scholarly journals The left tail of renewal measure

2017 ◽  
Vol 129 ◽  
pp. 306-310 ◽  
Author(s):  
Bartosz Kołodziejek
Keyword(s):  
Renewal Measure ◽  
Left Tail

scholarly journals Investigating the Links between UK House Prices and Share Prices with Copulas

2021 ◽  
Author(s):  
Rakesh K. Bissoondeeal ◽  
Leonidas Tsiaras
Keyword(s):  
Stock Market ◽  
House Prices ◽  
National Level ◽  
Tail Dependence ◽  
Share Prices ◽  
Property Prices ◽  
Left Tail ◽  
The Uk ◽  
The Relationship ◽  
Shared Characteristic

AbstractWe investigate the nonlinear links between the housing and stock markets in the UK using copulas. Our empirical analysis is conducted at both the national and regional levels. We also examine how closely London house prices are linked to those in other parts of the UK. We find that (i) the dependence between the different markets exhibits significant time-variation, (ii) at the national level, the relationship between house prices and the stock market is characterised by left tail dependence, i.e., they are more likely to crash, rather than boom, together, (iii) although left tail dependence with the stock market is a prominent feature of some regions, it is by no means a universally shared characteristic, (iv) the dependence between property prices in London and other parts of the UK displays widespread regional variations.

scholarly journals Forecasting Large Price Declines of the Nikkei Using the S&P 500 Implied Volatility

2016 ◽  
Vol 8 (1) ◽  
pp. 58
Author(s):  
Chikashi Tsuji
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Implied Volatility ◽  
Predictive Power ◽  
Downside Risk ◽  
Price Changes ◽  
Empirical Results ◽  
Left Tail

This paper empirically examines the forecast power of the previous day’s US implied volatility for large declines of the Nikkei by using several versions of quantile regression models. All our empirical results suggest that the previous day’s US S&P 500 implied volatility has forecast power for large price drops of the Nikkei 225 in Japan. Since we repeatedly and carefully tested the several left tail risks in price changes of the Nikkei and we also tested by using some different versions of quantile regression models, our evidence of the predictive power of the S&P 500 implied volatility for downside risk of the Nikkei is very robust.

The Impact of Volatility Targeting

2018 ◽  
Keyword(s):  
Sharpe Ratio ◽  
Leverage Effect ◽  
Left Tail ◽  
Risk Parity ◽  
Asset Classes ◽  
Equity Portfolios ◽  
Sharpe Ratios ◽  
Tail Events ◽  
The Impact ◽  
Extreme Returns

Recent studies show that volatility-managed equity portfolios realize higher Sharpe ratios than portfolios with a constant notional exposure. The authors show that this result only holds for risk assets, such as equity and credit, and they link this finding to the so-called leverage effect for those assets. In contrast, for bonds, currencies, and commodities, the impact of volatility targeting on the Sharpe ratio is negligible. However, the impact of volatility targeting goes beyond the Sharpe ratio: It reduces the likelihood of extreme returns across all asset classes. Particularly relevant for investors, left-tail events tend to be less severe because they typically occur at times of elevated volatility, when a target-volatility portfolio has a relatively small notional exposure. We also consider the popular 60–40 equity–bond balanced portfolio and an equity–bond–credit–commodity risk parity portfolio. Volatility scaling at both the asset and portfolio level improves Sharpe ratios and reduces the likelihood of tail events.

scholarly journals On the efficient simulation of the left-tail of the sum of correlated log-normal variates

2018 ◽  
Vol 24 (2) ◽  
pp. 101-115 ◽  
Author(s):  
Mohamed-Slim Alouini ◽  
Nadhir Ben Rached ◽  
Abla Kammoun ◽  
Raul Tempone
Keyword(s):  
Communication Systems ◽  
Variance Reduction ◽  
Gaussian Copula ◽  
Small Probability ◽  
Control Variate ◽  
Reduction Techniques ◽  
Left Tail ◽  
Mild Assumption ◽  
Log Normal ◽  
The Right

Abstract The sum of log-normal variates is encountered in many challenging applications such as performance analysis of wireless communication systems and financial engineering. Several approximation methods have been reported in the literature. However, these methods are not accurate in the tail regions. These regions are of primordial interest as small probability values have to be evaluated with high precision. Variance reduction techniques are known to yield accurate, yet efficient, estimates of small probability values. Most of the existing approaches have focused on estimating the right-tail of the sum of log-normal random variables (RVs). Here, we instead consider the left-tail of the sum of correlated log-normal variates with Gaussian copula, under a mild assumption on the covariance matrix. We propose an estimator combining an existing mean-shifting importance sampling approach with a control variate technique. This estimator has an asymptotically vanishing relative error, which represents a major finding in the context of the left-tail simulation of the sum of log-normal RVs. Finally, we perform simulations to evaluate the performances of the proposed estimator in comparison with existing ones.

Consumption, aggregate wealth and expected stock returns: a quantile cointegration approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ricardo Quineche
Keyword(s):  
Stock Returns ◽  
The United States ◽  
Labor Income ◽  
Left Tail ◽  
Long Run ◽  
Short Run ◽  
Excess Stock Returns ◽  
Aggregate Wealth ◽  
Risk Free Rate ◽  
The Right

Abstract This paper empirically examines the long-run relationship between consumption, asset wealth and labor income (i.e., cay) in the United States through the lens of a quantile cointegration approach. The advantage of using this approach is that it allows for a nonlinear relationship between these variables depending on the level of consumption. We estimate the coefficients using a Phillips–Hansen type fully modified quantile estimator to correct for the presence of endogeneity in the cointegrating relationship. To test for the null of cointegration at each quantile, we apply a quantile CUSUM test. Results show that: (i) consumption is more sensitive to changes in labor income than to changes in asset wealth for the entire distribution of consumption, (ii) the elasticity of consumption with respect to labor income (asset wealth) is larger at the right (left) tail of the consumption distribution than at the left (right) tail, (iii) the series are cointegrated around the median, but not in the tails of the distribution of consumption, (iv) using the estimated cay obtained for the right (left) tail of the distribution of consumption improves the long-run (short-run) forecast ability on real excess stock returns over a risk-free rate.

