scholarly journals The Influence of Climate on Fire Damage

1963 ◽  
Vol 2 (3) ◽  
pp. 345-351 ◽  
Author(s):  
Hans Andersson
Keyword(s):  
Risk Premium ◽  
Geographical Variation ◽  
Building Blocks ◽  
Fire Damage ◽  
Risk Premiums ◽  
Heating Systems ◽  
General Insurance ◽  
Fire Insurance ◽  
The Mean ◽  
In Fire

In his paper “Actuarial Activity in General Insurance in the Northern Countries of Europe” L. Wilhelmsen gives amongst other things an account of the work carried on by Centralstället för nordisk ömsesidig Brandförsäkringsstatistik (CNÖB, the Northern Central Office for Fire Insurance Statistics from Mutual Companies). In this he states that one part of the organisation's work is the carrying out of special investigations of current problems with material collected on each occasion for the purpose.The object of this paper is to report investigations carried out in CNÖB on the connection between temperature and risk premium in fire insurance. The material used is made up exclusively of civil risks (buildings) and the material has been taken from Sweden and Norway. The background to the investigation consists in the fact that in Scandinavia the risk premiums for fire insurance show an apparent geographical variation, in that the amount clearly increases in the most northerly provinces.As we know that the fire damage directly or indirectly caused by heating systems (chimney fires, cracked building blocks, embers from fireplaces or chimneys etc.) represents a large proportion of the damage in civil risks (40-50%, in some materials 60% or more), that the proportion is greatest in the northern parts and that in the Swedish material about 50% of the damage in any one area falls in the four months December to March, it is quite reasonable to trace the influence of the temperature factor behind the geographical variation; the colder the climate, the more lighting of fires and the more damage. This line of argument is connected exclusively to the frequency of damage; we shall return later to the mean degree of damage.

The Determinants of the Ex Ante Risk Premiumin Commercial Real Estate

2019 ◽  
Vol 41 (3) ◽  
pp. 411-441
Author(s):  
El i Beracha ◽  
Julia Freybote ◽  
Zhenguo Lin
Keyword(s):  
Real Estate ◽  
Risk Premium ◽  
Predictive Power ◽  
Real Estate Market ◽  
Commercial Real Estate ◽  
Stock Market Volatility ◽  
Switching Regression ◽  
Risk Premiums ◽  
Debt Capital ◽  
Ex Ante

We investigate the determinants of the ex ante risk premium in commercial real estate. Using a 20-year time series and Markov-switching regression, we find that the ex ante risk premium is affected by fundamental and non-fundamental determinants, albeit not symmetrically when risk premiums are increasing and decreasing. In particular, we find that changes in debt capital market conditions have a higher predictive power for changes in the ex ante risk premium when it is increasing, while changes in stock market volatility and commercial real estate market returns have a higher predictive power when the risk premium is on the decline. In addition, changes in commercial real estate sentiment and NAREIT returns can predict changes in the ex ante risk premium; however, the predictive power of these variables varies across property types and risk premium (risk perception) states.

The nineteenth-century US whaling industry: Where is the risk premium? New materials facilitate updated view

2021 ◽  
Vol 33 (2) ◽  
pp. 344-363
Author(s):  
Barbara L. Coffey
Keyword(s):  
Nineteenth Century ◽  
Risk Premium ◽  
Financial Returns ◽  
New Materials ◽  
Government Bonds ◽  
Us Government ◽  
Whaling Industry ◽  
The Mean ◽  
Printed Materials

Materials that were born digital, and printed materials that have been digitized, have aided an updated examination of nineteenth-century US whaling voyages’ financial returns. Items included the American Offshore Whaling Voyages dataset from whalinghistory.org , The Whalemen’s Shipping List and Merchant’s Transcript, a congressman’s speech and a state’s census reports. These works and others, with analysis, showed that for the 11,257 analysable voyages ending in the 1800s, the mean return was 4.7% and 4.6% for whaling and US government bonds, respectively. Ideally, this work will place the nineteenth-century US whaling industry returns in context of other investments.

Fire and Post-Fire Performance of Space Grid Structures

2012 ◽  
Vol 238 ◽  
pp. 621-624 ◽  
Author(s):  
Guang Yong Wang ◽  
Xing Qiang Wang ◽  
Guang Wei Liu
Keyword(s):  
Finite Element ◽  
Failure Modes ◽  
Fe Model ◽  
Fire Damage ◽  
Fire Performance ◽  
Membrane Action ◽  
Load Bearing Capacity ◽  
Space Grid ◽  
Deformation Stress ◽  
In Fire

A fire performance finite element (FE) model of space grid structures in fire and after fire is proposed, and deformation, stress redistribution, failure modes of grid structures are also studied. The result shows that tensile membrane action arises when the grid is loaded after fire, and the load bearing capacity after fire is reduced by fire damage.

