| 1 | |
| 00:00:22,050 --> 00:00:27,550 | |
| طيب بسم الله الرحمن الرحيم في المحاضرة الماضية كنا نحكي | |
| 2 | |
| 00:00:27,550 --> 00:00:32,110 | |
| عن ال .. إذا كانت ال observation .. if the | |
| 3 | |
| 00:00:32,110 --> 00:00:35,130 | |
| observation is not following the normal distribution | |
| 4 | |
| 00:00:35,130 --> 00:00:40,490 | |
| إذا كانت البيانات ما بتتبعش التوزيع الطبيعي okay so | |
| 5 | |
| 00:00:40,490 --> 00:00:46,030 | |
| how we know the data is not following the normal | |
| 6 | |
| 00:00:46,030 --> 00:00:50,300 | |
| distribution? We check the skewness (skew) and we .. | |
| 7 | |
| 00:00:50,300 --> 00:00:54,660 | |
| we check the kurtosis. احنا بنعمل check على ال .. | |
| 8 | |
| 00:00:54,660 --> 00:00:59,860 | |
| الالتواء و على التفلطح بيسموه ال skew أو skewness و ال | |
| 9 | |
| 00:00:59,860 --> 00:01:05,780 | |
| .. و ال kurtosis زي ما احنا شوفنا المرة الفاتة و | |
| 10 | |
| 00:01:05,780 --> 00:01:06,960 | |
| احنا رسمنا مع بعض | |
| 11 | |
| 00:01:14,280 --> 00:01:19,340 | |
| Okay زي ما تشوف now if you look at this black one | |
| 12 | |
| 00:01:19,340 --> 00:01:25,120 | |
| so it's skewed to the right if you look at the | |
| 13 | |
| 00:01:25,120 --> 00:01:32,520 | |
| blue one it's skewed to the left so you have to | |
| 14 | |
| 00:01:32,520 --> 00:01:38,680 | |
| think on three things in skewness if it's skewed | |
| 15 | |
| 00:01:40,320 --> 00:01:45,340 | |
| وإذا ال data is 0 فهذا يعني تحقيقنا لنمو عادي | |
| 16 | |
| 00:01:45,340 --> 00:01:56,400 | |
| إذا ال skew هو موجب وهو أكبر من 0 فال data هو | |
| 17 | |
| 00:01:56,400 --> 00:02:02,380 | |
| skewed إلى اليسار وإذا | |
| 18 | |
| 00:02:02,380 --> 00:02:06,900 | |
| ال skew هو سالب إلى اليسار | |
| 19 | |
| 00:02:08,690 --> 00:02:14,230 | |
| إذا كانت على اليسار، يعني أن المستثمرين يتجنبون | |
| 20 | |
| 00:02:14,230 --> 00:02:20,570 | |
| المخاطر، يتجنبون المخاطر | |
| 21 | |
| 00:02:20,570 --> 00:02:25,090 | |
| إذا كانت تتجنب على اليسار، يعني أن المستثمرين | |
| 22 | |
| 00:02:25,090 --> 00:02:33,250 | |
| يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون | |
| 23 | |
| 00:02:33,250 --> 00:02:33,410 | |
| المخاطر، يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون المخاطر | |
| 24 | |
| 00:02:33,410 --> 00:02:33,530 | |
| يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون | |
| 25 | |
| 00:02:33,530 --> 00:02:33,550 | |
| المخاطر، يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون المخاطر | |
| 26 | |
| 00:02:33,550 --> 00:02:35,750 | |
| يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون المخاطر، يتجنبون | |
| 27 | |
| 00:02:35,750 --> 00:02:41,100 | |
| المخاطر. المشكلة هي عندما تكون الملاحظة مرسومة إلى | |
| 28 | |
| 00:02:41,100 --> 00:02:47,500 | |
| اليسار لأن ما يعنيه .. انظر .. انظر هنا .. فقط .. | |
| 29 | |
| 00:02:47,500 --> 00:02:53,940 | |
| يعني أننا لدينا عدد .. لدينا البيانات مثل هذه .. | |
| 30 | |
| 00:02:53,940 --> 00:02:58,660 | |
| لذلك إذا كنت نتخيل البيانات .. إذا كنا نتخيل عدد | |
| 31 | |
| 00:02:58,660 --> 00:03:03,700 | |
| هذا البيانات .. عددها أو الوسط يجب أن يكون موجود | |
| 32 | |
| 00:03:03,700 --> 00:03:08,720 | |
| في الوسط. Okay should be located in the middle for | |
| 33 | |
| 00:03:08,720 --> 00:03:14,760 | |
| instance take this example here if you have like | |
| 34 | |
| 00:03:14,760 --> 00:03:23,940 | |
| this observation 9% 10% or let's say 12% this is R | |
| 35 | |
| 00:03:23,940 --> 00:03:30,960 | |
| okay إذا أخذنا بيانات ل R لليوم الأول for instance | |
| 36 | |
| 00:03:30,960 --> 00:03:38,560 | |
| لليوم الثاني الثالث الرابع الخامس السادس بيانات R | |
| 37 | |
| 00:03:38,560 --> 00:03:42,500 | |
| اللي هو ال R ال expected return إذا أخذنا ال R أو | |
| 38 | |
| 00:03:42,500 --> 00:03:49,560 | |
| ال daily return أخذنا 12% أو for instance 11% 10% | |
| 39 | |
| 00:03:49,560 --> 00:03:55,930 | |
| 7% 6% if we calculate the average إذا قمنا بحساب | |
| 40 | |
| 00:03:55,930 --> 00:04:03,390 | |
| مجموع هذا العدد أو مجموع العائد هو 12 plus 11 plus 10 | |
| 41 | |
| 00:04:03,390 --> 00:04:11,830 | |
| plus 9 plus 7 plus 6 divided by 1 2 3 4 5 6 مقسومة | |
| 42 | |
| 00:04:11,830 --> 00:04:18,030 | |
| على 6 احسبوها كم تطلع؟ إذا كان لدينا ملاحظات مثل | |
| 43 | |
| 00:04:18,030 --> 00:04:18,330 | |
| هذه؟ | |
| 44 | |
| 00:04:24,020 --> 00:04:29,080 | |
| سرعة القلات دائماً تكون twelve | |
| 45 | |
| 00:04:29,080 --> 00:04:36,540 | |
| percent eleven nine seven and finally six six | |
| 46 | |
| 00:04:36,540 --> 00:04:42,840 | |
| percent nine point one okay so the arithmetic or | |
| 47 | |
| 00:04:42,840 --> 00:04:45,800 | |
| the average is nine point one look at here so nine | |
| 48 | |
| 00:04:45,800 --> 00:04:53,860 | |
| point one is located here or somewhere here. مع ذلك | |
| 49 | |
| 00:04:53,860 --> 00:05:00,220 | |
| يعني أن الملاحظة أو البيانات أو عدد البيانات يكون | |
| 50 | |
| 00:05:00,220 --> 00:05:05,440 | |
| بين 9 و 10 وهو حوالي نصف البيانات أو تقسيم | |
| 51 | |
| 00:05:05,440 --> 00:05:12,040 | |
| البيانات إلى جزئين أساسيين، هذا يكون حوالي جزئين | |
| 52 | |
| 00:05:12,040 --> 00:05:19,000 | |
| كهذا، كما قلت إن هذا الجزء الصحيح هو مظهر اليسار | |
| 53 | |
| 00:05:19,000 --> 00:05:24,100 | |
| الذي يعني أن البيانات تتبع التوزيع الطبيعي لكن | |
| 54 | |
| 00:05:24,100 --> 00:05:30,540 | |
| المشكلة هي إذا كان لدينا قيم أعظم إذا كان لدينا | |
| 55 | |
| 00:05:30,540 --> 00:05:34,960 | |
| قيم أعظم أو ما يسمونه الـ outliers إذا كان لدينا | |
| 56 | |
| 00:05:34,960 --> 00:05:38,720 | |
| outliers على سبيل المثال دعونا نضيف شيء إلى هذه | |
| 57 | |
| 00:05:38,720 --> 00:05:45,150 | |
| البيانات. إذا كان لدينا الخطر هو مثلًا يوم واحد نقوم | |
| 58 | |
| 00:05:45,150 --> 00:05:57,250 | |
| بتسجيل حوالي 400% و 300% ما نشاهده في هذا البرنامج | |
| 59 | |
| 00:05:57,250 --> 00:05:59,830 | |
| إذا قمنا بتسجيل هذه المعلومات لدينا هذه المعلومات | |
| 60 | |
| 00:05:59,830 --> 00:06:05,030 | |
| الآن لدينا هذه المعلومات الآن و يبدو مثلًا هذا | |
| 61 | |
| 00:06:05,030 --> 00:06:10,910 | |
| المعلومات يبدو مثلًا هذا. هذا الاختلاف الكبير بين | |
| 62 | |
| 00:06:10,910 --> 00:06:13,550 | |
| الاثنين الملاحظات والمقالات المختلفة من الملاحظات | |
| 63 | |
| 00:06:13,550 --> 00:06:19,530 | |
| هذا يسمى قيم متطرفة أو أقل قيم أخرى أو أقل قيم | |
| 64 | |
| 00:06:19,530 --> 00:06:27,030 | |
| أخرى نسميهم قيم متطرفة أو أقل قيم أخرى نسميهم قيم | |
| 65 | |
| 00:06:27,030 --> 00:06:27,270 | |