How is the change in left-tail risk priced in China?

2021 ◽  
pp. 101703
Author(s):  
Kaisi Sun Sun ◽  
Hui Wang Wang ◽  
Yifeng Zhu
Keyword(s):  
Tail Risk ◽  
Left Tail

Left-tail momentum: Underreaction to bad news, costly arbitrage and equity returns

2020 ◽  
Vol 135 (3) ◽  
pp. 725-753 ◽  
Author(s):  
Yigit Atilgan ◽  
Turan G. Bali ◽  
K. Ozgur Demirtas ◽  
A. Doruk Gunaydin
Keyword(s):  
Equity Returns ◽  
Bad News ◽  
Left Tail

ON HETEROGENEITY IN THE INDIVIDUAL MODEL WITH BOTH DEPENDENT CLAIM OCCURRENCES AND SEVERITIES

2018 ◽  
Vol 48 (02) ◽  
pp. 817-839 ◽  
Author(s):  
Yiying Zhang ◽  
Xiaohu Li ◽  
Ka Chun Cheung
Keyword(s):  
Claim Amount ◽  
Common Belief ◽  
Individual Model ◽  
Numerical Examples ◽  
Scale Parameters ◽  
Aggregate Claim ◽  
Left Tail ◽  
Insurance Portfolio ◽  
The Individual ◽  
Arrangement Increasing

AbstractIt is a common belief for actuaries that the heterogeneity of claim severities in a given insurance portfolio tends to increase its dangerousness, which results in requiring more capital for covering claims. This paper aims to investigate the effects of orderings and heterogeneity among scale parameters on the aggregate claim amount when both claim occurrence probabilities and claim severities are dependent. Under the assumption that the claim occurrence probabilities are left tail weakly stochastic arrangement increasing, the actuaries' belief is examined from two directions, i.e., claim severities are comonotonic or right tail weakly stochastic arrangement increasing. Numerical examples are provided to validate these theoretical findings. An application in assets allocation is addressed as well.

Six Sigma metrics based on lognormal distribution for life tests

2019 ◽  
Vol 36 (9) ◽  
pp. 1477-1489 ◽  
Author(s):  
Ravichandran Joghee
Keyword(s):  
Six Sigma ◽  
Lognormal Distribution ◽  
Tail Probability ◽  
Tail Probabilities ◽  
Content Type ◽  
Underlying Distribution ◽  
Left Tail ◽  
Long Run ◽  
Life Tests ◽  
The Right

Purpose The purpose of this paper is to propose an approach for studying the Six Sigma metrics when the underlying distribution is lognormal. Design/methodology/approach The Six Sigma metrics are commonly available for normal processes that are run in the long run. However, there are situations in reliability studies where non-normal distributions are more appropriate for life tests. In this paper, Six Sigma metrics are obtained for lognormal distribution. Findings In this paper, unlike the normal process, for lognormal distribution, there are unequal tail probabilities. Hence, the sigma levels are not the same for left-tail and right-tail defects per million opportunities (DPMO). Also, in life tests, while left-tail probability is related to DPMO, the right tail is considered as extremely good PMO. This aspect is introduced and based on which the sigma levels are determined for different parameter settings and left- and right-tail probability combinations. Examples are given to illustrate the proposed approach. Originality/value Though Six Sigma metrics have been developed based on a normality assumption, there have been no studies for determining the Six Sigma metrics for non-normal processes, particularly for life test distributions in reliability studies. The Six Sigma metrics developed here for lognormal distribution is new to the practitioners, and this will motivate the researchers to do more work in this field of research.

Party Cohesion in Westminster Systems: Inducements, Replacement and Discipline in the House of Commons, 1836–1910

2014 ◽  
Vol 46 (3) ◽  
pp. 567-589 ◽  
Author(s):  
Andrew C. Eggers ◽  
Arthur Spirling
Keyword(s):  
Panel Data ◽  
Historical Development ◽  
Central Tendency ◽  
House Of Commons ◽  
New Members ◽  
Left Tail ◽  
Party Cohesion ◽  
Party Voting ◽  
The Universe ◽  
Member Of Parliament

This article considers the historical development of a characteristic crucial for the functioning and normative appeal of Westminster systems: cohesive legislative parties. It gathers the universe of the 20,000 parliamentary divisions that took place between 1836 and 1910 in the British House of Commons, construct a voting record for every Member of Parliament (MP) serving during this time, and conducts analysis that aims to both describe and explain the development of cohesive party voting. In line with previous work, it shows that – with the exception of a chaotic period in the 1840s and 1850s – median discipline was always high and increased throughout the century. The study uses novel methods to demonstrate that much of the rise in cohesion results from the elimination of a rebellious ‘left tail’ from the 1860s onwards, rather than central tendency shifts. In explaining the aggregate trends, the article uses panel data techniques and notes that there is scant evidence for ‘replacement’ explanations that involve new members behaving in more disciplined ways than those leaving the chamber. It offers evidence that more loyal MPs were more likely to obtain ministerial posts, and speculates that this and other ‘inducement’-based accounts offer more promising explanations of increasingly cohesive parties.

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