Impact of regulatory announcements on systemic risk in the Indian telecom sector

2018 ◽  
Vol 13 (5) ◽  
pp. 1395-1416 ◽  
Author(s):  
Sushma Priyadarsini Yalla ◽  
Som Sekhar Bhattacharyya ◽  
Karuna Jain
Keyword(s):  
Risk Premium ◽  
Systemic Risk ◽  
Service Providers ◽  
Policy Makers ◽  
Content Type ◽  
Risk Premiums ◽  
Buffering Hypothesis ◽  
Research Findings ◽  
Telecom Sector ◽  
The Impact

Purpose Post 1991, given the advent of liberalization and economic reforms, the Indian telecom sector witnessed a remarkable growth in terms of subscriber base and reduced competitive tariff among the service providers. The purpose of this paper is to estimate the impact of regulatory announcements on systemic risk among the Indian telecom firms. Design/methodology/approach This study employed a two-step methodology to measure the impact of regulatory announcements on systemic risk. In the first step, CAPM along with the Kalman filter was used to estimate the daily β (systemic risk). In the second step, event study methodology was used to assess the impact of regulatory announcements on daily β derived from the first step. Findings The results of this study indicate that regulatory announcements did impact systemic risk among telecom firms. The study also found that regulatory announcements either increased or decreased systemic risk, depending upon the type of regulatory announcements. Further, this study estimated the market-perceived regulatory risk premiums for individual telecom firms. Research limitations/implications The regulatory risk premium was either positive or negative, depending upon the different types of regulatory announcements for the telecom sector firms. Thus, this study contributes to the theory of literature by testing the buffering hypothesis in the context of Indian telecom firms. Practical implications The study findings will be useful for investors and policy-makers to estimate the regulatory risk premium as and when there is an anticipated regulatory announcement in the Indian telecom sector. Originality/value This is one of the first research studies in exploring regulatory risk among the Indian telecom firms. The research findings indicate that regulatory risk does exist in the telecom firms of India.

scholarly journals Experimental Validation of the Mean Pitch Theory

2021 ◽  
Vol 2090 (1) ◽  
pp. 012042
Author(s):  
T Meda ◽  
A Rogala
Keyword(s):  
Control Systems ◽  
Experimental Validation ◽  
Significant Loss ◽  
Simple Point ◽  
Fire Control ◽  
Mass Model ◽  
Body Model ◽  
Rigid Body Model ◽  
The Mean ◽  
In Fire

Abstract There are several types of exterior ballistic models used to calculate projectile’s flight trajectories. The most complex 6 degree of freedom rigid body model has many disadvantages to using it to create firing tables or rapid calculations in fire control systems. Some of ballistic phenomena can be simplified by empirical equations without significant loss of accuracy. This approach allowed to create standard NATO ballistic model for spin stabilized projectiles named Modified Point of Mass Model (PM Model). For fin (aerodynamically) stabilized projectiles like mortar projectiles simple Point of Mass Model is commonly used. The PM Model excludes many flight phenomena in calculations. In this paper authors show the mean pitch theory as an approximation of the natural fin stabilised projectile pitch during flight. The theory allows for simple improvement of accuracy of the trajectories calculation. In order to validate the theory data obtained from shooting of supersonic mortar projectiles were used. The comparison of accuracy between simple PM Model and PM Model including mean pitch theory were shown. Results were also compared with the angle of response theory.

scholarly journals On the Estimation of Cost of Equity using Industrial CAPM Approach

2020 ◽  
pp. 84-94
Author(s):  
Hassan Raza ◽  
Aijaz Mustafa Hashmi ◽  
Abdul Rasheed
Keyword(s):  
Family Environment ◽  
Risk Premium ◽  
Mutual Fund ◽  
Business Environment ◽  
Agency Cost ◽  
Risk Premiums ◽  
Fund Managers ◽  
Global Investors ◽  
Relationship Of ◽  
Industry Risk

This paper is an attempt to empirically investigate the industrial risk premium and realized return relationship by extending hybrid CAPM of Bodnar, Dumas, and Marston (2004). The inclusion of the industry risk premium offers more sophisticated results. Fama and Macbeth (1973) methodology are applied to test this relationship. The results indicate that there is a positive and significant relationship of the industry risk premium for Pakistan, India, and Brazil, whereas, it is insignificant for China, Russia, and South Africa. It is also seen that other risk premiums are insignificant for the said countries if industry risk premium is considered. The results also indicate that industry risk premium is only significant for those countries where the firms are mostly operated through the family business environment like Pakistan, India, and Brazil. This may lead to conclude that the industry risk premium can be used as the agency cost of minatory shareholders and controlling shareholders. This study provides an insight for the global investors, FPI holders, local and global mutual fund managers, to incorporate this industry risk premium into the existing CAPM framework especially for the countries where the business is managed as a family environment.

Financial Uncertainty with Ambiguity and Learning

2021 ◽  
Author(s):  
Hening Liu ◽  
Yuzhao Zhang
Keyword(s):  
Risk Premium ◽  
Regime Switching ◽  
Ambiguity Aversion ◽  
Volatility Risk ◽  
Asset Pricing Model ◽  
Variance Risk Premium ◽  
Financial Uncertainty ◽  
Empirical Relations ◽  
The Mean ◽  
Variance Risk

We examine a production-based asset pricing model with regime-switching productivity growth, learning, and ambiguity. Both the mean and volatility of the growth rate of productivity are assumed to follow a Markov chain with an unobservable state. The agent’s preferences are characterized by the generalized recursive smooth ambiguity utility function. Our calibrated benchmark model with modest risk aversion can match moments of the variance risk premium in the data and reconcile empirical relations between the risk-neutral variance and macroeconomic quantities and their respective volatilities. We show that the interplay between productivity volatility risk and ambiguity aversion is important for pricing variance risk in returns. This paper was accepted by Tomasz Piskorski, finance.

15. Quantitative Analysis: Description and Inference

2020 ◽  
pp. 393-421
Author(s):  
Sandra Halperin ◽  
Oliver Heath
Keyword(s):  
Quantitative Analysis ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quantitative Research ◽  
Building Blocks ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Margin Of Error ◽  
Univariate Statistics ◽  
The Mean

This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. After elaborating on descriptive statistics, the chapter explores inferential statistics and explains how to make generalizations. It also presents the concept of confidence intervals, more commonly known as the margin of error, and measures of central tendency.

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