Results 41 to 50 of about 513 (158)

Backtesting lambda value at risk [PDF]

The European Journal of Finance, 2017
A new risk measure, the lambda value at risk (Lambda VaR), has been recently proposed from a theoretical point of view as a generalization of the value at risk (VaR). The Lambda VaR appears attractive for its potential ability to solve several problems of the VaR. In this paper we propose three nonparametric backtesting methodologies for the Lambda VaR
Corbetta, J., Peri, Ilaria
openaire   +2 more sources

Data Driven Investment Strategies Using Bayesian Inference in Regime‐Switching Models

Applied Stochastic Models in Business and Industry, Volume 41, Issue 6, November/December 2025.
ABSTRACT This article presents the benefits of using Bayesian algorithms to fit regime‐switching models to daily financial returns data in order to design trading strategies. Our study focuses on a Gaussian hidden Markov model (HMM). We show how the application of a simple smoothing technique preserves the hidden Markov structure and facilitates regime
Eléonore Blanchard, Pierre‐Olivier Goffard   +1 more
wiley   +1 more source

Analyzing the use of generalized hyperbolic distributions to value at risk calculations

Economia Aplicada, 2005
The goal of this paper is to analyze the use of the Generalized Hyperbolic (GH) Distributions to model the US Dollar/Brazilian Real exchange rate in a way to produce more accurate VaR (Value at Risk) measurements.
José Santiago Fajardo Barbadian, Aquiles Rocha de Farias, José Renato Haas Ornelas   +2 more
doaj  

Trading Games: Beating Passive Strategies in the Bullish Crypto Market

Journal of Futures Markets, Volume 45, Issue 11, Page 1911-1933, November 2025.
ABSTRACT This study examines the effectiveness of cointegrated pairs trading in cryptocurrency markets, introducing systematic parameter optimization within the trading framework. The analysis is conducted using a dataset comprising ten major cryptocurrencies, selected based on market capitalization and consensus mechanism, spanning the period from ...
Rafael Baptista Palazzi
wiley   +1 more source

Evaluation of Value-at-Risk (VaR) using the Gaussian Mixture Models

Research in Statistics
The normality of the distribution of stock returns is one of the basic assumptions in financial mathematics. Empirical studies, however, undermine the validity of this assumption.
Indrė Morkūnaitė, Dmitrij Celov, Remigijus Leipus   +2 more
doaj   +1 more source

RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)

Media Statistika, 2022
Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return.
Tarno Tarno, Trimono Trimono, Di Asih I Maruddani, Yuciana Wilandari, Rianti Siwi Utami   +4 more
doaj   +1 more source

Asymmetric stable stochastic volatility models: estimation, filtering, and forecasting

Journal of Time Series Analysis, Volume 46, Issue 6, Page 1098-1124, November 2025.
This article considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation‐based methods to address key challenges in parameter estimation, the filtering of time‐varying volatility, and volatility forecasting.
Francisco Blasques, Siem Jan Koopman, Karim Moussa   +2 more
wiley   +1 more source

ESTIMATION OF VALUE AT RISK FOR GENERAL INSURANCE COMPANY STOCKS USING THE GARCH MODEL

Barekeng
Investment plays a crucial role in supporting economic development by allocating funds to generate future profits. Among various investment options, stock investment is widely popular.
Edwin Setiawan Nugraha, Agna Olivia, Fauziah Nur Fahirah Sudding, Karunia Eka Lestari   +3 more
doaj   +1 more source

Estimation and Inference for Higher‐Order Stochastic Volatility Models With Leverage

Journal of Time Series Analysis, Volume 46, Issue 6, Page 1064-1084, November 2025.
ABSTRACT Statistical inference—estimation and testing—for stochastic volatility models is challenging and computationally expensive. This problem is compounded when leverage effects are allowed. We propose efficient, simple estimators for higher‐order stochastic volatility models with leverage [SVL(p)$$ (p) $$], based on a small number of moment ...
Md. Nazmul Ahsan, Jean‐Marie Dufour, Gabriel Rodriguez‐Rondon   +2 more
wiley   +1 more source

Backtesting the Lee–Carter and the Cairns–Blake–Dowd Stochastic Mortality Models on Italian Death Rates

Risks, 2017
This work proposes a backtesting analysis that compares the Lee–Carter and the Cairns–Blake–Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975 ...
Carlo Maccheroni, Samuel Nocito
doaj   +1 more source