| متطرفة نسميهم قيم متطرفة نسميهم قيم متطرفة نسميهم قيم | |
| 66 | |
| 00:06:27,270 --> 00:06:27,390 | |
| متطرفة نسميهم قيم متطرفة نسميهم قيم متطرفة نسميهم قيم | |
| 67 | |
| 00:06:27,390 --> 00:06:27,850 | |
| متطرفة نسميهم قيم متطرفة نسميهم قيم متطرفة نسميهم قيم | |
| 68 | |
| 00:06:27,850 --> 00:06:29,840 | |
| متطرفة نسميهم قيم متطرفة نسميهم. نعيدوا احتساب ال | |
| 69 | |
| 00:06:29,840 --> 00:06:35,300 | |
| average. هنعيد احتساب ال average 400 plus 300 plus | |
| 70 | |
| 00:06:35,300 --> 00:06:41,580 | |
| 12 plus 11 plus 10 plus 9 plus 7 plus 6 divided by | |
| 71 | |
| 00:06:41,580 --> 00:06:47,520 | |
| 8 احسبوا ال average. ال average will be in some | |
| 72 | |
| 00:06:47,520 --> 00:06:55,880 | |
| area in here. ال average هيكون في .. احسبوا ال | |
| 73 | |
| 00:06:55,880 --> 00:06:56,360 | |
| average now | |
| 74 | |
| 00:07:01,030 --> 00:07:12,910 | |
| كم طلع؟ 94.3 you see so it is 94.3 so now the | |
| 75 | |
| 00:07:12,910 --> 00:07:16,750 | |
| average now the average what's what's happened | |
| 76 | |
| 00:07:16,750 --> 00:07:23,570 | |
| with the data؟ ايش اللي صار في البيانات؟ because yes | |
| 77 | |
| 00:07:23,570 --> 00:07:27,570 | |
| فينا بيانات شاذة فالبيانات الشاذة عملت pulling up | |
| 78 | |
| 00:07:28,540 --> 00:07:31,380 | |
| pulling the data to the top or pulling the average | |
| 79 | |
| 00:07:31,380 --> 00:07:35,580 | |
| to the top يعني هلأ صار ال average is pulling to | |
| 80 | |
| 00:07:35,580 --> 00:07:39,700 | |
| the top okay | |
| 81 | |
| 00:07:39,700 --> 00:07:46,160 | |
| صار في تحيز or there is a bias. صار عندي ايه؟ bias | |
| 82 | |
| 00:07:46,160 --> 00:07:51,140 | |
| in this case the positive look at here the | |
| 83 | |
| 00:07:51,140 --> 00:07:57,740 | |
| positive is greater than the negative. القيم المتطرفة | |
| 84 | |
| 00:07:57,740 --> 00:08:01,000 | |
| الموجبة أكثر من القيم المتطرفة السالبة we | |
| 85 | |
| 00:08:01,000 --> 00:08:04,880 | |
| don't have negative outliers here. فاللي صار أنه | |
| 86 | |
| 00:08:04,880 --> 00:08:08,640 | |
| صار عندي التواء لليمين هيكون الشكل تبعوا للشكل | |
| 87 | |
| 00:08:08,640 --> 00:08:16,140 | |
| هيكون الشكل هيك هيكون في to the right to the right | |
| 88 | |
| 00:08:16,140 --> 00:08:18,800 | |
| why to the right because we have extreme values | |
| 89 | |
| 00:08:18,800 --> 00:08:24,020 | |
| فال average ال average is move to the right. ال | |
| 90 | |
| 00:08:24,020 --> 00:08:27,130 | |
| average هيروح على ال right لأن هنا فينا الاربعماية | |
| 91 | |
| 00:08:27,130 --> 00:08:34,310 | |
| والتلاتماية هم outliers so the outliers try to move | |
| 92 | |
| 00:08:34,310 --> 00:08:38,550 | |
| the average to the right side هياخد ال average لل | |
| 93 | |
| 00:08:38,550 --> 00:08:44,010 | |
| right side okay this | |
| 94 | |
| 00:08:44,010 --> 00:08:51,250 | |
| is why .. this is why we have positive skew and | |
| 95 | |
| 00:08:51,250 --> 00:08:53,980 | |
| the opposite if we take another example here. إذا | |
| 96 | |
| 00:08:53,980 --> 00:09:00,120 | |
| أخذنا نفس المثال و | |
| 97 | |
| 00:09:00,120 --> 00:09:04,320 | |
| خلينا القيام like this شوفوا القيام like this | |
| 98 | |
| 00:09:04,320 --> 00:09:10,600 | |
| they say twelve percent eleven ten nine seven six | |
| 99 | |
| 00:09:10,600 --> 00:09:21,940 | |
| and we have here like point five and minus okay | |
| 100 | |
| 00:09:23,590 --> 00:09:31,290 | |
| minus fifteen and minus thirty. أخذنا القيام هدول | |
| 101 | |
| 00:09:31,290 --> 00:09:38,370 | |
| فشوفوا عكس الحالة هذه بيكون ال data like this. ال | |
| 102 | |
| 00:09:38,370 --> 00:09:44,810 | |
| average like this then it's like this. طب ال | |
| 103 | |
| 00:09:44,810 --> 00:09:49,390 | |
| outliers وين تحت ولا فوق؟ تحت. so it's negative لما | |
| 104 | |
| 00:09:49,390 --> 00:09:54,020 | |
| بيكون the outliers it means if the average is here | |
| 105 | |
| 00:09:54,020 --> 00:10:01,400 | |
| so the outliers try to push it down. bowling يعني | |
| 106 | |
| 00:10:01,400 --> 00:10:06,160 | |
| يسحب bowling up pushing down فهيصير ال average | |
| 107 | |
| 00:10:06,160 --> 00:10:08,840 | |
| somewhere هنا احسبوا الكلام ده شوفوا واحد و اتنين | |
| 108 | |
| 00:10:08,840 --> 00:10:17,780 | |
| طيب يعني هتكون بمكان مهم مظبوط. the average should | |
| 109 | |
| 00:10:17,780 --> 00:10:21,780 | |
| be somewhere in here but the average is moved down | |
| 110 | |
| 00:10:21,780 --> 00:10:27,500 | |
| because the data is skewed to the left. حيكون شكل | |
| 111 | |
| 00:10:27,500 --> 00:10:39,100 | |
| ال .. شكل ال .. شكله هيك تقريباً to | |
| 112 | |
| 00:10:39,100 --> 00:10:42,360 | |
| the left. فبكون هدول ال outliers minus fifteen | |
| 113 | |
| 00:10:42,360 --> 00:10:49,810 | |
| minus thirty is located somewhere in here. Okay, so | |
| 114 | |
| 00:10:49,810 --> 00:10:54,890 | |
| because there is no symmetry with the data, | |
| 115 | |
| 00:10:55,090 --> 00:10:58,430 | |
| generally speaking, most people in statistics they | |
| 116 | |
| 00:10:58,430 --> 00:11:02,690 | |
| ignore these things, they ignore this, الناس كلهم | |
| 117 | |
| 00:11:02,690 --> 00:11:07,070 | |
| بيتجاهلوهم، يعني بيتجاهلوهم، but in finance we | |
| 118 | |
| 00:11:07,070 --> 00:11:11,350 | |
| should consider them. In the first case look at | |
| 119 | |
| 00:11:11,350 --> 00:11:18,000 | |
| here, in this one when the .. when the data .. when | |
| 120 | |
| 00:11:18,000 --> 00:11:23,180 | |
| the data is positive when we have outliers greater | |
| 121 | |
| 00:11:23,180 --> 00:11:27,900 | |
| than the average it means we have a positive skew | |
| 122 | |
| 00:11:27,900 --> 00:11:32,100 | |
| but here we have negative skew and because we have | |
| 123 | |
| 00:11:32,100 --> 00:11:37,680 | |
| positive skew it means لأنه إذا كان عندنا skew ما | |
| 124 | |
| 00:11:37,680 --> 00:11:41,200 | |
| أنت عارف .. هيعرفنا ال skew يعني هالتواء صح؟ إذا | |
| 125 | |
| 00:11:41,200 --> 00:11:47,120 | |
| كانت موجب positive بكون عندي over estimate و إذا | |
| 126 | |
| 00:11:47,120 --> 00:11:54,220 | |
| كانت negative under estimate طيب this is the | |
| 127 | |
| 00:11:54,220 --> 00:11:58,940 | |
| importance of skew هذا أهمية الـ skew نيجي نحكي عن | |
| 128 | |
| 00:11:58,940 --> 00:12:06,280 | |
| الـ cortices على الـ cortices خلينا نذكركم بس | |
| 129 | |
| 00:12:06,280 --> 00:12:11,220 | |
| بالقانون تبع الـ skew how to calculate the skew بس | |
| 130 | |
