expected Shortfall of a heavy tailed distribution

Assume that you have a portfolio for which you have estimated a parametric model to the underlying instruments, but the distribution of the portfolio as a whole is too complicated to compute ...
0
votes
1answer
143 views

What is the realized volatility's estimation error?

Given an estimation procedure and real data, how would one compute the mean squared error? What value represents the "true" realized volatility in the case of calculating the Mean Squared Error in ...
18
votes
5answers
3k views

What methods do you use to improve expected return estimates when constructing a portfolio in a mean-variance framework?

One of the main problems when trying to apply mean-variance portfolio optimization in practice is its high input sensitivity. As can be seen in (Chopra, 1993) using historical values to estimate ...
1
vote
1answer
196 views

Basics about the scaling property of volatility

It is a usual practice to calculate realized volatility $\sigma$ using the square root of the usual variance estimator $\hat{{\sigma}²}$. This is done using the stock log returns (practitioners ...