| 00:12:11,220 --> 00:12:20,580 | |
| يعني القانون بتعرفوا أنه Q is equal R minus R bar | |
| 131 | |
| 00:12:20,580 --> 00:12:29,160 | |
| okay cubed divided by sigma cubed هذا هو الـ raise | |
| 132 | |
| 00:12:29,160 --> 00:12:34,460 | |
| to the power three الكورتوسيز ايش بيقيس الكورتوسيز | |
| 133 | |
| 00:12:34,460 --> 00:12:38,500 | |
| الكورتوسيز is measure to what extent our data is | |
| 134 | |
| 00:12:38,500 --> 00:12:43,030 | |
| flat يعني الـ I درجة بيكون الـ بيانات تبعتنا flat | |
| 135 | |
| 00:12:43,030 --> 00:12:55,930 | |
| ناخد نتالي لو | |
| 136 | |
| 00:12:55,930 --> 00:13:03,270 | |
| شوفنا احنا هذا | |
| 137 | |
| 00:13:03,270 --> 00:13:06,430 | |
| ايش رأيكوا؟ هذا normal distribution ولا ايش؟ this | |
| 138 | |
| 00:13:06,430 --> 00:13:07,370 | |
| is normal distribution | |
| 139 | |
| 00:13:11,120 --> 00:13:18,920 | |
| هذا normal distribution توزيع طبيعي هذا | |
| 140 | |
| 00:13:18,920 --> 00:13:24,980 | |
| فيه توزيع طبيعي why because the right side is | |
| 141 | |
| 00:13:24,980 --> 00:13:27,840 | |
| approximately equal to the left side يعني الجانب | |
| 142 | |
| 00:13:27,840 --> 00:13:34,160 | |
| اليمين تقريبا يشبه الجانب الشمال okay طيب so the | |
| 143 | |
| 00:13:34,160 --> 00:13:41,080 | |
| thing is now the thing is the thing is now إذا ننظر | |
| 144 | |
| 00:13:41,080 --> 00:13:44,980 | |
| إلى الجانب اليسار هو تقريبًا يقل الجانب اليسار، | |
| 145 | |
| 00:13:44,980 --> 00:13:50,140 | |
| إذا كان لدينا كورتوسيه، يعني أن البيانات أكتر | |
| 146 | |
| 00:13:50,140 --> 00:13:54,500 | |
| مطمئنة من المشاركة الطبيعية، يبدو هكذا | |
| 147 | |
| 00:14:14,330 --> 00:14:17,730 | |
| So the data .. this is .. this one this means we | |
| 148 | |
| 00:14:17,730 --> 00:14:20,490 | |
| have a very narrow mean .. mean and the data is | |
| 149 | |
| 00:14:20,490 --> 00:14:26,830 | |
| flat is scattered in the left and scattered in the | |
| 150 | |
| 00:14:26,830 --> 00:14:29,390 | |
| .. in the right or in the right and in the left if | |
| 151 | |
| 00:14:29,390 --> 00:14:35,750 | |
| you see here there is a space between this line | |
| 152 | |
| 00:14:35,750 --> 00:14:39,030 | |
| with this line but with this one there is .. there | |
| 153 | |
| 00:14:39,030 --> 00:14:43,430 | |
| is a limit يعني إذا إحنا بناحي البيانات موجودة هون | |
| 154 | |
| 00:14:46,170 --> 00:14:53,130 | |
| هنجيب بالـ Cortices أن | |
| 155 | |
| 00:14:53,130 --> 00:14:57,050 | |
| البيانات تأخذ بعض المكان هنا و بعض المكان هنا | |
| 156 | |
| 00:14:57,050 --> 00:15:04,630 | |
| هنلاقي بيانات فوق و لاتحت و في الوسط كيف نحسب الـ | |
| 157 | |
| 00:15:04,630 --> 00:15:08,530 | |
| Cortices كيف احنا بنحسب الـ Cortices The Cortices | |
| 158 | |
| 00:15:08,530 --> 00:15:20,920 | |
| is equal to R minus R bar raise to the power four raise | |
| 159 | |
| 00:15:20,920 --> 00:15:21,760 | |
| to the power four raise to the power four raise to the power | |
| 160 | |
| 00:15:21,760 --> 00:15:22,760 | |
| four raise to the power four raise to the power four | |
| 161 | |
| 00:15:22,760 --> 00:15:29,180 | |
| four raise to the power four raise to the power four raise to | |
| 162 | |
| 00:15:29,180 --> 00:15:32,180 | |
| the power four raise to the power four raise to the power four raise | |
| 163 | |
| 00:15:32,180 --> 00:15:33,860 | |
| to the power four raise to the power four raise to the power | |
| 164 | |
| 00:15:33,860 --> 00:15:41,320 | |
| four raise to the power four raise | |
| 165 | |
| 00:15:41,320 --> 00:15:46,930 | |
| لحظة يا تيه إذا قمنا بالتخيل هذا الجانب بشكل | |
| 166 | |
| 00:15:46,930 --> 00:15:51,230 | |
| مختلف و إذا كانت البيانات تتبع اتجارة عادية فهذا | |
| 167 | |
| 00:15:51,230 --> 00:15:55,790 | |
| يجب أن يكون ثلاثة إذا كانت النتيجة ثلاثة ثلاثة أقل | |
| 168 | |
| 00:15:55,790 --> 00:16:00,010 | |
| ثلاثة ثم ننتهي بزيرولذلك إذا كانت النتيجة صحيحة | |
| 169 | |
| 00:16:00,010 --> 00:16:06,870 | |
| إذا كانت النتيجة صحيحة إذا كانت النتيجة صحيحة إذا | |
| 170 | |
| 00:16:06,870 --> 00:16:07,170 | |
| كانت النتيجة صحيحة إذا كانت النتيجة صحيحة إذا كانت | |
| 171 | |
| 00:16:07,170 --> 00:16:08,990 | |
| النتيجة صحيحة إذا كانت النتيجة صحيحة إذا كانت | |
| 172 | |
| 00:16:08,990 --> 00:16:09,610 | |
| النتيجة صحيحة إذا كانت النتيجة صحيحة إذا كانت | |
| 173 | |
| 00:16:09,610 --> 00:16:11,190 | |
| النتيجة صحيحة إذا كانت النتيجة صحيحة إذا كانت | |
| 174 | |
| 00:16:11,190 --> 00:16:13,210 | |
| النتيجة صحيحة إذا كانت النتيجة صحيحة إذا كانت | |
| 175 | |
| 00:16:13,210 --> 00:16:18,530 | |
| النتيجة صحيحة إذا كانت النتيجة صحيحة إذا كانت | |
| 176 | |
| 00:16:18,530 --> 00:16:23,510 | |
| النتيجة صحيحة إذا | |
| 177 | |
| 00:16:23,510 --> 00:16:27,330 | |
| كانت | |
| 178 | |
| 00:16:27,330 --> 00:16:30,840 | |
| النتيجة صحيحة هي تطلع على المعادلة المعادلة فيها | |
| 179 | |
| 00:16:30,840 --> 00:16:34,500 | |
| إلها شقين هذه ناقص تلاتة إذا طلع هذا الجواب تلاتة | |
| 180 | |
| 00:16:34,500 --> 00:16:38,280 | |
| تلاتة ناقص تلاتة so it's equal zero إذا كان zero | |
| 181 | |
| 00:16:38,280 --> 00:16:43,440 | |
| بيكون هذا شوف هذه بيكون zero لأن هاي touch the | |
| 182 | |
| 00:16:43,440 --> 00:16:47,120 | |
| line and this is touch the line but if it is | |
| 183 | |
| 00:16:47,120 --> 00:16:51,400 | |
| greater than three شوف إذا كان هذا جوابي greater | |
| 184 | |
| 00:16:51,400 --> 00:16:55,440 | |
| than three then it's greater than zero so we have | |
| 185 | |
| 00:16:55,440 --> 00:16:59,070 | |
| cortices Okay, so this is the problem. | |
| 186 | |
| 00:17:02,490 --> 00:17:10,370 | |
| Now, again the Skew and Cortices help the | |
| 187 | |
| 00:17:10,370 --> 00:17:12,690 | |
| researcher and help financial people or investors | |
| 188 | |
| 00:17:12,690 --> 00:17:17,470 | |
| to mention the data are normally distributed or | |
| 189 | |
| 00:17:17,470 --> 00:17:22,230 | |
| not. إذا كانت البيانات تبعتهم موزعة توزيع طبيعي | |
| 190 | |
| 00:17:22,230 --> 00:17:23,090 | |
| ولا لأ؟ | |
| 191 | |
| 00:17:27,160 --> 00:17:30,700 | |
| الـ cortices بيبنوا like this طلع البيانات scattered | |
| 192 | |
| 00:17:30,700 --> 00:17:34,140 | |
| بيكون في outliers in the top and outliers in the | |
| 193 | |
| 00:17:34,140 --> 00:17:39,280 | |
| bottom and we have something in the middle فبتكون | |
| 194 | |
| 00:17:39,280 --> 00:17:43,700 | |
| في زي πاي باي observation أو binomial بتكونش | |
| 195 | |
| 00:17:43,700 --> 00:17:46,600 | |
| البيانات is focused on the average يعني زي ما انت | |
| 196 | |
| 00:17:46,600 --> 00:17:51,740 | |
| شايفها هان in this one red one the most of our | |
| 197 | |
| 00:17:51,740 --> 00:17:57,200 | |
| data look at here most of our data موجودة في مكان | |
| 198 | |
| 00:17:57,200 --> 00:18:03,100 | |
| ما هنا وهو حوالي 68% من البيانات الموجودة هنا ولكن | |
| 199 | |
| 00:18:03,100 --> 00:18:09,320 | |
| إذا كنت تنظر إلى الـ blue one حوالي 30% من | |
| 200 | |
| 00:18:09,320 --> 00:18:12,800 | |
| بياناتنا موجودة في الأعلى أو موجودة في الـ .. | |
| 201 | |
| 00:18:12,800 --> 00:18:18,700 | |
| والباقية من بياناتنا موجودة في الخارج ممكننا أن | |
| 202 | |
| 00:18:18,700 --> 00:18:23,600 | |
| نلاحظ مثل هذا إذا أردنا لدينا بيانات مثل هذه و | |
| 203 | |
| 00:18:23,600 --> 00:18:28,520 | |
| لدينا بيانات مثل هذه و لدينا مصادر مثل هذه حسنا اذا | |
| 204 | |
| 00:18:28,520 --> 00:18:36,000 | |
| ماهي عاملة؟ عاملة مخططة لهم لأن حسنا ربما العاملة | |
| 205 | |
| 00:18:36,000 --> 00:18:43,300 | |
| في هنا لذا لدينا شيء هنا و لدينا شيء هنا لذلك إذا | |
| 206 | |
| 00:18:43,300 --> 00:18:49,600 | |
| قمنا بترتيب هذا في مقالة ننتهي بمقالة بلو Okay, | |
| 207 | |
| 00:18:50,140 --> 00:18:53,060 | |
| this is .. it's like this has two wings, two big | |
| 208 | |
| 00:18:53,060 --> 00:18:57,460 | |
| wings يعني هناخد for instance look at here two big | |
| 209 | |
| 00:18:57,460 --> 00:19:02,220 | |
| wings الناس | |
| 210 | |
| 00:19:02,220 --> 00:19:05,720 | |
| .. بعض الناس مش كتير في الـ statistics so بتاع | |
| 211 | |
| 00:19:05,720 --> 00:19:10,020 | |
| they're ignoring the outlines فاحنا بالنسبة لل | |
| 212 | |
| 00:19:10,020 --> 00:19:14,540 | |
| finance outlines are important why outlines are | |
| 213 | |
| 00:19:14,540 --> 00:19:16,820 | |
| important because indicate something in finance | |
| 214 | |
| 00:19:17,510 --> 00:19:19,830 | |
| الناس تتعامل بـ Overestimation عندما تكون لديها | |
| 215 | |
| 00:19:19,830 --> 00:19:23,310 | |
| إتجار مفيد والناس تتعامل بـ Underestimate Risk | |
| 216 | |
| 00:19:23,310 --> 00:19:30,470 | |
| عندما تكون لديها إتجار مفيد حسنا | |
| 217 | |
| 00:19:30,470 --> 00:19:34,270 | |
| الآن | |
| 218 | |
| 00:19:34,270 --> 00:19:42,370 | |
| دعونا نتحدث عن إتجار مفقود وهو مهم أيضا في | |
| 219 | |
| 00:19:42,370 --> 00:19:50,930 | |
| الوزيارة لأن of this look at here as I said we have | |
| 220 | |
| 00:19:50,930 --> 00:19:55,530 | |
| data like this this is normal and then we have | |
| 221 | |
| 00:19:55,530 --> 00:20:01,050 | |
| outliers like this إذا احنا أخدنا observations R | |
| 222 | |
| 00:20:01,050 --> 00:20:05,530 | |
| زي هيك مع الكلام هدول الـ average بيكون somewhere | |
| 223 | |
| 00:20:05,530 --> 00:20:09,050 | |
| هنا because of this outliers maybe the average | |
| 224 | |
| 00:20:09,050 --> 00:20:14,230 | |
| will go down هيكون جريب لهدول Okay so what the | |
| 225 | |
| 00:20:14,230 --> 00:20:18,390 | |
| problem then is this positive skew or negative | |
| 226 | |
| 00:20:18,390 --> 00:20:23,070 | |
| okay this positive or negative why it is negative | |
| 227 | |
| 00:20:23,070 --> 00:20:27,070 | |
| because لأنه هيسحبوه من التحت okay so we have | |
| 228 | |
| 00:20:27,070 --> 00:20:33,150 | |
| negative and if we draw the negative so | |
| 229 | |
| 00:20:33,150 --> 00:20:37,230 | |
| this is this is a negative skew to the right to | |
| 230 | |
| 00:20:37,230 --> 00:20:44,670 | |
| the left وإذا قمت بإرسالهم هنا، فسنلاحظ أن البيانات | |
| 231 | |
| 00:20:44,670 --> 00:20:53,410 | |
| هنا ونلاحظ ما يوجد هنا، الـ outliers، حسنًا؟ | |
| 232 | |
| 00:20:53,410 --> 00:20:59,790 | |
| الآن هذه الـ outliers، لأنها في الجانات المفارقة، | |
| 233 | |
| 00:20:59,790 --> 00:21:06,850 | |
| نسميها قيمة في خطر be careful we write it values at | |
| 234 | |
| 00:21:06,850 --> 00:21:10,650 | |
| risk we are not writing like this this is var | |
| 235 | |
| 00:21:10,650 --> 00:21:13,950 | |
| which is variance and this is values at risk | |
| 236 | |
| 00:21:13,950 --> 00:21:19,930 | |
| values at risk what it means values at risk values | |
| 237 | |
| 00:21:19,930 --> 00:21:26,370 | |
| at risk it means قيم معرضة للخطر بالظبط قيم معرضة | |
| 238 | |
| 00:21:26,370 --> 00:21:31,990 | |
| للخطر يعني لو جينا احنا رصدنا درجات الطلاب لا يا | |
| 239 | |
| 00:21:31,990 --> 00:21:35,010 | |
| بابا مش الـ variance احنا حكينا look be careful | |
| 240 | |
| 00:21:35,010 --> 00:21:38,150 | |
| this is not not variance هذا مش whole variance | |
| 241 | |
| 00:21:38,150 --> 00:21:44,970 | |
| this is values at risk زي ما حكت انه قيم معرضة | |
| 242 | |
| 00:21:44,970 --> 00:21:53,570 | |
| للخطر values at risk values at risk قيم معرضة | |
| 243 | |
| 00:21:53,570 --> 00:21:58,250 | |
| للخطر طيب هلا مثلا أجينا احنا أخدنا درجات الطلاب | |
| 244 | |
| 00:21:59,240 --> 00:22:04,140 | |
| لجينا الطلاب في تسعين تمانين خمسين سبعين ستين ف الـ | |
| 245 | |
| 00:22:04,140 --> 00:22:07,980 | |
| values at risk هي الـ values الـ extreme negative | |
| 246 | |
| 00:22:07,980 --> 00:22:14,900 | |
| يعني أخدنا درجة الطلاب لجينا فينا تسعين خمس و | |
| 247 | |
| 00:22:14,900 --> 00:22:21,460 | |
| تسعين تمانين خمس و تمانين سبعين تسعة و ستين سبعين | |
| 248 | |
| 00:22:21,460 --> 00:22:25,500 | |
| خمس و تمانين okay و بعدين لجينا عشرين عشرة خمس و | |
| 249 | |
| 00:22:25,500 --> 00:22:30,760 | |
| أستعشسجلنا درجة الطلاب and we found like this | |
| 250 | |
| 00:22:30,760 --> 00:22:34,000 | |
| لقينا درجات الطلاب where is the values at risk | |
| 251 | |
| 00:22:34,000 --> 00:22:39,940 | |
| هدول هما الـ 20, 10, 15 هدول values at risk هدول | |
| 252 | |
| 00:22:39,940 --> 00:22:44,260 | |
| values at risk will move the will move the average | |
| 253 | |
| 00:22:44,260 --> 00:22:49,160 | |
| down وبالتالي الـ average هيصير misleading the | |
| 254 | |
| 00:22:49,160 --> 00:22:52,460 | |
| problem is now from the investment point of view | |
| 255 | |
| 00:22:52,460 --> 00:22:56,650 | |
| من وجهة نظر المستثمرين to what extent these people | |
| 256 | |
| 00:22:56,650 --> 00:23:02,610 | |
| are at risk؟ لأي درجة أن هدول الـ people في خطر؟ | |
| 257 | |
| 00:23:02,610 --> 00:23:07,010 | |
| لأي درجة هدول الطلاب عندهم .. okay let's things in | |
| 258 | |
| 00:23:07,010 --> 00:23:13,870 | |
| different ways values at risk measures worst loss | |
| 259 | |
| 00:23:13,870 --> 00:23:21,570 | |
| أسوأ خسارة يعني بنيجي و بنقول احنا ما هي أسوأ | |
| 260 | |
| 00:23:21,570 --> 00:23:28,470 | |
| خسارة ممكن نحصل عليها بالفصل أسوأ نتيجة يعني | |
| 261 | |
| 00:23:28,470 --> 00:23:35,750 | |
| لأ يعني كم طالب يرسب بنيجي نقول أسوأ نتيجة ممكن | |
| 262 | |
| 00:23:35,750 --> 00:23:42,350 | |
| نحصل عليها يعني جداش أن عدد طلاب مثلا ستين بنقول | |
| 263 | |
| 00:23:42,350 --> 00:23:46,650 | |
| احنا حسب الحسابات تبعنا أسوأ نتيجة ممكن نحصل عليها | |
| 264 | |
| 00:23:46,650 --> 00:23:53,050 | |
| أنه يرسب ثلاثة في المئة أو بطريقة ثانية أسوأ نتيجة | |
| 265 | |
| 00:23:54,240 --> 00:24:00,240 | |
| نحصل عليها أنه ما تزدش الخسارة بتاعتنا عن 3% this | |
| 266 | |
| 00:24:00,240 --> 00:24:05,220 | |
| is fine or in other words أو بطريقة أخرى نقول أسوأ | |
| 267 | |
| 00:24:05,220 --> 00:24:11,160 | |
| نتيجة أنه احنا نحصل عليها أنه النجاح يكون أقل من | |
| 268 | |
| 00:24:11,160 --> 00:24:17,640 | |
| 97% النجاح يكون أقل من 97% نفس الـ 3% نفس الفكرة | |
| 269 | |
| 00:24:17,640 --> 00:24:24,360 | |
| يعني احنا قلنا أو النجاح ما يزدش عن 97% فبكون لما | |
| 270 | |
| 00:24:24,360 --> 00:24:28,960 | |
| أقول النجاح ما يزدش عن 97% it means أن أسوأ خسارة | |
| 271 | |
| 00:24:28,960 --> 00:24:33,400 | |
| ممكن نحصل عليها 3% from investment point of view | |
| 272 | |
| 00:24:33,400 --> 00:24:38,580 | |
| ممكن من وجهة نظر الاستثمار okay what is the worst | |
| 273 | |
| 00:24:38,580 --> 00:24:45,740 | |
| loss ما هي أسوأ خسارة ممكن نحصل عليها so we need | |
| 274 | |
| 00:24:45,740 --> 00:24:49,060 | |
| to calculate values at risk عشان نحصل على أسوأ | |
| 275 | |
| 00:24:49,060 --> 00:24:52,700 | |
| خسارة there are three methods to calculate values | |
| 276 | |
| 00:24:52,700 --> 00:24:58,130 | |
| at risk in your handbook is only one method في | |
| 277 | |
| 00:24:58,130 --> 00:25:03,870 | |
| الكتاب تبعك موجود بس methods واحدة okay and this | |
| 278 | |
| 00:25:03,870 --> 00:25:06,490 | |
| method is called Monte Carlo method مش موجودة | |
| 279 | |
| 00:25:06,490 --> 00:25:09,630 | |
| بالكتاب أن اسمها Monte Carlo لكن أنا بقولكوا إياها | |
| 280 | |
| 00:25:09,630 --> 00:25:13,530 | |
| it is Monte Carlo فممكن بالـ corrections و لا بالصح | |
| 281 | |
| 00:25:13,530 --> 00:25:15,490 | |
| و الغلط تقولوا والله يا عزيزي مش موجودة بالكتاب no | |
| 282 | |
| 00:25:15,490 --> 00:25:19,370 | |
| I'm telling you now this method is Monte Carlo | |
| 283 | |
| 00:25:23,640 --> 00:25:29,100 | |
| اسم الطريقة اسمها Monte Carlo okay في Monte Carlo | |
| 284 | |
| 00:25:29,100 --> 00:25:32,580 | |
| في إذاعة اسمها Monte Carlo في دراسة اسمها Monte | |
| 285 | |
| 00:25:32,580 --> 00:25:39,820 | |
| Carlo so the normal so the values at risk is equal | |
| 286 | |
| 00:25:39,820 --> 00:25:52,890 | |
| to mu which is the average minus z times sigma و | |
| 287 | |
| 00:25:52,890 --> 00:25:58,530 | |
| سنشرح ماذا يعني Z يعني | |
| 288 | |
| 00:25:58,530 --> 00:26:02,950 | |
| ميو | |
| 289 | |
| 00:26:02,950 --> 00:26:11,090 | |
| أو عامل مانوس سيجما زد مانوس زد يعني عامل عامل | |
| 290 | |
| 00:26:11,090 --> 00:26:15,150 | |
| عامل عامل عامل عامل عامل عامل عامل عامل عامل عامل | |
| 291 | |
| 00:26:15,150 --> 00:26:16,530 | |
| عامل عامل عامل عامل عامل عامل عامل عامل عامل عامل | |
| 292 | |
| 00:26:16,530 --> 00:26:17,790 | |
| عامل عامل عامل عامل عامل عامل عامل عامل عامل عامل | |
| 293 | |
| 00:26:17,790 --> 00:26:17,810 | |
| عامل عامل عامل عامل عامل عامل عامل عامل عامل عامل | |
| 294 | |
| 00:26:21,460 --> 00:26:24,720 | |
| the critical value اللي هو القيمة الحرجة بتسميها | |
| 295 | |
| 00:26:24,720 --> 00:26:30,440 | |
| okay what it means القيمة الحرجة فاكرين القيمة | |
| 296 | |
| 00:26:30,440 --> 00:26:38,560 | |
| الحرجة at a particular confidence | |
| 297 | |
| 00:26:38,560 --> 00:26:43,960 | |
| level عند | |
| 298 | |
| 00:26:43,960 --> 00:26:48,600 | |
| مستوى معنوية أو مستوى ثقة معين خلّيني أجي نقول | |
| 299 | |
| 00:26:49,520 --> 00:26:54,560 | |
| تطلعوا على التلات مقالات الموجودين هنا لنفترض أن | |
| 300 | |
| 00:26:54,560 --> 00:26:59,960 | |
| هدول بمثله minus 30% و minus 20% و minus .. خلّيني | |
| 301 | |
| 00:26:59,960 --> 00:27:04,140 | |
| minus 30% و minus 20% هدول النقطتين الموجودين هنا | |
| 302 | |
| 00:27:04,140 --> 00:27:10,760 | |
| اللي هم الـ extreme negative values okay هلأ بنحكي | |
| 303 | |
| 00:27:10,760 --> 00:27:15,900 | |
| what is .. what is the worst loss | |
| 304 | |
| 00:27:19,890 --> 00:27:28,490 | |
| 95% ما هي أسوأ خسارة ممكن نحصل عليها عند 95% then | |
| 305 | |
| 00:27:28,490 --> 00:27:36,010 | |
| we apply this هنطبق هذه الـ average معروفة والـ sigma | |
| 306 | |
| 00:27:36,010 --> 00:27:39,910 | |
| معروفة الـ standard deviation معروف بيضل الـ z ايش | |
| 307 | |
| 00:27:39,910 --> 00:27:46,730 | |
| الـ z هذه الـ z عند 95% اللي هي المنطقة هذه عند 95% | |
| 308 | |
| 00:27:46,730 --> 00:27:53,260 | |
| بتساوي 1.65 أخدتها بالـ .. بتنجح بين الجدول اللي هو | |
| 309 | |
| 00:27:53,260 --> 00:27:55,980 | |
| بالـ .. اللي أخدتها من الإحسان اه one point six | |
| 310 | |
| 00:27:55,980 --> 00:28:00,980 | |
| five فبصير احنا الـ MUE minus one point six five | |
| 311 | |
| 00:28:00,980 --> 00:28:07,480 | |
| times sigma نفترض الجواب طلع لنا minus twenty | |
| 312 | |
| 00:28:07,480 --> 00:28:11,700 | |
| percent ايش معناه what it means ايش معناه ما عرفت | |
| 313 | |
| 00:28:11,700 --> 00:28:16,040 | |
| عشان عشانين تمية أسوأ خسارة ممكن احنا نحصل عليها | |
| 314 | |
| 00:28:16,040 --> 00:28:22,630 | |
| من الاستثمار في A ما بتزيد عن minus 20% in other | |
| 315 | |
| 00:28:22,630 --> 00:28:26,470 | |
| words the worst loss that we can take when we | |
| 316 | |
| 00:28:26,470 --> 00:28:32,990 | |
| invest in A is not greater than 20% or minus 20% | |
| 317 | |
| 00:28:32,990 --> 00:28:43,430 | |
| أو بطريقة أخرى أنه we are hundred percent sure or | |
| 318 | |
| 00:28:43,430 --> 00:28:50,040 | |
| five percent يعني احنا حكينا عن 95% هيك 95% وها 5% | |
| 319 | |
| 00:28:50,040 --> 00:28:57,100 | |
| بنسبة 5% احنا بنكون متأكدين أن البيانات الخسائر | |
| 320 | |
| 00:28:57,100 --> 00:29:06,720 | |
| تبعتنا مش هتزيد عن .. مش هتزيد عن 20% okay هذه إذا | |
| 321 | |
| 00:29:06,720 --> 00:29:13,040 | |
| كانت negative values طيب | |
| 322 | |
| 00:29:15,120 --> 00:29:18,240 | |
| إذا الـ values الـ risk بتقيس لإيه؟ أهم إيش تعرفوا | |
| 323 | |
| 00:29:18,240 --> 00:29:23,060 | |
| هذا الـ loss loss أسوأ خسارة ممكن احنا نحصل عليها | |
| 324 | |
| 00:29:23,060 --> 00:29:26,780 | |
| and we compare زي ما شوفنا الـ loss loss we compare | |
| 325 | |
| 00:29:26,780 --> 00:29:32,300 | |
| the average values with the negative values | |
| 326 | |
| 00:29:43,920 --> 00:29:46,320 | |
| So the values at risk just to remind you with the | |
| 327 | |
| 00:29:46,320 --> 00:29:50,020 | |
| values at risk a measure of loss most frequently | |
| 328 | |
| 00:29:50,020 --> 00:29:51,960 | |
| associated with the extreme negative returns | |
| 329 | |
| 00:29:51,960 --> 00:29:55,640 | |
| العلاج بالـ extreme negative returns be careful is | |
| 330 | |
| 00:29:55,640 --> 00:30:00,300 | |
| not related to the positive return is related to | |
| 331 | |
| 00:30:00,300 --> 00:30:03,460 | |
| the extreme negative return values at risk is the | |
| 332 | |
| 00:30:03,460 --> 00:30:07,800 | |
| quantile of a distribution below which lies Q | |
| 333 | |
| 00:30:07,800 --> 00:30:10,620 | |
| percent of the possible values of that | |
| 334 | |
| 00:30:10,620 --> 00:30:12,820 | |
| distribution يعني ما هو احتمال أنه نحصل على | |
| 335 | |
| 00:30:13,590 --> 00:30:16,510 | |
| outliers في المنطقة هذه مالكو مش كتير في هذا | |
| 336 | |
| 00:30:16,510 --> 00:30:18,650 | |
| الكلام لإنهم عرفوا ليه هذا، هذا شوية صعب عادي | |
| 337 | |
| 00:30:18,650 --> 00:30:26,890 | |
| okay the five percent values at risk في ناس | |
| 338 | |
| 00:30:26,890 --> 00:30:29,830 | |
| بيعتبروا الـ values at risk هي الـ probability هي | |
| 339 | |
| 00:30:29,830 --> 00:30:35,250 | |
| ايش احتمالية is the probability to make loss هي | |
| 340 | |
| 00:30:35,250 --> 00:30:40,950 | |
| احتمال جداش احنا الاحتمال نخسر دائما الناس بتنظر للـ | |
| 341 | |
| 00:30:40,950 --> 00:30:45,010 | |
| .. للربح لكن احنا في الـ finance و الـ investment | |
| 342 | |
| 00:30:45,010 --> 00:30:48,930 | |
| برضه بنشوف ما هو احتمال أن احنا نخسر بتعطينا الـ | |
| 343 | |
| 00:30:48,930 --> 00:30:53,230 | |
| investment option commonly estimated in practice | |
| 344 | |
| 00:30:53,230 --> 00:30:57,650 | |
| هذه كتير مستخدمة في الحياة العملية اللي هو الـ | |
| 345 | |
| 00:30:57,650 --> 00:31:01,210 | |
| values at risk صحيح أن أنت .. you first time to .. | |
| 346 | |
| 00:31:01,210 --> 00:31:04,730 | |
| to hear about this to know about this but this is | |
| 347 | |
| 00:31:04,730 --> 00:31:08,290 | |
| commonly used in practice كتير ناس بيستخدموها في | |
| 348 | |
| 00:31:08,290 --> 00:31:11,550 | |
| الحياة العملية ممكن ناس يكونوا مش خرجين جامعات | |
| 349 | |
| 00:31:11,550 --> 00:31:15,410 | |
| يعني unfortunately you are in the university and | |
| 350 | |
| 00:31:15,410 --> 00:31:17,570 | |
| you are the first time to know about this but some | |
| 351 | |
| 00:31:17,570 --> 00:31:19,990 | |
| people is not in the university and they know | |
| 352 | |
| 00:31:19,990 --> 00:31:23,670 | |
| about this في ناس مش أصلا ما راحوش على الجامعة و | |
| 353 | |
| 00:31:23,670 --> 00:31:25,890 | |
| they know their values at risk and they asking | |
| 354 | |
| 00:31:25,890 --> 00:31:29,690 | |
| themselves ايش أسوأ ايش ممكن نسويه مرات يعني even | |
| 355 | |
| 00:31:29,690 --> 00:31:33,520 | |
| me sometimes what is the worst thing if you know | |
| 356 | |
| 00:31:33,520 --> 00:31:36,500 | |
| the worst things is fine يعني ايش أسوأ شيء ممكن | |
| 357 | |
| 00:31:36,500 --> 00:31:39,700 | |
| يصير and build your decision based on the worst | |
| 358 | |
| 00:31:39,700 --> 00:31:44,500 | |
| thing على أسوأ شيء دائما احنا we are looking to | |
| 359 | |
| 00:31:44,500 --> 00:31:48,780 | |
| the future as a flourish a future and we ignoring | |
| 360 | |
| 00:31:48,780 --> 00:31:51,860 | |
| the worst things يعني بنشوف المستقبل أحسن شيء و | |
| 361 | |
| 00:31:51,860 --> 00:31:55,620 | |
| أحلى شيء ف sometimes you have to look back and to | |
| 362 | |
| 00:31:55,620 --> 00:31:58,360 | |
| see if the worst thing happened what you can do | |
| 363 | |
| 00:31:58,360 --> 00:32:02,870 | |
| then إذا أسوأ شيء صار شو نعمل؟ from the investment | |
| 364 | |
| 00:32:02,870 --> 00:32:08,250 | |
| point of view من وجهة نظر المستثمرين، so if you | |
| 365 | |
| 00:32:08,250 --> 00:32:10,790 | |
| know the worst things so you can easily manage the | |
| 366 | |
| 00:32:10,790 --> 00:32:13,870 | |
| investment لكن if you don't know the worst things | |
| 367 | |
| 00:32:13,870 --> 00:32:17,630 | |
| so how you can know this so commonly estimated in | |
| 368 | |
| 00:32:17,630 --> 00:32:20,790 | |
| practice كتير مشهورة بالـ practice is the return at | |
| 369 | |
| 00:32:20,790 --> 00:32:25,490 | |
| the fifth percentile okay يعني الـ .. الـ .. الـ .. | |
| 370 | |
| 00:32:25,490 --> 00:32:28,930 | |
| بتعرفوا الـ .. أخدتوا الاشارات؟ أخدتوا الاشارات و | |
| 371 | |
| 00:32:28,930 --> 00:32:33,250 | |
| الربيع؟ الربيع الأول؟ الربيع الثاني؟ هذا هو الزمان | |
| 372 | |
| 00:32:33,250 --> 00:32:37,070 | |
| أخدته يعني هي بتيجي بعد ما أنا أقسم البيانات شوف | |
| 373 | |
| 00:32:37,070 --> 00:32:41,730 | |
| عندي بيانات في عندي observation اه بقسمها إلى | |
| 374 | |
| 00:32:41,730 --> 00:32:47,650 | |
| أشيريات percentiles فـ percentile أنه احنا بنقسم | |
| 375 | |
| 00:32:47,650 --> 00:32:51,330 | |
| البيانات من الـ .. البيانات .. البيانات بنقسمها من | |
| 376 | |
| 00:32:51,330 --> 00:32:56,550 | |
| أعلى إلى أقل وبنقسمها إلى .. إلى عشيريات أول عشرات | |
| 377 | |
| 00:32:56,550 --> 00:33:00,110 | |
| .. يعني مثلا جيبنا درجات الطلاب مثلا جيبنا درجات | |
| 378 | |
| 00:33:00,110 --> 00:33:05,850 | |
| الطلاب من تسعين لسفر مش لمية أو من مية لسفرأه من | |
| 379 | |
| 00:33:05,850 --> 00:33:11,170 | |
| مية لسفر بعدين جسمنا عملنا لهم ranking و روحنا | |
| 380 | |
| 00:33:11,170 --> 00:33:14,470 | |
| جيبنا أول عشر طلاب بعدين ثانية عشر طلاب ثالث عشر | |
| 381 | |
| 00:33:14,470 --> 00:33:18,930 | |
| طلاب رابعة و .. and so on هنجري أنه احنا حسب .. | |
| 382 | |
| 00:33:18,930 --> 00:33:22,270 | |
| هذا بيسموه percentile هذا إيش اسمه؟ في عندنا شغل | |
| 383 | |
| 00:33:22,270 --> 00:33:26,210 | |
| اسمه quartile و في quantile و في عندنا percentile | |
| 384 | |
| 00:33:26,210 --> 00:33:31,950 | |
| okay بقى percent اللي هو الربيع و العشر و المهم | |
| 385 | |
| 00:33:32,700 --> 00:33:36,460 | |
| ففي ال percentile أو خلينا نحكي بال .. إذا قسمناهم | |
| 386 | |
| 00:33:36,460 --> 00:33:40,420 | |
| لمئة مثلا أو لعشرة طبعا هم مستخدم ال quantile | |
| 387 | |
| 00:33:40,420 --> 00:33:43,880 | |
| ممكن نستخدم ال percentile نقسمهم لأول عشرة .. أول | |
| 388 | |
| 00:33:43,880 --> 00:33:46,320 | |
| عشرة .. أول عشرة .. هذا أول عشرة .. ثاني عشرة .. | |
| 389 | |
| 00:33:46,320 --> 00:33:50,440 | |
| ال values at risk هي بتكون بالعشرات اللي تحت يعني | |
| 390 | |
| 00:33:50,440 --> 00:33:53,340 | |
| بالنسبة للطلاب ال values عشان أنا أعرف where is | |
| 391 | |
| 00:33:53,340 --> 00:33:57,280 | |
| the best هيكونوا هم اللي تحت أصلا فعشان هيك they | |
| 392 | |
| 00:33:57,280 --> 00:34:02,480 | |
| take the last quantiles or last quantiles or last | |
| 393 | |
| 00:34:02,480 --> 00:34:06,480 | |
| percentiles okay when returns are sorted from high | |
| 394 | |
| 00:34:06,480 --> 00:34:10,800 | |
| to low جربوها يعني لو بتاخدوا معايا الحاسوب | |
| 395 | |
| 00:34:10,800 --> 00:34:14,520 | |
| التحليل معايا بيواجهيكوا how .. بيصنفوا ناخذ آخر | |
| 396 | |
| 00:34:14,520 --> 00:34:18,240 | |
| ناس سهل نعرف أن مين أسوأ ناس موجودين لا سمح الله | |
| 397 | |
| 00:34:18,240 --> 00:34:21,920 | |
| يعني okay | |
| 398 | |
| 00:34:21,920 --> 00:34:27,470 | |
| خليني بس ع السريع لإن أنا هخلصكم اليوم الشغلات | |
| 399 | |
| 00:34:27,470 --> 00:34:32,810 | |
| المصيبة ال dial بس ال expected shortfall is expected | |
| 400 | |
| 00:34:32,810 --> 00:34:35,910 | |
| shortfall is also called conditional tail | |
| 401 | |
| 00:34:35,910 --> 00:34:40,110 | |
| expectation المشكلة | |
| 402 | |
| 00:34:40,110 --> 00:34:44,530 | |
| في ال values at risk is comparing these values | |
| 403 | |
| 00:34:44,530 --> 00:34:49,350 | |
| with these values لما احنا we compare this نقرر | |
| 404 | |
| 00:34:49,350 --> 00:34:54,140 | |
| الناس الشاطرين بالناس الرسوبيين يعني we compare the | |
| 405 | |
| 00:34:54,140 --> 00:34:57,300 | |
| positive values with the negative values هذا باسمه | |
| 406 | |
| 00:34:57,300 --> 00:34:59,820 | |
| بال values at risk so values at risk is a | |
| 407 | |
| 00:34:59,820 --> 00:35:05,680 | |
| conservative measure يعني محافظ شوية but in | |
| 408 | |
| 00:35:05,680 --> 00:35:08,900 | |
| shortfalls is only focusing on the negative values | |
| 409 | |
| 00:35:08,900 --> 00:35:12,340 | |
| بس بتركز على ال negative values to what extent | |
| 410 | |
| 00:35:12,340 --> 00:35:18,980 | |
| these values are negative؟ قد إيش هم سيئين أصلا okay | |
| 411 | |
| 00:35:18,980 --> 00:35:23,750 | |
| we know we have negative values يعني احنا بنعرف أن | |
| 412 | |
| 00:35:23,750 --> 00:35:28,270 | |
| في عندنا negative returns but to what extent these | |
| 413 | |
| 00:35:28,270 --> 00:35:31,990 | |
| negative returns influence on our portfolio or in | |
| 414 | |
| 00:35:31,990 --> 00:35:37,890 | |
| our decision يعني شفنا مثلا في عندنا طلاب راسبين | |
| 415 | |
| 00:35:37,890 --> 00:35:42,870 | |
| تحت لكن قد إيش هدول مهمين بالنسبة لنا إذا جينا أن | |
| 416 | |
| 00:35:42,870 --> 00:35:48,720 | |
| والله هذا العدد مقارنة مع ال big people أنه very | |
| 417 | |
| 00:35:48,720 --> 00:35:52,700 | |
| very small we can ignore them but if it is if | |
| 418 | |
| 00:35:52,700 --> 00:35:57,080 | |
| there is a problem if we observe if we observe the | |
| 419 | |
| 00:35:57,080 --> 00:36:00,380 | |
| negative return like we have a number of people so | |
| 420 | |
| 00:36:00,380 --> 00:36:06,240 | |
| we focus on this ف shortfalls is not comparing the | |
| 421 | |
| 00:36:06,240 --> 00:36:09,040 | |
| good people with the good results with the bad | |
| 422 | |
| 00:36:09,040 --> 00:36:12,980 | |
| results just only focusing on the bad results بس | |
| 423 | |
| 00:36:12,980 --> 00:36:16,880 | |
| بتطلع ال negative returns okay and see why why | |
| 424 | |
| 00:36:16,880 --> 00:36:21,960 | |
| these negative returns So values at risk take the | |
| 425 | |
| 00:36:21,960 --> 00:36:26,640 | |
| highest return from the worst cases Okay بتاخد | |
| 426 | |
| 00:36:26,640 --> 00:36:32,840 | |
| أعلى عائد من أسوأ حالات Expected shortfalls اللي | |
| 427 | |
| 00:36:32,840 --> 00:36:37,420 | |
| EC take an average return of the worst cases هتيجي | |
| 428 | |
| 00:36:37,420 --> 00:36:43,170 | |
| كأنه إيش هنسوي طلعوا هنا شوفوا الحالة هنا في ال | |
| 429 | |
| 00:36:43,170 --> 00:36:46,970 | |
| values at risk ال average moved to the down، | |
| 430 | |
| 00:36:46,970 --> 00:36:52,290 | |
| مظبوط؟ وشوفنا إيش ال .. إيش هذا تأثرت بهدول، لكن | |
| 431 | |
| 00:36:52,290 --> 00:36:56,250 | |
| بال .. ال expected shortfalls هذا .. we ignore | |
| 432 | |
| 00:36:56,250 --> 00:37:00,550 | |
| this and we calculate the average of this، بنشوف | |
| 433 | |
| 00:37:00,550 --> 00:37:04,870 | |
| ال average تلقى هدول، قد إيش هو is negative، قد إيش هو | |
| 434 | |
| 00:37:04,870 --> 00:37:10,250 | |
| سيء هذا الفرق بين ال values at risk و بين expected | |
| 435 | |
| 00:37:10,250 --> 00:37:13,790 | |
| shortfalls expected shortfalls تأخذ عدد عادل عادل | |
| 436 | |
| 00:37:13,790 --> 00:37:19,750 | |
| عادل عادل عادل عادل عادل عادل عادل عادل عادل عادل | |
| 437 | |
| 00:37:19,750 --> 00:37:21,010 | |
| عادل عادل عادل عادل عادل عادل عادل عادل عادل عادل | |
| 438 | |
| 00:37:21,010 --> 00:37:24,190 | |
| عادل عادل عادل عادل عادل عادل عادل عادل عادل عادل | |
| 439 | |
| 00:37:24,190 --> 00:37:25,530 | |
| عادل عادل عادل عادل عادل عادل عادل عادل عادل عادل | |
| 440 | |
| 00:37:25,530 --> 00:37:25,550 | |
| عادل عادل عادل عادل عادل عادل عادل عادل عادل عادل | |
| 441 | |
| 00:37:25,550 --> 00:37:31,770 | |
| عادل عادل عادل عادل | |
| 442 | |
| 00:37:31,770 --> 00:37:36,500 | |
| على اللي هو ال lower partial standard deviation and | |
| 443 | |
| 00:37:36,500 --> 00:37:42,740 | |
| the Sortino ratio احنا حكينا احنا إذا كان عندنا | |
| 444 | |
| 00:37:42,740 --> 00:37:47,540 | |
| non-normal distribution so the average is no | |
| 445 | |
| 00:37:47,540 --> 00:37:52,940 | |
| longer is a good measure to return or to the risk | |
| 446 | |
| 00:37:52,940 --> 00:37:57,140 | |
| حكينا إذا كان البيانات مش normal distribution | |
| 447 | |
| 00:37:57,940 --> 00:38:00,640 | |
| توزيعها مش طبيعي معناه الكلام ال average is | |
| 448 | |
| 00:38:00,640 --> 00:38:04,020 | |
| misleading the standard deviation is misleading so | |
| 449 | |
| 00:38:04,020 --> 00:38:09,520 | |
| what we can do then إيش ممكن نسوي instead of using | |
| 450 | |
| 00:38:09,520 --> 00:38:12,680 | |
| average بدل ما احنا نستخدم ال average we can | |
| 451 | |
| 00:38:12,680 --> 00:38:19,180 | |
| replace the average by the risk free يعني شوفوا | |
| 452 | |
| 00:38:19,180 --> 00:38:23,780 | |
| شايفين البيانات هذه البيانات | |
| 453 | |
| 00:38:23,780 --> 00:38:29,100 | |
| هذه هذه البيانات اللي فيها outliers وروحنا جيبنا ال | |
| 454 | |
| 00:38:29,100 --> 00:38:32,740 | |
| average طالع حامل حسب ال lower partial standard | |
| 455 | |
| 00:38:32,740 --> 00:38:38,960 | |
| deviation هذا ال average is misleading so the | |
| 456 | |
| 00:38:38,960 --> 00:38:42,600 | |
| statisticians or the statistical people and the | |
| 457 | |
| 00:38:42,600 --> 00:38:45,340 | |
| financial people think the average is misleading | |
| 458 | |
| 00:38:45,340 --> 00:38:51,580 | |
| so what we can do then is remove the average بدل ال | |
| 459 | |
| 00:38:51,580 --> 00:38:54,960 | |
| average so replace the average with the risk | |
| 460 | |
| 00:38:54,960 --> 00:38:55,240 | |
| -free | |
| 461 | |
| 00:38:59,820 --> 00:39:03,120 | |
| بنجيب ال average و بنحط ال risk free بلغة okay | |
| 462 | |
| 00:39:03,120 --> 00:39:09,140 | |
| because the risk free is a parameter or a good | |
| 463 | |
| 00:39:09,140 --> 00:39:12,620 | |
| indicator for all of the investments فاحنا بنشوف | |
| 464 | |
| 00:39:12,620 --> 00:39:17,400 | |
| ال risk free وين بيجي أه بيجي أها خلاص فلما بنحسب | |
| 465 | |
| 00:39:17,400 --> 00:39:20,440 | |
| ال sigma بنقول هذه ال observation ناقصها يعني لما | |
| 466 | |
| 00:39:20,440 --> 00:39:24,900 | |
| احنا نحسب ال sigma كنا نحسبها R minus R bar okay | |
| 467 | |
| 00:39:24,900 --> 00:39:28,500 | |
| تربيع divided by N صح؟ | |
| 468 | |
| 00:39:31,100 --> 00:39:34,840 | |
| في حالة ما نستخدم ال lower partial هنشيل ال R bar | |
| 469 | |
| 00:39:34,840 --> 00:39:44,880 | |
| ونحط بدلها إيه؟ ال R ال RR بس فهدول بيعتقدوا أنه | |
| 470 | |
| 00:39:44,880 --> 00:39:49,260 | |
| هيك أدق بيصير so issues need to consider negative | |
| 471 | |
| 00:39:49,260 --> 00:39:52,620 | |
| deviations separately طبعا هاي negative retained | |
| 472 | |
| 00:39:52,620 --> 00:39:55,180 | |
| separately بتتوافق مع ال expected shortfalls، | |
| 473 | |
| 00:39:55,180 --> 00:39:58,840 | |
| مظبوط؟ ها دي بتتوافق مع ال expected shortfalls | |
| 474 | |
| 00:39:58,840 --> 00:40:04,420 | |
| اللي فاتت yes لأن احنا just focus on the expected | |
| 475 | |
| 00:40:04,420 --> 00:40:10,620 | |
| shortfalls هذي بس ركزوا على ال negative values | |
| 476 | |
| 00:40:12,010 --> 00:40:16,050 | |
| الإضافة الجديدة اللي عملوها يعني هي ال ال values | |
| 477 | |
| 00:40:16,050 --> 00:40:20,030 | |
| at risk انتبهوا ال values at risk زي صار فيها | |
| 478 | |
| 00:40:20,030 --> 00:40:23,430 | |
| developments بعدين اجوا ناس قالوا لأ ال values at | |
| 479 | |
| 00:40:23,430 --> 00:40:26,590 | |
| risk هي conservatives خلينا نطور واحدة ثانية | |
| 480 | |
| 00:40:26,590 --> 00:40:29,730 | |
| سموها ال expected shortfalls قالوا لأ ال expected | |
| 481 | |
| 00:40:29,730 --> 00:40:33,750 | |
| shortfalls بتاخد عند اعتبار ال average صحيح it's | |
| 482 | |
| 00:40:33,750 --> 00:40:37,800 | |
| it's looking at the negative returns بقى is looking | |
| 483 | |
| 00:40:37,800 --> 00:40:40,460 | |
| to the average and the average is misleading so | |
| 484 | |
| 00:40:40,460 --> 00:40:45,780 | |
| what we can do then replace the average by the by | |
| 485 | |
| 00:40:45,780 --> 00:40:49,000 | |
| the risk free فهم اعتمدوا نقطتين need to consider | |
| 486 | |
| 00:40:49,000 --> 00:40:51,220 | |
| the negative deviation separately negative returns | |
| 487 | |
| 00:40:51,220 --> 00:40:53,900 | |
| and need to consider deviation from return from | |
| 488 | |
| 00:40:53,900 --> 00:40:57,620 | |
| the risk free rates من ال risk free not from the | |
| 489 | |
| 00:40:57,620 --> 00:40:59,280 | |
| not from the average | |
| 490 | |
| 00:41:07,530 --> 00:41:11,730 | |
| هذه الأولى look like they expected shortfalls | |
| 491 | |
| 00:41:11,730 --> 00:41:15,930 | |
| خلصنا إيش عملوا تطوير عليها؟ عملوا تطوير جديد | |
| 492 | |
| 00:41:15,930 --> 00:41:21,570 | |
| عليها بدل ما يحسبوا ال minus minus the average | |
| 493 | |
| 00:41:21,570 --> 00:41:29,850 | |
| استخدموا ال risk free بس ال LBSD similar to usual | |
| 494 | |
| 00:41:29,850 --> 00:41:32,390 | |
| standard deviation هي شبه ال standard deviation | |
| 495 | |
| 00:41:32,390 --> 00:41:37,700 | |
| لكن إيش الفرق منها؟ بس ال risk free طيب فاكرين | |
| 496 | |
| 00:41:37,700 --> 00:41:43,760 | |
| share ratio share ratio اللي هو ال share ratio | |
| 497 | |
| 00:41:43,760 --> 00:41:46,700 | |
| اللي حكيناكوا فيها ال excess return او risk | |
| 498 | |
| 00:41:46,700 --> 00:41:51,700 | |
| premium divided by the standard deviation، مظبوط؟ | |
| 499 | |
| 00:41:51,700 --> 00:41:58,220 | |
| طيب بما أن ال data is not normally distributed طب | |
| 500 | |
| 00:41:58,220 --> 00:42:02,520 | |
| بعد كلام ال share ratio is not working هذا الكلام | |
| 501 | |
| 00:42:02,520 --> 00:42:05,800 | |
| حكيناه قويا قبل تلت أربعتين قولنا إذا البيانات | |
| 502 | |
| 00:42:05,800 --> 00:42:10,840 | |
| توزيع غير طبيعي معنى الكلام إن ال-sharp ratio مش | |
| 503 | |
| 00:42:10,840 --> 00:42:16,080 | |
| صح please focus on this what I said just three | |
| 504 | |
| 00:42:16,080 --> 00:42:20,000 | |
| meetings I said if our data is not normally | |
| 505 | |
| 00:42:20,000 --> 00:42:25,080 | |
| distributed we cannot.. we no longer use the | |
| 506 | |
| 00:42:25,080 --> 00:42:29,290 | |
| sharp ratio طب what is the solution if our data is | |
| 507 | |
| 00:42:29,290 --> 00:42:33,970 | |
| not normally distributed we can just replace the | |
| 508 | |
| 00:42:33,970 --> 00:42:35,730 | |
| standard deviation because the standard deviation | |
| 509 | |
| 00:42:35,730 --> 00:42:38,830 | |
| is misleading in Sharpe ratio and replace this | |
| 510 | |
| 00:42:38,830 --> 00:42:44,870 | |
| with the downside risk yes and when we replace it with | |
| 511 | |
| 00:42:44,870 --> 00:42:50,170 | |
| downside risk it's become Sortino ratio or Sortino | |
| 512 | |
| 00:42:55,820 --> 00:42:59,780 | |
| So Sortino Ratio is similar to the Sharpe Ratio | |
| 513 | |
| 00:42:59,780 --> 00:43:03,300 | |
| بقى in .. in .. in the Sortino Ratio just we | |
| 514 | |
| 00:43:03,300 --> 00:43:06,160 | |
| replace the standard deviation with the downside risk | |
| 515 | |
| 00:43:06,160 --> 00:43:12,460 | |
| اه حلو والله okay | |
| 516 | |
| 00:43:12,460 --> 00:43:18,600 | |
| تمام | |
| 517 | |
| 00:43:18,600 --> 00:43:19,500 | |
| okay | |